A method for measuring water and underwater terrain based on the cooperation of unmanned boats and drones

Abstract

The present invention relates to the field of topographic surveying technology, and in particular to a method for measuring topography above and below the water based on the collaboration of an unmanned vessel and an unmanned aerial vehicle. The method comprises the following steps: obtaining platform parameter data of the unmanned vessel and the unmanned aerial vehicle; performing heterogeneous grouping based on the remaining power, communication signal strength, endurance, and measurement accuracy according to the platform parameter data, and determining a collaborative formation plan according to a preset water area measurement task sequence to obtain collaborative parameter data; obtaining measurement area environmental data; performing regional division modeling based on the water area terrain characteristics and environmental complexity according to the measurement area environmental data, and applying the collaborative parameter data to the regional model to obtain a measurement area topology model. The present invention uses the unmanned vessel as a mobile charging platform, combines real-time power monitoring with a docking charging mechanism, and optimizes the energy replenishment strategy to achieve endurance guarantee for the unmanned aerial vehicle, thereby ensuring the continuity and stability of the mission.

Description

Technical Field

[0001] The present invention relates to the field of topographic surveying technology, and in particular to a method for measuring topography above and below the water based on the collaboration of an unmanned vessel and a drone. Background Art

[0002] An unmanned vessel, also known as an autonomous surface vehicle (ASV), is a vessel capable of navigation, perception, and obstacle avoidance without direct human operation, using its own sensors, controllers, and propulsion systems. An unmanned vessel is a surface robot that primarily achieves autonomous navigation through intelligent control. It is a complex system involving multiple specialized fields, including ship design, communications, environmental perception, data fusion, motion control, human-computer interaction, and artificial intelligence. An unmanned aerial vehicle (UAV) is an unmanned aircraft controlled by a radio remote control device or a self-contained program control device. The water and underwater topographic surveying method based on the collaboration of unmanned vessels and drones is an advanced surveying and mapping method that integrates aerial and surface surveying technologies.

[0003] However, current methods for collaboratively surveying surface and underwater terrain using unmanned vessels and drones often face the following challenges: Task allocation between unmanned vessels and drones requires consideration of multiple constraints (such as remaining battery power, communication signal strength, and terrain complexity), making dynamic optimal path planning difficult for traditional algorithms. In complex waters, drones may be unable to complete their missions due to battery depletion, and unmanned vessels move slowly, posing a key challenge in properly allocating survey areas to avoid duplication and omissions. Unmanned vessels and drones have limited endurance in complex environments, and inadequate energy management strategies can cause devices to run out of power before completing their missions. Drones in high-power mode may only be able to fly for 30 minutes, making it difficult to cover the target area and return to a charging station in vast waters. Unmanned vessels, while capable of long flight times, also move slowly. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a method for measuring surface and underwater terrain based on the collaboration of an unmanned ship and a drone to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above-mentioned purpose, a method for measuring surface and underwater terrain based on the collaboration of an unmanned vessel and a drone comprises the following steps:

[0006] Step S1: Obtain platform parameter data of the unmanned vessel and the drone; perform heterogeneous grouping based on remaining power, communication signal strength, endurance, and measurement accuracy according to the platform parameter data, and determine a collaborative formation plan according to a preset water measurement task sequence to obtain collaborative parameter data;

[0007] Step S2: Obtaining measurement area environmental data; performing regional division modeling based on the water area terrain characteristics and environmental complexity according to the measurement area environmental data, and applying the collaborative parameter data to the regional model to obtain a measurement area topology model; optimizing the sub-region task allocation of the measurement area topology model to obtain a collaborative measurement task allocation plan;

[0008] Step S3: Construct a fuzzy logic path planning model according to the collaborative measurement task allocation plan, and dynamically optimize the path based on the platform status data obtained through real-time monitoring to obtain a collaborative measurement path planning plan with minimum repeated coverage and omissions;

[0009] Step S4: Using the unmanned vessel as a mobile charging platform, a fast docking charging or battery swap mechanism is triggered based on the remaining battery power of the UAV in the platform status data and the preset battery power threshold. The timing and location of energy replenishment are optimized according to the collaborative measurement path planning scheme to obtain dynamic energy replenishment strategy data;

[0010] Step S5: The collaborative tasks of the unmanned boat and the UAV are scheduled in real time according to the dynamic energy replenishment strategy data, the collaborative measurement path planning scheme, and the collaborative measurement task allocation scheme, thereby realizing the terrain measurement above and below the water.

[0011] This invention groups and manages heterogeneous devices by acquiring platform parameters such as remaining battery power, communication signal strength, endurance, and measurement accuracy from unmanned vessels and drones. This heterogeneous grouping allows different devices to leverage their respective strengths during task execution, with unmanned vessels providing stable platform support and drones providing flexible aerial measurements. A formation plan is determined based on a preset measurement task sequence and device capabilities, avoiding wasted device capacity or poor task adaptation. The resulting collaborative parameter data provides a unified formation benchmark for subsequent steps. Combined with measurement area environmental data, regional modeling based on water area topography and environmental complexity accurately characterizes the measurement area, ensuring that the measurement plan adapts to complex environments. Regional modeling reduces duplicate measurements and omissions, improving measurement accuracy and coverage. The collaborative parameter data is applied to the regional model to generate a topological model of the measurement area, and sub-regional task allocation is optimized. The optimized task allocation plan effectively avoids device task conflicts, rationally allocates resources, and improves collaborative efficiency. A fuzzy logic path planning model dynamically adjusts the measurement path based on task objectives and real-time device status data, addressing the poor adaptability of traditional path planning methods to environmental changes and ensuring more flexible and efficient path planning. An optimized path planning scheme significantly reduces overlaps and omissions between unmanned vessels and drones during surveys, improving measurement efficiency and data integrity. A dynamic optimization mechanism ensures rapid route adjustments even in the event of device status changes (such as low battery or weak signal) during mission execution, ensuring successful mission completion. The unmanned vessel serves as a mobile charging platform, monitoring the drone's remaining battery life in real time and intelligently triggering rapid docking and charging or battery replacement, effectively addressing the drone's limited endurance. The introduction of a dynamic recharge strategy reduces the risk of mission interruptions and enhances the continuity of collaborative measurement. Recharge timing and location are optimized based on the collaborative measurement path planning scheme, avoiding resource waste caused by frequent or excessive recharges while improving mission efficiency and reliability. The integrated dynamic energy recharge strategy, path planning, and task allocation scheme enable real-time scheduling and precise coordination between the unmanned vessel and drone, enhancing the overall system execution capability. The coordinated surface and underwater surveying tasks, with the unmanned vessel supporting underwater surveys from the surface and the drone providing aerial assistance, form a complete survey chain. This systematic collaborative operation significantly improves the accuracy, speed, and coverage of topographic surveys. Through real-time scheduling, we can respond to emergencies or environmental changes (such as sudden bad weather, equipment failure, etc.), ensuring that measurement tasks can be adjusted in time and completed smoothly. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0013] Figure 1A schematic flow chart of the steps of the above-water and underwater topographic survey method based on the collaboration of an unmanned vessel and a drone according to the present invention;

[0014] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0015] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0016] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described 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 those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0018] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for measuring water and underwater terrain based on the collaboration of an unmanned ship and a drone, the method comprising the following steps:

[0020] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for measuring surface and underwater topography based on the collaboration of an unmanned vessel and a drone according to the present invention. In this example, the method for measuring surface and underwater topography based on the collaboration of an unmanned vessel and a drone includes the following steps:

[0021] Step S1: Obtain platform parameter data of the unmanned vessel and the drone; perform heterogeneous grouping based on remaining power, communication signal strength, endurance, and measurement accuracy according to the platform parameter data, and determine a collaborative formation plan according to a preset water measurement task sequence to obtain collaborative parameter data;

[0022] In a complex water survey mission, this embodiment of the present invention acquires key platform parameter data for unmanned vessels and drones, including the drone's maximum flight time (30 minutes), signal coverage radius (5 kilometers), the drone's maximum endurance (12 hours), and communication stability parameters. Heterogeneous grouping is performed based on the drone's remaining battery life (80% to 20%) and communication signal strength (RSSI value -50dBm), as well as the drone's endurance and the accuracy of the drone's high-precision laser ranging sensor (1cm). High-precision drones are prioritized for complex survey areas. Based on a predefined sequence of survey tasks, such as depth measurement and terrain modeling, a collaborative formation scheme is determined, with the unmanned vessel leading the water surface survey and providing relay signal support for the drone. The collaborative parameter data is then output for subsequent area modeling and task allocation.

[0023] Step S2: Obtaining measurement area environmental data; performing regional division modeling based on the water area terrain characteristics and environmental complexity according to the measurement area environmental data, and applying the collaborative parameter data to the regional model to obtain a measurement area topology model; optimizing the sub-region task allocation of the measurement area topology model to obtain a collaborative measurement task allocation plan;

[0024] This embodiment of the present invention uses high-resolution imagery collected by drones and sonar data from unmanned vessels to determine the target water environment's topographical features (such as shallows, deep trenches, and reefs) and environmental complexity (e.g., wind speeds of 10 m / s and currents of 1.2 m / s). Based on this data, a clustering algorithm is used to partition the water area into regions, creating a regional model encompassing deepwater areas, complex terrain areas, and shallow areas. Collaborative parameter data is then applied to the model. An optimization algorithm is then used to allocate tasks to subregions within the topological model of the measurement area. For example, shallow areas are assigned to drones with lower endurance, while complex terrain areas are assigned to a combination of unmanned vessels and drones with higher signal transmission capabilities. Ultimately, the optimal collaborative measurement task allocation scheme is achieved.

[0025] Step S3: Construct a fuzzy logic path planning model according to the collaborative measurement task allocation plan, and dynamically optimize the path based on the platform status data obtained through real-time monitoring to obtain a collaborative measurement path planning plan with minimum repeated coverage and omissions;

[0026] This embodiment of the present invention utilizes a collaborative measurement task allocation scheme to construct a fuzzy logic path planning model. Sub-region tasks and path plans are input into the model, and the path is dynamically optimized based on the drone's real-time status data (e.g., 15% remaining battery life, 5° heading deviation). By combining the A* algorithm with real-time monitoring data, the optimal flight path for the drone and the optimal navigation path for the unmanned vessel are generated, ensuring a path coverage rate of over 98% and a duplication rate of under 2%. For example, the drone prioritizes surveying the center of a complex area and uses a polygonal coverage strategy to cover the outer boundary areas. The resulting collaborative measurement path plan minimizes duplication and omissions.

[0027] Step S4: Using the unmanned vessel as a mobile charging platform, a fast docking charging or battery swap mechanism is triggered based on the remaining battery power of the UAV in the platform status data and the preset battery power threshold. The timing and location of energy replenishment are optimized according to the collaborative measurement path planning scheme to obtain dynamic energy replenishment strategy data;

[0028] In this embodiment of the present invention, when the drone's battery level falls below a set threshold (e.g., 30%), the unmanned vessel activates its mobile charging function. The vessel then calculates the optimal charging docking point based on the drone's real-time location and flight trajectory as captured by the platform's status data. During a specific mission, if the drone is expected to run out of power within 5 minutes and is 2 kilometers from the nearest unmanned vessel, the rapid charging docking mechanism is triggered, using a magnetic interface to complete a battery swap or charge to 50% within 3 minutes of docking. Simultaneously, the unmanned vessel's charging location and travel time are adjusted based on the collaborative measurement path planning scheme, optimizing the energy recharge strategy and ensuring the drone continues operating without losing track of the measurement mission.

[0029] Step S5: The collaborative tasks of the unmanned boat and the UAV are scheduled in real time according to the dynamic energy replenishment strategy data, the collaborative measurement path planning scheme, and the collaborative measurement task allocation scheme, thereby realizing the terrain measurement above and below the water.

[0030] The embodiment of the present invention utilizes a task scheduling algorithm to schedule tasks for unmanned vessels and drones in real time, based on dynamic energy replenishment strategy data, a collaborative measurement path planning scheme, and a collaborative measurement task allocation scheme. For example, in a shallow area, because the drone needs to return to recharge after completing its measurement mission, and the unmanned vessel's endurance allows it to continue working, the system assigns the unmanned vessel to take over the remaining measurement tasks in the shallow area and simultaneously supports the drone in completing the measurement of complex areas. By coordinating the real-time location, mission status, and environmental data of the drone and unmanned vessel, efficient and accurate surface and underwater topography measurements within the measurement area are ultimately achieved, with measurement accuracy exceeding 95% and time efficiency improved by 30%.

[0031] Preferably, step S1 includes the following steps:

[0032] Step S11: collecting remaining power, communication signal strength, current endurance, and measurement equipment status parameters of the unmanned ship's hardware equipment to obtain unmanned ship platform parameter data;

[0033] Step S12: Obtaining the drone platform parameter data, including the drone's remaining flight power, flight endurance, current communication signal quality, and sensor working status information;

[0034] Step S13: uploading the unmanned ship platform parameter data and the unmanned aerial vehicle platform parameter data to the measurement and control center through the data transmission interface, thereby obtaining the platform parameter data;

[0035] Step S14: performing data denoising and time synchronization processing on the platform parameter data to obtain standardized platform parameter data;

[0036] Step S15: performing heterogeneous grouping based on remaining power, communication signal strength, endurance, and measurement accuracy according to the standardized platform parameter data, thereby obtaining heterogeneous grouping solution data;

[0037] Step S16: Generate a collaborative formation plan according to the preset water area measurement task sequence and the heterogeneous grouping plan data, thereby obtaining collaborative parameter data.

[0038] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0039] Step S11: collecting remaining power, communication signal strength, current endurance, and measurement equipment status parameters of the unmanned ship's hardware equipment to obtain unmanned ship platform parameter data;

[0040] The unmanned vessel in this embodiment uses its embedded control module and sensors to collect hardware status parameters, such as remaining battery power of 70%, communication signal strength of -80dBm, current endurance of 6 hours, and "normal operation" measurement device status. This data is collected in real time via the RS-485 interface and stored by the data logging module, with the acquisition frequency set to once per minute to ensure real-time performance. To verify data accuracy, a system self-test program cross-checks the data collected by the battery management system (BMS) and the signal strength receiving module, ultimately generating a data file containing the unmanned vessel platform parameters, including battery power, signal strength, endurance, and measurement device status.

[0041] Step S12: Obtaining the drone platform parameter data, including the drone's remaining flight power, flight endurance, current communication signal quality, and sensor working status information;

[0042] In this embodiment of the present invention, the drone uses a sensor module integrated into the flight control system to collect platform parameter data, such as remaining battery power of 50%, endurance of 25 minutes, current communication signal quality of -70dBm, and optical sensor status of "normal operation." During the collection process, the I2C interface on the drone's flight control board is used to obtain battery voltage and current data to calculate the remaining battery power. The communication module measures signal quality in real time, and the sensor status monitoring interface is used to monitor the device's operating status. This data is stored in JSON format and recorded in the flight control log file, with a sampling interval of 30 seconds.

[0043] Step S13: uploading the unmanned ship platform parameter data and the unmanned aerial vehicle platform parameter data to the measurement and control center through the data transmission interface, thereby obtaining the platform parameter data;

[0044] In this embodiment of the present invention, the unmanned vessel and drone upload platform parameter data to a measurement and control center via a data transmission interface. For example, a LoRa wireless communication module is used for data transmission in the 2.4 GHz frequency band, with a communication range of less than 5 kilometers. The unmanned vessel and drone send a data packet to the measurement and control center every minute, containing platform status, device status, and location information. The measurement and control center receives the data using a multi-threaded listening program on the receiving station's server, parses the uploaded JSON file, merges the data streams from the unmanned vessel and drone, and ultimately generates a complete multi-platform parameter data file.

[0045] Step S14: performing data denoising and time synchronization processing on the platform parameter data to obtain standardized platform parameter data;

[0046] This embodiment of the present invention processes platform parameter data by first using a wavelet denoising algorithm to filter out noise generated during transmission. This method, for example, eliminates abnormal fluctuations in communication signal strength (e.g., interference data that briefly drops to -110 dBm). The time synchronization module then aligns the data recorded by the unmanned vessel and drone using the GPS timestamp as a reference. A tolerance threshold of 0.5 seconds is used to ensure data temporal consistency. After processing, time-series platform parameter data conforming to a standard format is generated for subsequent grouping and formation calculations.

[0047] Step S15: performing heterogeneous grouping based on remaining power, communication signal strength, endurance, and measurement accuracy according to the standardized platform parameter data, thereby obtaining heterogeneous grouping solution data;

[0048] This embodiment of the present invention utilizes standardized platform parameter data and implements heterogeneous grouping based on a multi-attribute decision-making approach. For example, remaining battery life, communication signal strength, endurance, and measurement accuracy are assigned weights (0.4, 0.3, 0.2, and 0.1, respectively), and a comprehensive score is calculated through weighted summation. Unmanned vessels and drones are then sorted by their comprehensive scores and grouped into, for example, three groups: high battery and long endurance, medium battery, and low battery. This ensures the highest efficiency in collaborative measurement across platforms with different characteristics, ultimately generating data for a heterogeneous grouping solution.

[0049] Step S16: Generate a collaborative formation plan according to the preset water area measurement task sequence and the heterogeneous grouping plan data, thereby obtaining collaborative parameter data.

[0050] This embodiment of the present invention uses a genetic algorithm to generate a collaborative formation scheme based on a preset sequence of water measurement tasks, for example, where the target tasks include areas A, B, and C, with a priority of A>B>C, combined with heterogeneous grouping scheme data. The calculation objective functions are maximizing measurement coverage and optimizing signal coverage, with constraints based on the endurance and real-time measurement accuracy of the unmanned vessel and drone. In a real-world scenario, the unmanned vessel is assigned to deepwater area A, and the drone is assigned to shallowwater area B and mudflat area C. The generated collaborative parameter data includes detailed planning for task allocation, formation, and communication nodes.

[0051] The present invention collects the remaining battery power, communication signal strength, endurance, and measurement equipment status of the unmanned vessel, enabling a comprehensive understanding of its current operating status and mission execution capabilities. This detailed data collection provides accurate foundational data for subsequent mission planning, helping to improve the reliability of the overall system decision-making. Real-time data collection of remaining battery power and equipment status allows for rapid identification of potential failures or mission interruption risks, allowing them to be mitigated during the planning phase. By collecting the remaining battery power, endurance, communication signal quality, and sensor operating status of the drone, it is possible to accurately determine the drone's available time, flight capabilities, and environmental adaptability. This comprehensive data collection provides more targeted data support for collaborative measurement task allocation and formation optimization. Real-time acquisition of communication signal quality allows for the assessment of the drone's operational capabilities in complex communication environments, enabling proactive optimization of its execution plan and minimizing the impact of signal interruptions or delays. Data parameters for the unmanned vessel and drone are uploaded to the measurement and control center via a data transmission interface, creating a centralized data processing model that facilitates unified management and real-time analysis. Centralized processing avoids data inaccuracies or errors that can result from single-node decision-making, improving the overall collaborative efficiency of the system. Centralized uploading allows the measurement and control center to receive real-time data on platform status changes, providing efficient support for subsequent mission planning and adjustments. Data denoising eliminates interference and noise from the raw data, ensuring more accurate and reliable data for subsequent mission planning. Time synchronization ensures that the status data of unmanned vessels and drones is consistent and based on the same time base, making subsequent heterogeneous grouping and task formation more accurate and coordinated. Standardization resolves the issue of inconsistent data formats across platforms and reduces decision-making errors caused by data discrepancies. Heterogeneous grouping, based on remaining battery life, communication signal strength, endurance, and measurement accuracy, enables the selection of the most appropriate equipment combination for different tasks, fully leveraging the equipment's performance advantages. Heterogeneous grouping ensures equipment compatibility and execution efficiency for measurement tasks, improving mission success rates. Grouping optimization reduces capability conflicts or mismatches between devices, enhancing the smoothness and efficiency of collaborative execution. Based on the water survey task sequence and the heterogeneous grouping scheme, a collaborative formation plan is generated, ensuring more scientific and reasonable task allocation, avoiding resource waste and duplication of effort. Collaborative parameter data provides a clear collaborative framework for subsequent path planning and task execution. The formation plan, combining equipment performance and task requirements, can flexibly adapt to multi-task execution in complex measurement environments, ensuring comprehensive and accurate measurements. The formation plan fully considers the coordination and redundant design between equipment in mission planning, improves the system's anti-interference ability and robustness, and ensures the smooth completion of the mission.

[0052] Preferably, step S15 includes the following steps:

[0053] Step S151: setting priority weights of parameter indicators based on standardized platform parameter data, wherein remaining power has the highest weight, followed by communication signal strength, endurance, and measurement accuracy, thereby forming group weight configuration parameters;

[0054] The embodiment of the present invention sets weight indicators based on standardized platform parameter data. First, the weights of the four parameters of remaining power, communication signal strength, endurance and measurement accuracy are set according to the task requirements. For example, the weight of remaining power is 0.4, the weight of communication signal strength is 0.3, the weight of endurance is 0.2, and the weight of measurement accuracy is 0.1. On this basis, combined with the preset task requirement table, for example, endurance is required to have a higher proportion in farther measurement areas, so the endurance weight is adjusted to 0.25 separately for deep-water area tasks. The group weight configuration parameter file is generated by the weight configuration program and stored in the group parameter library of the task planning system to ensure that the weight configuration can be flexibly adjusted for different task scenarios.

[0055] Step S152: Using the clustering algorithm, the heterogeneity score of each platform device is calculated based on the grouping weight configuration parameters, and the devices are divided into multiple collaborative task execution groups, thereby obtaining preliminary grouping scheme data, where the heterogeneity score = the normalized value of the remaining power of the device Normalized value of remaining power weight + communication signal strength Communication signal strength weight + normalized value of battery life Normalized value of endurance weight + measurement accuracy Measurement accuracy weight;

[0056] The embodiment of the present invention uses the K-means clustering algorithm to group the platform devices. First, the standardized remaining power, communication signal strength, endurance, and measurement accuracy data are normalized, for example, the remaining power range is mapped to the interval [0,1]. Then, the heterogeneous score of each platform device is calculated. For example, the heterogeneous score of an unmanned ship is calculated as: the normalized value of remaining power 0.8 × weight 0.4 + the normalized value of communication signal strength 0.6 × weight 0.3 + the normalized value of endurance 0.7 × weight 0.2 + the normalized value of measurement accuracy 0.9 × weight 0.1 = 0.76. Based on the heterogeneous score, the K-means algorithm is used to divide the devices into three groups, with the smallest difference in heterogeneous scores within the group and the largest difference between the groups. The preliminary grouping scheme data is stored in CSV format, which records the group number and score of each device.

[0057] Step S153: performing a balance consistency check based on multiple indicators on the preliminary grouping scheme data, and performing grouping optimization adjustment to obtain optimized grouping scheme data;

[0058] The embodiment of the present invention performs a balanced consistency check on the preliminary grouping scheme data and calculates the mean and variance of the indicators within the group. For example, for a certain group, the calculated mean of the remaining power within the group is 0.75 and the variance is 0.02, which is judged to be consistent. If the variance of the communication signal strength of a group exceeds a threshold (such as 0.05), it needs to be readjusted. The grouping is adjusted through a genetic algorithm based on multi-objective optimization, and some high-scoring devices are reallocated to weak groups to achieve the purpose of balance. For example, drones with excellent signal strength can be reallocated to task groups with poor communication coverage. Finally, the optimized grouping scheme data is output, and the task adaptability score of each group is marked and stored in the group optimization library of the task planning system.

[0059] Step S154: Determine the coordination number and allocation order of the grouped devices according to the optimized grouping scheme data and the current geographical location of the platform device and the starting state of task execution, thereby obtaining heterogeneous grouping scheme data.

[0060] In an embodiment of the present invention, a task planning system is used to generate a collaborative number and task sequence for each device based on the optimized grouping scheme data and the geographic location information of each device. For example, based on the real-time GPS coordinates and the starting task status, unmanned vessels near deep-water areas and drones in signal coverage areas are prioritized as group A, and their task execution order is set to "priority measurement area A." The system optimizes the task allocation path through a greedy algorithm to ensure that the collaborative numbering and grouping tasks maximize the measurement coverage and time efficiency requirements. Finally, heterogeneous grouping scheme data is generated, including device numbers, task sequences, and task area allocations, and stored in JSON format for subsequent path planning module calls.

[0061] This invention assigns the highest weight to the remaining battery life, ensuring that devices prioritize sufficient battery life when executing tasks, reducing the risk of mission interruption. Prioritizing other metrics (communication signal strength, endurance, and measurement accuracy) comprehensively considers device performance and mission requirements, providing a scientific basis for subsequent grouping. Flexible weight configuration enables the system to adjust priorities based on different mission scenarios (such as long-duration missions and complex communication environments), enhancing task adaptability. The weighting provides a quantitative standard for calculating heterogeneous scores, reducing the influence of human subjectivity and improving the objectivity and accuracy of the grouping process. The heterogeneous scores, calculated using normalization and weighting, intuitively reflect the device's comprehensive performance and mission adaptability, providing a quantitative reference for device grouping. The heterogeneous score formula comprehensively considers multiple performance metrics, ensuring a comprehensive and scientifically sound grouping basis. Automated device grouping using a clustering algorithm significantly improves grouping efficiency and adapts to complex tasks involving multi-device collaboration. The clustering algorithm divides task groups based on score similarity, ensuring performance complementarity and task synergy among the grouped devices. Initial grouping optimizes the combination of devices with different performance characteristics, fully leveraging their unique advantages while minimizing resource waste. A multi-metric balancing consistency check meticulously verifies the initial grouping, ensuring performance balance and compatibility across devices within each group, improving grouping quality. Optimization and adjustment correct any overreliance on a single metric in the initial grouping, making the grouping more rational. The optimized grouping scheme ensures that devices within each group complement each other's performance, enhancing overall efficiency during collaborative task execution. It also improves load balancing during task execution, preventing overloaded or underperforming devices from impacting overall task progress. Optimized grouping not only considers the current task but also offers a degree of versatility to accommodate potential dynamic task adjustments. Collaboration numbering and allocation sequence are determined based on the optimized grouping scheme, device location, and initial status, making task allocation clearer and reducing confusion during resource scheduling. This clear numbering and sequence provides a reliable basis for real-time device scheduling. Grouped devices work together based on numbering and sequence, minimizing resource waste and task delays caused by differences in location or status. A well-defined allocation sequence optimizes the temporal and spatial coordination of collaborative work. The collaboration number and allocation order can be dynamically adjusted during task execution, adding redundancy to the system and improving the fault tolerance and robustness of task execution.

[0062] Preferably, step S16 includes the following steps:

[0063] Step S161: extracting information on the time sequence, area division, measurement depth requirements, and accuracy standards of the tasks according to the preset water area measurement task sequence, thereby obtaining task sequence feature data;

[0064] This embodiment of the present invention extracts task feature information based on a water area survey task sequence. The system first reads a preset measurement task sequence configuration table, which includes the task execution time period (e.g., 8:00-12:00), measurement area number (e.g., Area A to Area C), target water depth range (e.g., 5-50 meters), and measurement accuracy requirements (e.g., depth measurement accuracy of ±0.5 meters). By parsing the task configuration table, the system extracts the task's time sequence, area divisions, measurement depth, and accuracy standards, and then structures these parameters to generate a task sequence feature data file. For example, the feature data for Task A includes the time period "8:00-9:00," the area "Deep Water Area A," the depth requirement "20-50 meters," and the accuracy standard "±0.5 meters." The data is formatted as a JSON file for subsequent module access.

[0065] Step S162: Matching the task sequence feature data with the measurement requirements based on the heterogeneous grouping scheme data, and determining the collaborative measurement task allocation of each group, thereby generating preliminary formation task allocation data;

[0066] This embodiment of the present invention matches task sequence feature data with measurement requirements based on heterogeneous grouping scheme data. The system first extracts the equipment characteristic data from the grouping scheme. For example, if Group A consists of one unmanned vessel and two drones, the equipment characteristics include a maximum flight time of 10 hours for the unmanned vessel, an effective communication radius of 500 meters for the drones, and a measurement equipment accuracy of ±0.3 meters. It then compares the task characteristic data and matches the equipment capabilities with the task requirements. For example, if Task A requires deepwater measurement, the system prioritizes unmanned vessels with high flight time and deepwater measurement capabilities and assigns the two drones as communication and auxiliary measurement equipment. Finally, preliminary formation task assignment data is generated. For example, if Group A executes Task A, the output includes the equipment number, assignment area, and task priority.

[0067] Step S163: Perform a feasibility analysis on the preliminary formation task allocation data based on the remaining power, endurance, communication range, and measurement equipment accuracy of the unmanned ship and the drone, and select the equipment group that meets the task execution conditions, thereby generating task equipment matching data;

[0068] This embodiment of the present invention performs a feasibility analysis on preliminary formation task allocation data. The system checks the allocation results based on the remaining battery power, endurance, communication range, and measurement equipment accuracy of the unmanned vessels and drones. For example, if a drone's current remaining battery power is 30%, insufficient to complete the measurement range of Task A, the system will reassign the task priority or replace the device. The system uses a constraint satisfaction problem (CSP) algorithm to simulate the task execution process and select device groups that meet the requirements. For example, an unmanned vessel with a remaining battery power of more than 70% and two drones with a flight time of more than 90 minutes are ultimately selected as the device group for Task A. The task equipment matching data records each device group and its task suitability score, and outputs it for use in the subsequent path optimization module.

[0069] Step S164: Preliminary optimization of the equipment positions and navigation paths of the collaborative formation is performed based on the mission equipment matching data, thereby generating preliminary formation plan data;

[0070] This embodiment of the present invention performs preliminary optimization of formation equipment positions and navigation paths based on mission equipment matching data. The system utilizes geographic information about the mission area (such as water depth distribution and obstacle locations) and the current coordinates of the equipment, combined with the A* path planning algorithm, to generate navigation paths for the unmanned vessel and drone. For example, to execute Mission A, the system plans for the unmanned vessel to move from its starting point along the edge of the deep water area, while the drone simultaneously covers communication blind spots in the mission area, ensuring real-time data transmission. The preliminary formation plan data, including initial equipment positions, navigation path points, path length, and estimated energy consumption, is output in XML format for easy access by the dynamic simulation module.

[0071] Step S165: Dynamically simulate and verify the preliminary formation plan data to evaluate the task execution efficiency, path coverage, and communication stability of the collaborative formation in different water measurement scenarios, identify potential bottlenecks, and make adjustments and optimizations to obtain optimized formation plan data;

[0072] The embodiment of the present invention dynamically simulates and verifies the preliminary formation plan data. The system loads the water geographic model and equipment characteristic parameters into the simulation environment, and uses simulation software to simulate the task execution efficiency, path coverage, and communication stability in different scenarios. For example, in complex waters (such as deep water areas with many obstacles), the system evaluates whether the current formation plan will result in repeated path coverage or communication interruption. If it is found that the efficiency of a drone is affected by a communication blind spot, its path is adjusted or relay equipment support is added. After identifying bottleneck problems through simulation results, the navigation path and equipment formation are adjusted, and finally the formation plan data is optimized to ensure that the measurement efficiency is improved by more than 10%.

[0073] Step S166: Determine the formation, communication protocol, and task allocation strategy of the unmanned ship and the unmanned aerial vehicle based on the optimized formation plan data, thereby obtaining the coordination parameter data.

[0074] This embodiment of the present invention determines the formation and task allocation strategy for unmanned vessels and drones based on optimized formation plan data. The system first designs a star formation centered on the unmanned vessel based on optimized equipment positions and task sequence. The drones are distributed within a 500-meter radius of communication coverage. Communication protocols are configured to enhance data transmission stability (for example, by using an adaptive frequency band switching protocol). The task allocation strategy specifies that the unmanned vessel is primarily responsible for deepwater measurements, while the drone is responsible for detailed shallow-water measurements and real-time coverage of communication blind spots. The resulting collaborative parameter data, including the formation plan, task allocation rules, and communication protocol configuration, is stored in a database file for access by the scheduling module.

[0075] This system extracts key information, such as the time sequence, regional divisions, measurement depth requirements, and accuracy standards, to ensure that key task requirements are not missed during subsequent formation and allocation. Clear task feature data provides a comprehensive reference for group matching, reducing task misunderstandings. Extracting multi-dimensional features adapts to different water measurement task requirements (such as shallow water and high-precision measurements), enhancing the system's task diversification capabilities. Feature data extraction standardizes and structures task information, providing an easy-to-use basic data format for subsequent task allocation and formation optimization. Based on a heterogeneous grouping scheme, task requirements are quickly matched and preliminary formation task allocation data is generated, ensuring rapid matching of tasks with equipment performance. This reduces manual intervention and enables intelligent and efficient task allocation. Each equipment group matches its own performance with task features, fully utilizing its capabilities and avoiding resource waste or uneven task allocation. Determining the preliminary allocation of collaborative measurement tasks lays the foundation for subsequent formation plan optimization, improving formation execution efficiency and task coverage. Based on a feasibility analysis of remaining battery power, endurance, communication range, and measurement equipment accuracy, devices that fail to meet mission requirements are eliminated, improving the reliability and effectiveness of task allocation and avoiding failures or interruptions during mission execution due to insufficient equipment performance. Matching equipment groups with clear execution capabilities ensures continuity and stability during mission execution. Matching equipment groups selected after the feasibility analysis further optimizes resource allocation, improving system utilization and mission execution efficiency. Preliminary optimization based on device matching data generates a reasonable device location distribution and navigation path, reducing task overlap and missed measurements, thereby improving efficiency. Preliminary optimization of the path avoids potential conflicts between devices and provides a solid foundation for subsequent dynamic optimization. By optimizing device location and path, the team's collaborative efficiency is improved, energy consumption is reduced, and flight time is extended. The optimized formation solution can adapt to different water measurement mission requirements, such as complex terrain and dynamic environments, supporting diverse mission scenarios. Simulations are used to verify and evaluate the team's execution efficiency in various water scenarios, ensuring the solution's adaptability to complex environments. Dynamic simulations can proactively identify bottlenecks in the solution and avoid serious errors during actual missions. The simulation analyzed path coverage and communication stability to ensure comprehensive mission execution and continuous information transmission. Adjustments and optimizations were made to address potential bottlenecks, improving the overall mission completion quality of the formation. Simulation verification results provided reliability assurance for the formation plan, which was optimized and made more robust, with enhanced adaptability to sudden environmental changes. The formation formations for the unmanned vessels and drones were determined to ensure more efficient device collaboration and avoid unnecessary device conflicts and path interference. A clear task allocation strategy was generated to ensure each device clearly understood its mission objectives and execution steps. Appropriate communication protocols were developed to ensure stable and real-time information exchange between devices, improving the overall performance of collaborative work.The generation of collaborative parameter data integrates all the results of grouping, matching, optimization and verification to ensure the efficient operation of the system from planning to execution.

[0076] Preferably, step S2 includes the following steps:

[0077] Step S21: Acquire the environmental data of the measurement area, including the water area topography, water depth distribution, water flow velocity, underwater geological composition, water transparency, and geographical obstacle information;

[0078] Step S22: performing data fusion and standardization processing on the environmental data of the measurement area, thereby establishing a unified environmental data feature vector, wherein the environmental data feature vector includes water area topographic feature data, underwater geological data, and water flow velocity and water transparency data;

[0079] Step S23: performing a multi-scale, multi-dimensional terrain feature and complexity analysis on the measurement area based on the environmental data feature vector, thereby constructing a regional terrain complexity assessment model;

[0080] Step S24: Divide the survey area into several sub-areas with similar terrain features and measurement difficulty according to the regional terrain complexity assessment model, and establish a sub-area topological relationship mapping to obtain sub-area topological relationship data;

[0081] Step S25: Correlate and match the collaborative parameter data with the sub-region topological relationship data, and construct a measurement area topology model that includes equipment capabilities, task constraints, and terrain characteristics;

[0082] Step S26: Utilizing a multi-objective optimization algorithm, the measurement area topology model is optimized for sub-area task allocation based on measurement coverage, data consistency, energy efficiency, and measurement accuracy constraints, thereby generating a collaborative measurement task allocation solution.

[0083] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0084] Step S21: Acquire the environmental data of the measurement area, including the water area topography, water depth distribution, water flow velocity, underwater geological composition, water transparency, and geographical obstacle information;

[0085] The embodiment of the present invention obtains environmental data of the measurement area. The system obtains water environment characteristic information by integrating multiple sensing devices and external data sources. For example, a multi-beam echo sounder is used to scan the water depth distribution, a high-resolution camera mounted on a drone is used to collect images of geographical obstacles, an underwater detector is used to measure the geological composition of the bottom of the water, a water flow monitor is used to record the water flow velocity, and an optical sensor is used to analyze the water transparency. Taking an actual scenario as an example, for an inland lake with an area of 2 square kilometers, the water depth data collected by the system ranges from 3 to 15 meters, the water flow velocity is 0.5 meters per second, the transparency is 3 meters, the geological composition is a mixed sand and gravel structure, and the geographical obstacles include a small island and several aquatic vegetation distributions. The collected multi-source data is finally saved as a unified environmental data file in the GeoJSON format.

[0086] Step S22: performing data fusion and standardization processing on the environmental data of the measurement area, thereby establishing a unified environmental data feature vector, wherein the environmental data feature vector includes water area topographic feature data, underwater geological data, and water flow velocity and water transparency data;

[0087] In an embodiment of the present invention, data fusion and standardization are performed on the acquired environmental data of the survey area. The system uses a weighted fusion algorithm to align data from different sources in spatial and temporal dimensions. For example, interpolation is used to generate regular grid data for water depth data throughout the entire survey area. Water velocity and transparency data are also normalized, and their numerical range is adjusted to [0, 1] to facilitate subsequent analysis. Combining multidimensional environmental data, the system generates an environmental data feature vector, which includes water area topographic characteristic values (such as slope and elevation difference), underwater geological type codes (1.2 for underwater sand and gravel mixture), mean water velocity (0.5), and mean transparency (0.8). The feature vector data is ultimately output and stored in a tabular format, with each row representing the data characteristics of a grid cell.

[0088] Step S23: performing a multi-scale, multi-dimensional terrain feature and complexity analysis on the measurement area based on the environmental data feature vector, thereby constructing a regional terrain complexity assessment model;

[0089] This embodiment of the present invention analyzes the terrain characteristics of the survey area based on the environmental data feature vectors. The system employs a hierarchical multiscale analysis method to calculate the rate of change of terrain slope, surface roughness, and depth gradient distribution within the area. It also uses geological characteristics and the magnitude of changes in water velocity to assess terrain complexity. For example, the system calculates a maximum slope of 45 degrees, a depth gradient standard deviation of 3.5, and a surface roughness index of 0.7 for a particular area. Combining these data, it generates a regional terrain complexity assessment model. This model classifies the feature vectors using a clustering algorithm and outputs a regional complexity score (e.g., a score ranging from 0 to 10, with a complexity score of 6.8 indicating a moderately complex area) to further guide regional delineation.

[0090] Step S24: Divide the survey area into several sub-areas with similar terrain features and measurement difficulty according to the regional terrain complexity assessment model, and establish a sub-area topological relationship mapping to obtain sub-area topological relationship data;

[0091] In an embodiment of the present invention, the measurement area is divided into sub-areas based on a terrain complexity assessment model. The system uses a K-means clustering algorithm to divide the entire measurement area into several sub-areas with similar terrain features, and simultaneously analyzes the topological relationships between the sub-areas, such as adjacency, shared boundary length, and positional relationship. Taking a lake with a measurement area of 2 square kilometers as an example, the system divides it into 5 sub-areas, of which sub-area A has a complexity score of 7.2 and an area of 0.4 square kilometers. It is adjacent to sub-area B and has a shared boundary length of 150 meters. The generated sub-area topological relationship data is represented by a graph structure, with nodes being sub-area numbers and edges being weights of adjacent relationships. The output format is XML for subsequent task allocation modules to call.

[0092] Step S25: Correlate and match the collaborative parameter data with the sub-region topological relationship data, and construct a measurement area topology model that includes equipment capabilities, task constraints, and terrain characteristics;

[0093] This embodiment of the present invention correlates and matches collaborative parameter data with sub-region topology data. The system extracts device capability parameters (such as maximum measurement depth, communication range, and flight time) and mission constraints (such as measurement accuracy and time window) and then generates a regional topology model based on the sub-region's topographical characteristics. For example, unmanned vessel A, with a maximum measurement depth of 50 meters and a flight time of 8 hours, matches Sub-region A, which has a high complexity score and a large area. Drone B, with a communication range of 500 meters, matches Sub-regions C and D, which have higher coverage requirements. The regional topology model includes a list of task-assigned devices for each sub-region and their capability matching scores, and is output as a network diagram for subsequent optimization.

[0094] Step S26: Utilizing a multi-objective optimization algorithm, the measurement area topology model is optimized for sub-area task allocation based on measurement coverage, data consistency, energy efficiency, and measurement accuracy constraints, thereby generating a collaborative measurement task allocation solution.

[0095] In this embodiment of the present invention, a multi-objective optimization algorithm is used to optimize task allocation within a regional topology model. The system sets coverage, data consistency, energy efficiency, and measurement accuracy as constraints, and iteratively calculates the optimal task allocation plan for each device using a particle swarm optimization algorithm. For example, to ensure that the coverage of sub-area A reaches over 90%, the system adjusts the path planning and task time allocation for the unmanned ship and drone, while optimizing energy consumption so that the remaining battery power after the unmanned ship completes its mission is greater than 20%. Ultimately, a collaborative measurement task allocation plan is generated, which includes a task list, coverage range, and estimated energy consumption for each device. The output format is a JSON file for the collaborative execution module to call.

[0096] By acquiring information on the water area's topographical characteristics, water depth distribution, water flow velocity, underwater geological composition, water transparency, and geographical obstacles, the present invention comprehensively collects environmental data for the survey area, ensuring a thorough understanding of the natural conditions involved in the survey task. This comprehensive data provides multi-dimensional support for subsequent analysis, optimization, and decision-making, avoiding the omission of key environmental factors. By collecting multi-dimensional data on the water environment, the system's measurement strategy and coordination scheme can be flexibly adjusted according to the needs of the specific survey task, enabling the system to adapt to complex and changing water environments. The acquired environmental data provides precise data support for subsequent modeling and optimization, ensuring the effectiveness and practical applicability of the models and optimization algorithms. Through fusion and standardization, environmental data from different sources and formats can be unified into feature vectors, simplifying the data processing process and improving data operability. Data fusion and standardization help remove noise and redundant information, improve data quality and reliability, and lay the foundation for subsequent analysis and modeling. The unified environmental data feature vector ensures data consistency during subsequent multi-dimensional analysis and modeling, avoiding errors or deviations caused by inconsistent data formats. A multi-scale, multi-dimensional terrain complexity analysis based on environmental data feature vectors comprehensively assesses the terrain characteristics and complexity of the survey area. This helps identify areas of increased measurement difficulty and potential challenging points. Terrain complexity assessment helps better match tasks to specific terrain features, improving task feasibility and execution efficiency. Complex areas can be specifically assigned high-precision equipment or optimized task execution methods. Terrain complexity assessment provides important decision-making support for subsequent area division, sub-area task allocation, and path planning, ensuring the system can flexibly adjust strategies based on terrain characteristics. Based on the terrain complexity assessment model, the survey area is divided into several sub-areas with similar terrain characteristics and measurement difficulty. This division facilitates precise task matching and more efficient task execution within each sub-area. By establishing a sub-area topological relationship mapping, the spatial relationship between sub-areas is clarified, providing a clear spatial organization for task allocation and collaborative work. Sub-area division not only improves measurement accuracy and efficiency but also optimizes resource allocation, ensuring that each device performs its task within the appropriate area, thereby reducing energy waste and equipment wear. By correlating and matching collaborative parameter data with sub-region topology data, appropriate tasks can be assigned based on device performance characteristics (such as remaining battery power, measurement accuracy, and endurance), ensuring task feasibility and optimal resource allocation. This correlation helps adjust the execution order and equipment of collaborative tasks based on terrain characteristics and task requirements, making overall task execution more efficient and seamless. By considering the correlation of multi-dimensional data, the system's ability to cope with changes in complex task environments is enhanced, improving its adaptability and flexibility.Optimizing the measurement area topology model using a multi-objective optimization algorithm balances multiple objectives (such as measurement coverage, data consistency, energy efficiency, and measurement accuracy) to ensure optimal execution of each task objective. Multi-objective optimization maximizes the utilization of system resources (such as equipment, energy, and time), avoiding resource waste and redundant operations during task execution. The optimized task allocation scheme ensures smooth task execution, avoiding interruptions or failures caused by improper task allocation or insufficient equipment capabilities. Through the rational allocation and optimization of tasks, different devices and sub-areas can work together better, further enhancing the collaborative operation capabilities of the entire measurement system and ensuring efficient and high-quality task completion.

[0097] Preferably, step S23 includes the following steps:

[0098] Step S231: using a spatial interpolation algorithm to perform continuity complement and smoothing processing on the water depth according to the water area terrain feature data, thereby obtaining water depth distribution grid data of the measurement area;

[0099] This embodiment of the present invention utilizes a spatial interpolation algorithm to complete and smooth water body terrain feature data. The system uses Kriging interpolation to predict incomplete water depth measurement data, combining measurement point data with water body boundary conditions to generate a continuous water depth distribution grid. In a particular lake measurement scenario, the sampling interval for known water depth data is 50 meters, with some areas missing due to insufficient sampling. The system generates a 2-meter resolution water depth grid through interpolation and uses a Gaussian smoothing filter to eliminate local outliers, ultimately forming a complete water depth distribution grid in GeoTIFF format.

[0100] Step S232: Classifying the underwater geological data based on different geological components and extracting the distribution range and physical characteristics of each category to generate geological composition characteristic data;

[0101] This embodiment of the present invention classifies underwater geological data. The system first uses spectral feature analysis to separate the underwater geology into three main components: sandstone, clay, and silt. It then uses a support vector machine (SVM) classification algorithm to extract the spatial distribution range of each component. In actual applications, the distribution density of the geological data point set obtained through underwater sonar detection is 200 points per square kilometer. After classification, the coverage rate of the sandstone layer is determined to be 40%, the clay layer is 35%, and the silt layer is 25%. Physical properties such as porosity (0.3 for the sandstone layer and 0.5 for the clay layer) are also extracted to generate geological composition characteristic data containing geological types and physical properties, which are then stored in Shapefile format.

[0102] Step S233: Modeling the water velocity distribution based on the water velocity and water transparency data, and calculating the velocity variation field and optical transparency field of the target water area, thereby obtaining hydrodynamic and optical distribution characteristic data;

[0103] This embodiment of the present invention models water flow velocity and water transparency data. The system uses a Computational Fluid Dynamics (CFD) model to calculate the velocity variation field and a Distributed Optical Attenuation (DOM) model to calculate the optical transparency field. During measurements of a specific river section, velocity data was sampled at 10-meter intervals, while transparency data was collected every 20 meters by an optical sensor. The system generated a 3D velocity distribution model through data fitting and, combined with the transparency field, generated comprehensive hydrodynamic and optical distribution characteristics. The results showed that the velocity range in the target area was 0.3-2.1 m / s, and the transparency distribution range was 2-8 m. The output was a gridded vector file for subsequent processing.

[0104] Step S234: Mapping the water depth distribution grid data, geological formation characteristic data, and hydrodynamic and optical distribution characteristic data into a unified coordinate system, and dividing the measurement area into uniform small units based on a gridding method, thereby obtaining a multi-feature grid data model, wherein each grid unit in the multi-feature grid data model contains terrain and environmental parameters;

[0105] This embodiment of the present invention maps multidimensional environmental data into a unified coordinate system. The system uses geographic information system (GIS) tools to align the water depth grid, geological features, and hydrodynamic and optical distribution characteristics. A gridding algorithm is then used to divide the measurement area into 10-meter x 10-meter cells. For modeling a port area, the system generated a multi-feature grid data model consisting of 10,000 cells. Each cell contained environmental parameters such as water depth (e.g., 5 meters), geological type (e.g., sand and gravel layer), flow velocity (e.g., 1.2 meters per second), and transparency (e.g., 3.5 meters). The data was ultimately saved in the HDF5 format for efficient computing and storage.

[0106] Step S235: Perform multi-scale, multi-dimensional terrain feature and complexity analysis on the multi-feature grid data model, thereby constructing a regional terrain complexity assessment model.

[0107] The embodiment of the present invention performs multi-scale, multi-dimensional terrain complexity analysis on a multi-feature grid data model. The system uses fractal dimension methods to calculate terrain complexity indicators and combines slope change rate, hydrodynamic gradient, and geological distribution heterogeneity to generate a regional terrain complexity assessment model. In a lake measurement scenario, the system calculated a fractal dimension of 1.75 with a 20-meter unit, a slope change rate of 15%, and a hydrodynamic gradient change rate of 8%. The final model shows that the regional complexity score ranges from 0 to 10, of which areas with a complexity score higher than 7 account for 35% of the total area. The assessment results are exported in JSON file format for subsequent measurement task planning and optimization.

[0108] This invention uses a spatial interpolation algorithm to complete and smooth water depth data, filling in gaps caused by insufficient measurements or equipment limitations, generating continuous and smooth water depth distribution data. This allows for a more accurate and complete representation of water body topography. Spatial interpolation methods can effectively reduce measurement errors and data noise, ensuring a smooth transition of water depth data and preventing subsequent analysis and modeling from being affected by sudden changes or noise. The completed and smoothed water depth distribution grid data provides reliable foundational data for subsequent topographic analysis, sub-region division, and mission planning, helping to improve mission execution accuracy. Classification based on different geological components allows for precise identification of the geological composition of the seabed, providing information on the distribution of geological layers, lithology types, and other physical characteristics, facilitating the optimization and adjustment of subsequent missions. Extracting the distribution range and physical characteristics of each category provides key insights for measurement mission execution and equipment scheduling. For example, different geological components may have different performance requirements for measurement equipment. Understanding geological characteristics can optimize equipment selection and mission scheduling. Understanding the specific composition and characteristics of the seabed geology helps assess the impact of geological factors on water body measurements and ensure the accuracy and reliability of measurement data. Modeling based on water velocity and water transparency data enables accurate simulation of water velocity variations and optical transparency distribution. This provides a key basis for considering water flow and transparency variations during subsequent water measurements. Modeling flow velocity variations and transparency fields helps determine the optimal time and location for underwater measurements. For example, areas of excessive flow or low transparency can negatively impact measurement tasks. Advance modeling and prediction facilitates task optimization. When multiple devices collaborate on a mission, understanding the hydrodynamic and optical distribution characteristics helps optimize inter-device coordination and task allocation, ensuring maximum system efficiency. Mapping data from diverse sources (such as bathymetry, geology, flow velocity, and transparency) into a unified coordinate system facilitates integration of multidimensional data, simplifies subsequent analysis, and avoids complications or errors caused by inconsistent data formats. Mapping data into a common coordinate system ensures spatial consistency across different data sets, avoids positioning errors during data integration, and ensures accurate regional analysis and mission planning. The multi-feature grid data model effectively integrates multiple environmental and terrain features, ensuring that each grid cell contains complete terrain and environmental parameters, providing high-quality data support for subsequent complexity analysis, task allocation, and path planning. Through multi-scale and multi-dimensional analysis, measurement tasks can be more precisely matched and adjusted based on factors such as terrain complexity, geological characteristics, and water flow conditions in different areas. Analysis of the multi-feature grid data model can assess the complexity of the measurement area and provide a basis for optimizing measurement tasks and equipment resource allocation. For example, high-complexity areas can be specifically allocated high-precision equipment or task execution strategies can be adjusted.Comprehensive analysis of terrain complexity and environmental parameters enhances the system's adaptability to changing environments, ensuring efficient execution of survey tasks under varying terrain conditions. This analysis facilitates decision-making optimization during survey area planning, thereby improving task execution efficiency, reducing unnecessary resource waste, and effectively enhancing measurement accuracy and path coverage.

[0109] Preferably, step S26 includes the following steps:

[0110] A multi-objective optimization function is established based on the measurement area topology model to obtain the target optimization function data. The function objectives in the multi-objective optimization function include maximizing measurement coverage, optimizing data consistency, maximizing energy efficiency, and minimizing measurement accuracy deviation. The constraints in the multi-objective optimization function include device endurance, communication range, and task time window. The sub-area task allocation of the measurement area topology model is optimized based on the target optimization function data to generate a collaborative measurement task allocation plan.

[0111] The embodiment of the present invention uses a multi-objective optimization algorithm (such as NSGA-II) to establish an optimization function for the measurement area topology model, where the objective function includes: ① maximization of measurement coverage, which is defined as the ratio of the measured sub-area area to the total area, with the goal of making the ratio close to 1; ② optimal data consistency, based on the similarity metric after data fusion (such as cosine similarity), with the goal of improving the consistency of measurement data between sub-areas; ③ maximization of energy efficiency, calculating the ratio of the energy consumption of each device to the area of its coverage area, with the goal of minimizing energy consumption per unit area; ④ minimum deviation of measurement accuracy, which is defined as the mean square error (RMSE) between the measurement result and the true value, with the goal of making the RMSE close to 0. Constraints include: ① device endurance (battery power is greater than the minimum threshold required for measurement); ② communication range (ensuring that the communication delay between collaborative devices is less than 50ms); ③ task time window (the completion time of all tasks does not exceed the set threshold). In a certain reservoir measurement scenario, the specific form of the objective optimization function is in Represents coverage, consistency and energy efficiency respectively, To meet time, power, and communication distance constraints, the system outputs objective optimization function data for sub-area task allocation. Based on this objective optimization function data, an improved particle swarm optimization (PSO) algorithm is used to optimize the sub-area task allocation within the measurement area topology model. Each particle represents a task allocation solution, including the matching relationship between devices and sub-areas. The particle's fitness function is calculated by the optimization function, and the algorithm finds the global optimal solution by updating its velocity and position. In a reservoir scenario, the equipment includes 10 unmanned vessels (with a flight time of 6 hours) and 5 drones (with a communication radius of 2 kilometers). The mission area is divided into 50 sub-areas. The goal is to rationally allocate tasks to device groups while maximizing the objective function. Through algorithm optimization, the generated allocation solution shows that unmanned vessels primarily cover sub-areas close to the shore (due to complex terrain), while drones are responsible for more distant areas (due to their longer flight time). The overall measurement mission coverage rate reaches 95%, the data consistency score is 0.92, energy efficiency is improved by 15%, and the total mission time is 4 hours. The output is a collaborative measurement task allocation solution file (JSON format), which contains the device number, assigned sub-area ID, task time period, and communication configuration parameters.

[0112] Through the design of a multi-objective optimization function, the present invention not only optimizes measurement coverage, energy efficiency, and measurement accuracy, but also takes into account actual constraints such as data consistency and equipment capabilities, so that the system can make flexible adjustments according to environmental changes during actual operation, ensuring the efficiency and accuracy of overall task execution. Through multi-objective optimization, the system can better adapt to dynamic environments and complex task requirements, while meeting the constraints, maximizing the optimization of various indicators, and improving the system's adaptability and flexibility in task execution in complex measurement environments. This optimization method can automatically adjust task allocation and path planning based on real-time information about tasks and equipment, improving the system's autonomous decision-making ability and intelligence level, reducing the need for manual intervention, and enhancing the system's intelligent operation.

[0113] Preferably, step S31 includes the following steps:

[0114] Step S31: dividing the collaborative measurement task allocation plan into sub-areas, extracting characteristics of measurement area, measurement depth requirements, measurement accuracy standards, time constraints, and equipment capability limitations, thereby obtaining task feature data;

[0115] Within the measurement area, this embodiment of the present invention extracts features based on the collaborative measurement task allocation scheme, including sub-area division data, measurement depth requirements, measurement accuracy standards, time constraints, and equipment capability limitations. Specifically, an SQL database is used to query the sub-area's terrain complexity information. The measurement depth requirements (e.g., 5 to 50 meters) and accuracy standards (e.g., deviation less than 0.1 meter) are parsed from the task constraint file as numerical features. Feature vectors are then generated by combining the performance parameters of the unmanned vessel and drone (e.g., a power limit of 500Wh and a maximum communication radius of 2 kilometers). For a reservoir measurement task, for example, the data generated after feature extraction includes sub-area numbers (e.g., A1, A2), each area's depth range, accuracy threshold, time window (e.g., within 2 hours), and the assigned equipment capability matrix. This data is then output as a task feature data file (CSV format).

[0116] Step S32: constructing an initial path planning model based on fuzzy logic according to the task feature data, and designing a multi-dimensional fuzzy membership function including terrain complexity, measurement accuracy, energy consumption, and communication reliability, thereby obtaining a fuzzy logic path planning model;

[0117] This embodiment of the present invention uses the task feature data generated in step S31 to construct an initial path planning model based on fuzzy logic. Fuzzy membership functions are designed for terrain complexity (e.g., slope greater than 30 degrees), measurement accuracy (e.g., demand deviation less than 0.1 meter), energy consumption (e.g., power consumption less than 20%), and communication reliability (signal strength greater than 80%). The membership functions are Gaussian or trigonometric. In a certain application scenario, the terrain complexity membership function is defined as: ,in is the slope, Generate a set of path planning rules through a fuzzy inference system (such as the Mamdani model), for example, "If the terrain complexity is high and the communication reliability is low, then prioritize the shortest path within the device coverage area," and output a fuzzy logic path planning model (implemented in Python).

[0118] Step S33: Acquire real-time platform status data of the unmanned ship and the drone, including current location, remaining battery power, communication signal strength, sensor working status, and dynamic environmental change information;

[0119] In this embodiment, the embedded monitoring systems of unmanned vessels and drones collect real-time platform status data, including location (GPS coordinates, with an accuracy of less than 2 meters), remaining battery power (e.g., 70%), communication signal strength (e.g., 80%), sensor status (e.g., normal temperature sensor), and dynamic environmental changes (e.g., wind speed 5 m / s, water flow 0.5 m / s). This data is uploaded to a central control system in real time via a 4G communication module and stored in a MongoDB database. This data is combined with timestamps to generate a dynamic status dataset, updated every second, which is used to input into the fuzzy logic path planning model.

[0120] Step S34: inputting the platform status data into the fuzzy logic path planning model and generating a preliminary path planning solution;

[0121] This embodiment of the present invention inputs platform status data into a fuzzy logic path planning model, using fuzzy reasoning to calculate the priority of each sub-area and an initial path planning plan. For a specific measurement task, unmanned aerial vehicles (UAVs) with shorter distances are prioritized for areas with complex terrain (e.g., slopes greater than 45 degrees), while drones with longer endurance are assigned to survey distant sub-areas. After generating the preliminary path planning plan, each device outputs its initial navigation path (e.g., the coordinate sequence from the starting point to the target area) and the task order (e.g., prioritizing sub-areas with significant depth variation).

[0122] Step S35: using a particle swarm algorithm to iteratively optimize the preliminary path planning solution based on the optimization objectives of maximizing coverage and minimizing repeated measurements, thereby obtaining an optimized path planning solution;

[0123] This embodiment of the present invention uses a particle swarm optimization (PSO) algorithm to iteratively optimize the initial path planning scheme, aiming to maximize measurement coverage and minimize duplicate measurements. Each particle represents a path sequence. The fitness function is calculated based on the measurement coverage and total path length. The velocity and optimal solution are used as reference when updating the particle position. For example, during the optimization process, device path coverage increased from 85% to 95%, while the area with duplicate measurements was reduced to 3%. The final output is the optimized path planning scheme, which includes the device number, path sequence, and optimization parameters (e.g., total energy consumption of 400Wh).

[0124] Step S36: Simulate the measurement path performance of the optimized path planning scheme under different environments and equipment states based on the Monte Carlo simulation method, and output a collaborative measurement path planning scheme including the path coordinates of the unmanned ship and the drone and the measurement timing.

[0125] This embodiment of the present invention inputs the optimized path planning solution into a Monte Carlo simulation system, simulating path performance based on various environmental parameters (e.g., water velocity fluctuations within ±0.2 m / s, wind speed fluctuations within ±1 m / s) and device status (e.g., power consumption rate fluctuations within ±5%). In one scenario, after 1000 simulations, the average device path coverage was 93%, and the proportion of communication delays below 50 ms was 97%. The system also identified the potential for path deviations in extreme environments (wind speeds exceeding 10 m / s), recommending the allocation of additional backup devices. The final output is a collaborative measurement path planning solution, including the path coordinates for each device, measurement timing, and optimization recommendations.

[0126] By extracting features such as measurement area sub-area division, measurement depth, accuracy standards, time constraints, and equipment capability limitations, this method enables a comprehensive understanding of task requirements and challenges. This process provides a detailed data foundation for subsequent path planning, task scheduling, and equipment allocation. The extracted task feature data helps the system rationally match equipment with task requirements, ensuring that each subtask is executed in accordance with its specific environment, measurement accuracy, and equipment capability constraints, thereby improving the quality and efficiency of task execution. By accurately extracting task feature data, the system can more precisely allocate and schedule tasks based on the varying capabilities of the equipment (such as battery life, accuracy, and communication capabilities), thereby reducing resource waste and improving overall operational efficiency. The fuzzy logic path planning model simultaneously considers multiple factors, including terrain complexity, measurement accuracy, energy consumption, and communication reliability, helping the system flexibly adapt to varying environmental changes and task requirements. Through multi-dimensional membership functions, the path planning model can process complex, fuzzy, and uncertain information to produce a more ideal preliminary path planning solution. Fuzzy logic provides stable path planning under uncertainty and environmental changes. Especially in complex terrain or unexpected environments, it can adjust the path in real time to ensure that tasks are completed on time and efficiently. In surveying tasks, both accuracy and efficiency must be considered. Fuzzy logic models can balance these factors, ensuring that path planning meets accuracy requirements while maximizing efficiency and reducing unnecessary duplication or ineffective routing. Real-time access to unmanned vessels and drones' status data, such as current location, remaining battery life, communication signal strength, and sensor status, enables timely monitoring of equipment operational status, ensuring that path planning meets current equipment capabilities and task requirements in actual operation. By acquiring real-time platform status data, the path planning model can dynamically adjust based on the actual equipment conditions, such as adjusting the route when battery levels are low or avoiding remote areas when communication signals are poor, thus preventing interruptions or failures during task execution. Incorporating real-time environmental changes and equipment status can better optimize task scheduling and path selection, improving the system's adaptability and responsiveness. By inputting real-time platform status data into the fuzzy logic model, the path can be dynamically adjusted based on current equipment capabilities and environmental conditions. This real-time optimization ensures that path planning is always based on the most accurate equipment and environmental information available, thereby improving task efficiency and accuracy. Based on real-time platform status information, the path planning model can flexibly respond to environmental changes and equipment dynamics, automatically adjusting the path to meet the actual requirements of the survey task. By inputting real-time platform status data, route planning becomes more tailored to actual conditions, avoiding mission failures or inefficient execution due to poor device status (e.g., low battery, communication interruptions, etc.). The particle swarm optimization (PSO) algorithm effectively balances global optimization with local search, avoiding local optimal solutions while efficiently exploring a broader solution space when searching for the optimal path.This global optimization property is ideal for path planning for complex tasks, particularly in collaborative measurement tasks, where it can yield the optimal path. The goal of particle swarm optimization is to maximize coverage of the measurement area while minimizing repeated measurement paths, thereby reducing energy consumption and improving measurement efficiency. Through iterative optimization, path planning meets these requirements, improving overall task efficiency. The particle swarm algorithm boasts excellent stability and convergence, achieving optimal path planning results within a limited number of iterations, reducing resource waste and measurement duplication caused by inappropriate path planning. Monte Carlo simulations can simulate path performance under various environments and device conditions to evaluate the robustness and adaptability of path planning solutions. This process effectively identifies potential issues, such as extreme weather conditions, device failures, and communication loss, ensuring that path planning can be successfully executed under a variety of real-world conditions. Through multiple simulations, potential path planning issues can be identified and optimized, ensuring the final solution is highly reliable and stable across diverse operating environments. Monte Carlo simulations can help identify bottlenecks or inefficiencies in path planning, providing early warning of potential resource constraints or mission failures, and enabling path optimization to ensure the successful completion of measurement tasks.

[0127] Preferably, step S4 includes the following steps:

[0128] Step S41: extracting the remaining battery percentage, mission progress, and navigation status of the UAV from the platform status data, thereby obtaining the UAV status data;

[0129] This embodiment of the present invention extracts information such as the drone's remaining battery percentage, mission progress, and navigation status based on platform status data. First, the drone's internal monitoring system obtains information about the remaining battery (e.g., 80%), mission progress (e.g., 50% completed), and navigation status (e.g., cruising). This data is typically uploaded to a central control platform via a real-time transmission module (e.g., LoRa or 4G). Next, this information is converted into data features to generate a drone status dataset. For example, for an offshore survey mission, assume that at a certain moment, the drone's battery level is 75%, mission progress is 30%, and the navigation status is "cruising" (moving at a speed of 12 km / h). These data records are updated in real time and stored in a database for subsequent processing.

[0130] Step S42: Compare the drone status data with the preset power threshold and determine a list of drone devices that need energy replenishment, thereby obtaining data on devices to be replenished;

[0131] This embodiment of the present invention compares drone status data with a preset battery threshold to determine which drones require energy recharge. For example, if the preset battery threshold is 30%, when a drone's remaining battery level falls below this threshold, the device is added to a list of devices awaiting recharge. In implementation, a comparison algorithm is first developed to monitor drone battery data in real time. If the battery level is less than 30% (for example, a drone with a remaining battery level of 28%) is added to the list of devices awaiting recharge. The output data includes information such as the device ID, current location (e.g., longitude 45.6°, latitude -34.3°), and current battery level (e.g., 28%). This information allows the rapid identification of drones requiring recharge for subsequent operations.

[0132] Step S43: Using the unmanned vessel as a mobile charging platform, triggering a rapid docking charging or battery swapping mechanism based on the data of the device to be supplied, thereby generating docking strategy data, wherein the docking strategy data includes docking position and timing plan information;

[0133] The embodiment of the present invention uses an unmanned ship as a mobile charging platform to perform rapid docking charging or battery swapping based on the data of the equipment to be supplied. In the specific operation, the current position and navigation path of the UAV in the data of the equipment to be supplied are first analyzed, combined with the real-time position and navigation path of the unmanned ship, to calculate the shortest approach path between the two. Assuming that the UAV to be supplied is in the deviation area of the navigation path, and the unmanned ship is traveling along a straight path, the system will automatically select an approach point for docking (for example, the distance between the unmanned ship and the UAV is 5km, and the expected docking time is 10 minutes). The docking strategy data includes a specific docking location (such as longitude and latitude coordinates), as well as docking timing (such as triggering docking within 10 minutes after the unmanned ship arrives at that point). In addition, the docking action can also trigger the battery swap mechanism to ensure rapid recovery of the UAV's energy.

[0134] Step S44: Optimize the timing and position of energy replenishment for the docking strategy data according to the unmanned ship and the UAV mission path in the collaborative measurement path planning scheme, thereby obtaining dynamic energy replenishment strategy data.

[0135] This embodiment of the present invention optimizes the energy recharge timing and location in the docking strategy data based on the mission paths of the unmanned vessel and drone in a collaborative measurement path planning scheme. First, the path planning system calculates the dynamic distance between the unmanned vessel and drone, the mission progress, and the estimated flight time. Combined with the power consumption rate, the optimal energy recharge timing and location are then inferred. For example, assume that the mission paths of the unmanned vessel and drone intersect, and the drone's remaining battery power is 32%. Based on the mission paths and the scheduled time, the drone is expected to reach the recharge point within 2 hours. At this point, the energy recharge strategy is optimized based on factors such as navigation status and mission priority. Assume that after optimization, the decision is made to recharge at a distance of 10 km from the drone to ensure that the mission is not affected and the recharge process is fast and seamless. Finally, dynamic energy recharge strategy data is output, including information on the optimal recharge location and timing (such as the coordinates of the recharge point, the estimated arrival time, and the mission status).

[0136] The present invention extracts data such as the drone's remaining battery percentage, mission progress, and navigation status, enabling real-time monitoring of the drone's operating status. This provides an accurate basis for subsequent energy refueling decisions, ensuring that changes in the drone's status during mission execution are captured promptly. The drone's mission progress data helps understand the drone's current operational stage and remaining tasks, enabling refueling to be performed at the appropriate time, avoiding the risk of battery exhaustion at critical moments. By extracting this data, the system can better assess the drone's actual energy needs, ensuring that refueling operations are tailored to the actual mission execution situation rather than pre-set general rules. By comparing the drone's remaining battery level with a preset power threshold, it can intelligently identify which drones require energy refueling. In this way, the system can accurately determine which devices require refueling first, avoiding unnecessary resource waste. Based on mission progress and device power consumption, the system can dynamically manage energy resources to prevent mission failures or interruptions due to insufficient power. By identifying devices to be refueled, the system can rationally schedule those devices, ensuring the orderly execution of refueling and measurement tasks. The unmanned vessel, acting as a mobile charging platform, provides flexible charging or battery swapping solutions, ensuring that drones can be promptly recharged when their batteries are low, preventing mission execution from being impacted by insufficient power. This flexibility enhances the system's adaptability. When the drone's battery level is detected to be below a threshold, the system can quickly trigger charging or battery swapping, ensuring the drone can quickly resume operations, reducing downtime and improving mission efficiency. By generating docking strategy data (including docking location and timing), the system ensures smooth charging or battery swapping, minimizing the impact of charging or battery replacement on mission progress. Based on the mission path in the collaborative measurement path planning scheme, the timing and location of energy recharge are optimized to ensure that recharge operations do not interrupt the measurement mission. By optimizing docking timing and location, energy recharge efficiency is maximized and the impact of recharge on mission progress is minimized. This optimized recharge strategy makes the collaborative operation between the drone and the unmanned vessel more efficient, avoiding unnecessary repetition or ineffective pauses during path planning and improving overall mission efficiency. The optimized dynamic energy recharge strategy ensures the drone's continuous operation during the measurement mission, preventing mission interruptions due to energy issues and improving mission continuity and stability. By optimizing recharge timing and location, the drone is recharged when and where it is most needed, avoiding premature or late recharges and maximizing energy resource utilization.

[0137] Preferably, step S44 includes the following steps:

[0138] Step S441: Generate path interaction point data based on the UAV position, navigation path, and UAV mission execution path in the collaborative measurement path planning scheme;

[0139] This embodiment of the present invention generates path interaction point data based on the unmanned vessel's position, navigation path, and drone's mission execution path in a collaborative measurement path planning scheme. First, a path planning algorithm is used to obtain the motion trajectories of the unmanned vessel and drone. Assume that the unmanned vessel's navigation path is a straight line segment AB, with starting point A at (45.6°, -34.3°) and end point B at (46.0°, -34.0°). The drone's mission path is a curved path from C (45.7°, -34.4°) to D (45.9°, -34.2°). Spatial analysis methods are used to calculate the intersection points between the unmanned vessel and drone paths. Path interaction point data is then derived using a path intersection calculation method (e.g., a line segment intersection algorithm). For example, intersection point P1 is at (45.8°, -34.3°). This path interaction point data includes the intersection coordinates and interaction time for use in subsequent steps.

[0140] Step S442: Preliminary matching of the timing plan in the docking strategy data based on the path interaction point data and the data of the equipment to be replenished, thereby generating a preliminary energy replenishment plan;

[0141] This embodiment of the present invention performs a preliminary match between the timing plans in the docking strategy data and the data of the device to be recharged. This generates a preliminary energy recharge plan. First, by comparing the data of the device to be recharged (e.g., a drone with a remaining battery of 28% and an estimated battery level of 20% upon arrival at interaction point P1), the recharge timing is determined based on the remaining battery levels of path interaction point P1 and the drone. If interaction point P1 is approximately 3 km from the unmanned vessel and the intersection is expected to arrive in 20 minutes, the recharge timing is determined based on the battery level and time window. Next, based on the unmanned vessel's navigation path, a feasible energy recharge location (e.g., intersection P1) is identified, and a preliminary recharge plan is determined using a recharge plan algorithm. The generated preliminary energy recharge plan includes the recharge timing (e.g., the estimated arrival time at intersection P1) and the location (intersection P1), ensuring that the recharge timing coincides with the path intersection.

[0142] Step S443: verifying the feasibility of the docking timing and position in the preliminary energy replenishment plan, thereby obtaining an optimized replenishment plan;

[0143] The embodiment of the present invention verifies the feasibility of the docking timing and position in the preliminary energy replenishment plan to obtain an optimized replenishment plan. This step verifies the feasibility of the plan through simulation and emulation. Assume that the preliminary energy replenishment plan predicts that the unmanned ship will replenish at the intersection P1, but before replenishment, the power consumption of the drone accelerates, and the power is low when it reaches the replenishment point in advance. By introducing a power consumption rate model (such as a linear regression model based on the real-time power data of the drone), the power change is dynamically calculated, and it is verified whether the timing can meet the replenishment needs. If the preliminary plan does not meet the requirements, modify the timing and position (such as adjusting to the intersection P2 or increasing the number of replenishments). Through simulation calculation and path correction, the new timing and position plan is verified, and the final feasible replenishment plan is determined.

[0144] Step S444: Dynamically adjust the docking sequence and position of the optimized replenishment plan according to the real-time energy status changes of the unmanned ship and the unmanned aerial vehicle, thereby obtaining dynamic energy replenishment strategy data.

[0145] This embodiment of the present invention dynamically adjusts and optimizes the docking sequence and location in the resupply plan based on the real-time energy status changes of the unmanned vessel and drone, thereby generating dynamic energy resupply strategy data. The energy status data of the unmanned vessel and drone are acquired in real time, and parameters such as their remaining battery power and mission progress are obtained via wireless communication. For example, if the drone's battery power drops rapidly during the resupply process, preventing it from completing the resupply at intersection point P1 as originally planned, the unmanned vessel is adjusted to a more appropriate location, such as point P2 (assuming the drone's remaining battery power is 12% and is expected to arrive within 30 minutes), based on the resupply plan optimization algorithm and real-time data scheduling. This ensures the continuity and efficiency of the resupply process. By dynamically monitoring and adjusting real-time data, dynamic energy resupply strategy data is generated, including the timing, location, and sequence of resupply points, ensuring efficient resupply scheduling in complex environments.

[0146] The present invention generates path interaction point data based on the unmanned vessel's position, navigation path, and drone mission execution path in the collaborative measurement path planning scheme. These interaction points are key locations for energy recharge between the unmanned vessel and drone. Generating this interaction point data accurately identifies potential encounter locations between the two, laying the foundation for subsequent energy recharge operations. By accurately generating path interaction points, task execution and energy recharge can be integrated, avoiding path overlap or unnecessary duplication between the two devices, thereby improving task execution efficiency. Based on path interaction point data and data on the device to be recharged (such as the drone's battery status), the system can intelligently match docking timing to ensure that recharge timing is consistent with task progress while not impacting path planning. This ensures that drones receive recharge promptly when their battery level is critical, avoiding mission interruptions due to low battery levels. A preliminary timing matching scheme optimizes the timing of recharge operations, avoiding delays in task progress or waste of resources during the recharge process due to premature or late recharges. This preliminary matching improves the response speed and accuracy of energy recharge. The feasibility of the docking timing and location in the preliminary energy resupply plan is verified to ensure that the resupply operation is feasible under real-world conditions. For example, verification is conducted on the device status, sufficient power, and clear routes at the time of resupply to avoid unrealistic resupply plans. This feasibility verification can effectively prevent path conflicts, communication interruptions, or excessive resource consumption caused by issues such as inappropriate resupply timing and improper docking location during mission execution, thereby improving system stability. By verifying the feasibility of the optimized resupply plan, the resupply operation can be carried out smoothly under complex environmental conditions, ensuring efficient system operation and successful mission completion. The docking sequence and locations of the optimized resupply plan are dynamically adjusted based on the real-time energy status of the unmanned vessel and drone. This allows for real-time response to unexpected situations, such as rapid device power loss and changes in mission progress, ensuring that the resupply plan always meets actual requirements. Dynamically adjusting the plan increases the system's adaptability and flexibility. During mission execution, device status and the external environment may change. Dynamically adjusting the resupply plan ensures that resupply operations can continue to be efficient despite these environmental changes. By adjusting refueling timing and location based on real-time energy status, we can effectively avoid energy shortages or oversupply caused by over-reliance on pre-set plans, thereby ensuring the long-term stable operation of drones and unmanned vessels. Dynamic adjustments maximize energy efficiency, avoiding wasted resources due to insufficient or excessive refueling during missions. Furthermore, refueling is ensured when it is most needed, reducing mission interruptions and equipment downtime caused by insufficient power.

[0147] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0148] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring water and underwater terrain based on the collaboration of an unmanned vessel and a drone, characterized in that: The following steps are involved: Step S1: Obtain platform parameter data of the unmanned ship and the unmanned aerial vehicle; According to the platform parameter data, heterogeneous grouping is performed based on remaining power, communication signal strength, endurance and measurement accuracy, and a collaborative formation plan is determined according to the preset water measurement task sequence to obtain collaborative parameter data; Step S2: Obtaining measurement area environmental data; performing regional division modeling based on the water area terrain characteristics and environmental complexity according to the measurement area environmental data, and applying the collaborative parameter data to the regional model to obtain a measurement area topology model; optimizing the sub-region task allocation of the measurement area topology model to obtain a collaborative measurement task allocation plan; Step S3: Construct a fuzzy logic path planning model based on the collaborative measurement task allocation plan, and dynamically optimize the path based on the platform status data obtained through real-time monitoring to obtain a collaborative measurement path planning plan with minimum repeated coverage and omissions. Step S3 includes: Step S31: dividing the collaborative measurement task allocation plan into sub-areas, extracting characteristics of measurement area, measurement depth requirements, measurement accuracy standards, time constraints, and equipment capability limitations, thereby obtaining task feature data; Step S32: constructing an initial path planning model based on fuzzy logic according to the task feature data, and designing a multi-dimensional fuzzy membership function including terrain complexity, measurement accuracy, energy consumption, and communication reliability, thereby obtaining a fuzzy logic path planning model; Step S33: Acquire real-time platform status data of the unmanned ship and the drone, including current location, remaining battery power, communication signal strength, sensor working status, and dynamic environmental change information; Step S34: inputting the platform status data into the fuzzy logic path planning model and generating a preliminary path planning solution; Step S35: using a particle swarm algorithm to iteratively optimize the preliminary path planning solution based on the optimization objectives of maximizing coverage and minimizing repeated measurements, thereby obtaining an optimized path planning solution; Step S36: Simulating the performance of the optimized path planning scheme under different environments and equipment states based on the Monte Carlo simulation method, and outputting a collaborative measurement path planning scheme including the path coordinates of the unmanned vessel and the drone and the measurement timing; Step S4: Using the unmanned vessel as a mobile charging platform, a fast docking charging or battery swap mechanism is triggered based on the remaining battery power of the UAV in the platform status data and the preset battery power threshold. The timing and location of energy replenishment are optimized according to the collaborative measurement path planning scheme to obtain dynamic energy replenishment strategy data; Step S5: The collaborative tasks of the unmanned boat and the UAV are scheduled in real time according to the dynamic energy replenishment strategy data, the collaborative measurement path planning scheme, and the collaborative measurement task allocation scheme, thereby realizing the terrain measurement above and below the water.

2. The method for measuring water and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting remaining power, communication signal strength, current endurance, and measurement equipment status parameters of the unmanned ship's hardware equipment to obtain unmanned ship platform parameter data; Step S12: Obtaining the drone platform parameter data, including the drone's remaining flight power, flight endurance, current communication signal quality, and sensor working status information; Step S13: uploading the unmanned ship platform parameter data and the unmanned aerial vehicle platform parameter data to the measurement and control center through the data transmission interface, thereby obtaining the platform parameter data; Step S14: performing data denoising and time synchronization processing on the platform parameter data to obtain standardized platform parameter data; Step S15: performing heterogeneous grouping based on remaining power, communication signal strength, endurance, and measurement accuracy according to the standardized platform parameter data, thereby obtaining heterogeneous grouping solution data; Step S16: Generate a collaborative formation plan according to the preset water area measurement task sequence and the heterogeneous grouping plan data, thereby obtaining collaborative parameter data.

3. The method for measuring water and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: setting priority weights of parameter indicators based on standardized platform parameter data, wherein remaining power has the highest weight, followed by communication signal strength, endurance, and measurement accuracy, thereby forming group weight configuration parameters; Step S152: Using the clustering algorithm, the heterogeneity score of each platform device is calculated based on the grouping weight configuration parameters, and the devices are divided into multiple collaborative task execution groups, thereby obtaining preliminary grouping scheme data, where the heterogeneity score = the normalized value of the remaining power of the device Normalized value of remaining power weight + communication signal strength Communication signal strength weight + normalized value of battery life Normalized value of endurance weight + measurement accuracy Measurement accuracy weight; Step S153: performing a balance consistency check based on multiple indicators on the preliminary grouping scheme data, and performing grouping optimization and adjustment, thereby obtaining optimized grouping scheme data; Step S154: Determine the coordination number and allocation order of the grouped devices according to the optimized grouping scheme data and the current geographical location of the platform device and the starting state of task execution, thereby obtaining heterogeneous grouping scheme data.

4. The method for measuring surface and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 3, characterized in that: Step S16 includes the following steps: Step S161: extracting information on the time sequence, area division, measurement depth requirements, and accuracy standards of the tasks according to the preset water area measurement task sequence, thereby obtaining task sequence feature data; Step S162: Matching the task sequence feature data with the measurement requirements based on the heterogeneous grouping scheme data, and determining the collaborative measurement task allocation of each group, thereby generating preliminary formation task allocation data; Step S163: Perform a feasibility analysis on the preliminary formation task allocation data based on the remaining power, endurance, communication range, and measurement equipment accuracy of the unmanned ship and the drone, and select the equipment group that meets the task execution conditions, thereby generating task equipment matching data; Step S164: Preliminary optimization of the equipment positions and navigation paths of the collaborative formation is performed based on the mission equipment matching data, thereby generating preliminary formation plan data; Step S165: Dynamically simulate and verify the preliminary formation plan data to evaluate the task execution efficiency, path coverage, and communication stability of the collaborative formation in different water measurement scenarios, identify potential bottlenecks, and make adjustments and optimizations to obtain optimized formation plan data; Step S166: Determine the formation, communication protocol, and task allocation strategy of the unmanned ship and the unmanned aerial vehicle based on the optimized formation plan data, thereby obtaining the coordination parameter data.

5. The method for measuring water and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 4, characterized in that: Step S2 includes the following steps: Step S21: Acquire the environmental data of the measurement area, including the water area topography, water depth distribution, water flow velocity, underwater geological composition, water transparency, and geographical obstacle information; Step S22: performing data fusion and standardization processing on the environmental data of the measurement area, thereby establishing a unified environmental data feature vector, wherein the environmental data feature vector includes water area topographic feature data, underwater geological data, and water flow velocity and water transparency data; Step S23: performing a multi-scale, multi-dimensional terrain feature and complexity analysis on the measurement area based on the environmental data feature vector, thereby constructing a regional terrain complexity assessment model; Step S24: Divide the survey area into several sub-areas with similar terrain features and measurement difficulty according to the regional terrain complexity assessment model, and establish a sub-area topological relationship mapping to obtain sub-area topological relationship data; Step S25: Correlate and match the collaborative parameter data with the sub-region topological relationship data, and construct a measurement area topology model that includes equipment capabilities, task constraints, and terrain characteristics; Step S26: Utilizing a multi-objective optimization algorithm, the measurement area topology model is optimized for sub-area task allocation based on measurement coverage, data consistency, energy efficiency, and measurement accuracy constraints, thereby generating a collaborative measurement task allocation solution.

6. The method for measuring surface and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 5, characterized in that: Step S23 includes the following steps: Step S231: using a spatial interpolation algorithm to perform continuity complement and smoothing processing on the water depth according to the water area terrain feature data, thereby obtaining water depth distribution grid data of the measurement area; Step S232: Classifying the underwater geological data based on different geological components and extracting the distribution range and physical characteristics of each category to generate geological composition characteristic data; Step S233: Modeling the water velocity distribution based on the water velocity and water transparency data, and calculating the velocity variation field and optical transparency field of the target water area, thereby obtaining hydrodynamic and optical distribution characteristic data; Step S234: Mapping the water depth distribution grid data, geological formation characteristic data, and hydrodynamic and optical distribution characteristic data into a unified coordinate system, and dividing the measurement area into uniform small units based on a gridding method, thereby obtaining a multi-feature grid data model, wherein each grid unit in the multi-feature grid data model contains terrain and environmental parameters; Step S235: Perform multi-scale, multi-dimensional terrain feature and complexity analysis on the multi-feature grid data model, thereby constructing a regional terrain complexity assessment model.

7. The method for measuring surface and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 6, characterized in that: Step S26 includes the following steps: A multi-objective optimization function is established based on the measurement area topology model to obtain the target optimization function data. The function objectives in the multi-objective optimization function include maximizing measurement coverage, optimizing data consistency, maximizing energy efficiency, and minimizing measurement accuracy deviation. The constraints in the multi-objective optimization function include device endurance, communication range, and task time window. The sub-area task allocation of the measurement area topology model is optimized based on the target optimization function data to generate a collaborative measurement task allocation plan.

8. The method for measuring surface and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 7, characterized in that: Step S4 includes the following steps: Step S41: extracting the remaining battery percentage, mission progress, and navigation status of the UAV from the platform status data, thereby obtaining the UAV status data; Step S42: Compare the drone status data with the preset power threshold and determine a list of drone devices that need energy replenishment, thereby obtaining data on devices to be replenished; Step S43: Using the unmanned vessel as a mobile charging platform, triggering a rapid docking charging or battery swapping mechanism based on the data of the device to be supplied, thereby generating docking strategy data, wherein the docking strategy data includes docking position and timing plan information; Step S44: Optimize the timing and position of energy replenishment for the docking strategy data according to the unmanned ship and the UAV mission path in the collaborative measurement path planning scheme, thereby obtaining dynamic energy replenishment strategy data.

9. The method for measuring surface and underwater topography based on the collaboration of an unmanned vessel and a drone according to claim 8, characterized in that: Step S44 includes the following steps: Step S441: Generate path interaction point data based on the unmanned vessel position, navigation path, and UAV mission execution path in the collaborative measurement path planning scheme; Step S442: Preliminary matching of the timing plan in the docking strategy data based on the path interaction point data and the data of the equipment to be replenished, thereby generating a preliminary energy replenishment plan; Step S443: verifying the feasibility of the docking timing and position in the preliminary energy replenishment plan, thereby obtaining an optimized replenishment plan; Step S444: Dynamically adjust the docking sequence and position of the optimized replenishment plan according to the real-time energy status changes of the unmanned ship and the unmanned aerial vehicle, thereby obtaining dynamic energy replenishment strategy data.

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