Method and device for allocating time slot resources in cooperative ad hoc network of unmanned clusters

Abstract

The embodiment of the application provides a kind of unmanned cluster cooperative ad hoc network time slot resource allocation method and device, by innovatively designing task configuration system, by state machine and resource matrix, the effective mapping of demand is realized.Construct time slot allocation mechanism, combine connectivity analysis and resource matching, establish reliable scheduling scheme.Introduce dynamic optimization, through state monitoring and strategy adjustment, ensure the adaptability of allocation.The method effectively solves the deficiencies of traditional technology in task configuration, time slot allocation and dynamic adjustment, etc., provides technical support for unmanned cluster cooperation.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a method and apparatus for allocating time slot resources in a UAV swarm collaborative self-organizing network. Background Technology

[0002] Existing methods for allocating time slots in unmanned swarm systems have significant shortcomings. Traditional systems perform poorly in terms of task configuration and resource allocation, failing to effectively support the needs of search and rescue missions and impacting communication efficiency.

[0003] Furthermore, existing technologies suffer from bottlenecks in time slot allocation and network connectivity. Most systems lack a robust time frame structure and state analysis strategy, leading to inefficient resource scheduling.

[0004] Existing systems have technical shortcomings in dynamic adjustment. The lack of in-depth analysis of node states makes it difficult to achieve flexible resource allocation through strategy optimization, thus impacting system performance. Solving these problems is crucial for improving the collaborative capabilities of unmanned swarms. Summary of the Invention

[0005] To address the problems in existing technologies, this application provides a method and apparatus for allocating time slot resources in unmanned swarm collaborative self-organizing networks. This method and apparatus can effectively solve the shortcomings of traditional technologies in terms of task configuration, time slot allocation, and dynamic adjustment, and provide technical support for unmanned swarm collaboration.

[0006] To solve at least one of the above problems, this application provides the following technical solution:

[0007] Firstly, this application provides a method for allocating time slot resources in an unmanned cluster cooperative self-organizing network, including:

[0008] Set the cross-domain cluster node as the gateway control node, the work group node as the surface sensing node, and the unmanned platform node as the search and rescue execution node. Generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on the search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert the task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit.

[0009] The time frame is divided into a time synchronization area and a data interaction area. The time frame structure is written into the control unit of each surface unmanned node. The buffer queue length of the adjacent search and rescue node is read to generate a time slot occupancy table that reflects the communication status of the unmanned cluster. The network connectivity matrix between the surface nodes is calculated based on the time slot occupancy table. The network connectivity matrix is ​​matched with the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario. The initial time slot allocation scheme is written into the resource scheduling unit.

[0010] Control information is written into the header of the data frame in a backpack structure. A frame configuration table is generated based on the electromagnetic propagation characteristics at sea. The frame configuration table is used for data frame processing. The time slot occupancy table is read to perform distributed time slot selection for search and rescue nodes. An unmanned node queue status table is established based on the network connectivity matrix. The time slot occupancy dynamic adjustment coefficient is calculated based on the unmanned node queue status table. The allocation scheme is corrected according to the dynamic adjustment coefficient. The corrected allocation scheme is written into the time slot resource scheduling unit to generate a time slot resource scheduling strategy that supports the maritime maneuvering of unmanned swarms.

[0011] Furthermore, it also includes: constructing a cluster organization table containing control hierarchy, command relationship, and operation area; reading the cross-domain cluster node identifier and mapping it to the gateway control node; reading the operation group node identifier and mapping it to the water surface sensing node; reading the unmanned platform node identifier and mapping it to the search and rescue execution node; and writing the cluster organization table into the node management unit.

[0012] Construct a task planning table that includes task attributes, message types, and communication cycles. Divide the node communication levels according to the cluster organization table, generate a task configuration matrix that includes node identifiers, level attributes, and communication requirements, and write the task configuration matrix into the task management unit.

[0013] Furthermore, it also includes: constructing a state machine configuration table containing state transition conditions, task triggering conditions, and simulation parameters; generating a task state machine based on the task configuration matrix; performing Monte Carlo simulations on search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the state machine; generating a task requirement prediction table; and writing the task requirement prediction table into the simulation engine.

[0014] A resource mapping table containing communication performance, data capacity, and quality indicators is constructed. Communication resource requirements are calculated based on the task requirement prediction table, a time slot resource matrix is ​​generated, and the time slot resource matrix is ​​written into the resource allocation unit according to priority.

[0015] Furthermore, it also includes: constructing a time frame structure table containing synchronization frame length, data frame length, and protection interval; dividing the time frame into a time synchronization area and a data interaction area according to a fixed interval; generating a sub-frame parameter table containing frame format, number of time slots, and time slot length; and writing the sub-frame parameter table into the control unit of each unmanned node on the water surface.

[0016] A neighbor status table containing node address, queue length, and lifetime is constructed. The cache queue length information of neighboring search and rescue nodes is read, and a time slot occupancy table containing time slot status, reservation information, and hop count information is generated. The time slot occupancy table is then written into the status management unit.

[0017] Furthermore, it also includes: constructing a network topology table containing node identifiers, link status, and time slot status; calculating the connectivity between water surface nodes based on the time slot occupancy table; generating a network connectivity matrix; and writing the network connectivity matrix into the topology management unit.

[0018] A matching strategy table containing resource requirements, connectivity status, and allocation weights is constructed. The network connectivity matrix and the time slot resource matrix are subjected to weighted matching operations to generate an initial time slot allocation scheme. The initial time slot allocation scheme is then written into the resource scheduling unit.

[0019] Furthermore, it also includes: constructing a dedicated frame header table containing frame type, reservation field, and queue length; writing control information into the data frame header in a backpack structure; generating a sub-frame configuration table based on the characteristics of electromagnetic propagation at sea; and writing the sub-frame configuration table into the data processing unit.

[0020] A time slot status table containing idle, occupied, and busy states is constructed. The time slot occupancy table is read to determine the status, and a distributed selection matrix containing sending time slots, receiving time slots, and reserved time slots is generated. The distributed selection matrix is ​​then written into the time slot scheduling unit.

[0021] Furthermore, it also includes: constructing a status monitoring table containing node identifiers, queue lengths, and lifecycles; calculating the queue status distribution among nodes based on the network connectivity matrix; generating an unmanned node queue status table; calculating a time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table; and writing the dynamic adjustment coefficient into the adjustment control unit.

[0022] A resource reallocation table containing an initial scheme, adjustment coefficients, and priorities is constructed. The initial time slot allocation scheme is modified according to the dynamic adjustment coefficients to generate a dynamic time slot allocation scheme. The dynamic time slot allocation scheme is then written into the time slot resource scheduling unit.

[0023] Secondly, this application provides a time slot resource allocation device for unmanned cluster cooperative self-organizing networks, comprising:

[0024] The task configuration module is used to set cross-domain cluster nodes as gateway control nodes, work group nodes as surface sensing nodes, and unmanned platform nodes as search and rescue execution nodes, generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit.

[0025] The resource scheduling module is used to divide the time frame into a time synchronization area and a data interaction area, write the time frame structure into the control unit of each surface unmanned node, read the buffer queue length of the adjacent search and rescue node, generate a time slot occupancy table reflecting the communication status of the unmanned cluster, calculate the network connectivity matrix between the surface nodes according to the time slot occupancy table, perform matching operation between the network connectivity matrix and the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario, and write the initial time slot allocation scheme into the resource scheduling unit.

[0026] The time slot adjustment module is used to write control information into the data frame header in a backpack structure, generate a frame configuration table based on the electromagnetic propagation characteristics at sea, use the frame configuration table for data frame processing, read the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, establish an unmanned node queue status table based on the network connectivity matrix, calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table, correct the allocation scheme according to the dynamic adjustment coefficient, write the corrected allocation scheme into the time slot resource scheduling unit, and generate a time slot resource scheduling strategy that supports unmanned swarm maritime maneuvering.

[0027] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method.

[0028] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method described above.

[0029] Fifthly, this application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method.

[0030] As can be seen from the above technical solution, this application provides a method and apparatus for allocating time slot resources in an unmanned swarm collaborative self-organizing network. Through an innovative design of the task configuration system, and by using a state machine and resource matrix, it achieves effective mapping of requirements. A time slot allocation mechanism is constructed, and a reliable scheduling scheme is established by combining connectivity analysis and resource matching. Dynamic optimization is introduced, and the adaptability of the allocation is ensured through state monitoring and policy adjustment. This method effectively solves the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, providing technical support for unmanned swarm collaboration. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the unmanned cluster cooperative self-organizing network time slot resource allocation method in the embodiments of this application;

[0033] Figure 2 This is a structural diagram of the unmanned cluster collaborative self-organizing network time slot resource allocation device in the embodiments of this application;

[0034] Figure 3 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0035] Figure label:

[0036] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0039] To address the problems existing in current technologies, this application provides a method and apparatus for allocating time slot resources in unmanned collaborative self-organizing networks. Through an innovative task configuration system design, it achieves effective mapping of requirements using a state machine and resource matrix. A time slot allocation mechanism is constructed, combining connectivity analysis and resource matching to establish a reliable scheduling scheme. Dynamic optimization is introduced, ensuring the adaptability of the allocation through state monitoring and policy adjustment. This method effectively solves the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, providing technical support for unmanned cluster collaboration.

[0040] To effectively address the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, and to provide technical support for unmanned swarm collaboration, this application provides an embodiment of a time slot resource allocation method for unmanned swarm collaborative self-organizing networks. See [link to embodiment]. Figure 1 The unmanned cluster collaborative self-organizing network time slot resource allocation method specifically includes the following:

[0041] Step S101: Set the cross-domain cluster node as the gateway control node, the work group node as the surface sensing node, and the unmanned platform node as the search and rescue execution node. Generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on the search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert the task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit.

[0042] This embodiment focuses on a typical mixed-team scenario of maritime disaster relief. The prerequisite is that node registration and link connectivity testing have been completed. The node list includes cross-domain cluster nodes capable of satellite / 5G backhaul, operational group nodes responsible for aerial reconnaissance and surface inspection, and numerous unmanned platform nodes performing net-like searches, life-saving drops, and towing salvage. The primary action of S101 is to map node identifiers from different sources to three operational functions: cross-domain cluster nodes are mapped to gateway control nodes, used for uplinking to satellite or 5G and undertaking cross-domain command; operational group nodes are mapped to surface sensing nodes, responsible for reconnaissance, situational awareness, and backbone relay; and unmanned platform nodes are mapped to search and rescue execution nodes, responsible for side-scan searches, close-range tracking, and salvage. This mapping is not a simple renaming; rather, it involves constructing an organizational table within the node management unit that includes control levels, command relationships, and operational areas. The hierarchical attributes are solidified based on the link capabilities, energy reserves, and payload sensor types reported by the nodes, serving as the basis for subsequent message cycles and resource priorities.

[0043] After mapping, this embodiment generates a task configuration matrix based on the bidirectional constraints of task and communication. The specific process is as follows: First, a task planning table is constructed, defining task attributes (e.g., "area coverage," "target tracking," "casualty evacuation and retrieval"), message types (cross-domain command broadcasting, group status reporting, group member status reporting, heartbeat, synchronization), and communication cycle intervals. Then, the matrix is ​​expanded hierarchically, with rows corresponding to node identifiers, columns corresponding to message types and cycle requirements, and cells recording the communication overhead profile of the node in the current task phase (expected message rate, average load, timing jitter tolerance). For example, the cross-domain broadcasting of the gateway control node is set to the 1–5 Hz range, the water surface sensing node sends group status reports at 0.5 Hz and is linearly related to the group size, the status reporting of the search and rescue node is maintained at 1 Hz, and a high-priority event frame is temporarily superimposed when a suspected drowning person is captured. The resulting task configuration matrix not only depicts the message cycle but also embeds priority tags and transmission reliability requirements, facilitating the subsequent mapping of task requirements to resources.

[0044] To incorporate the dynamic nature of the task into the time-varying constraints of communication resources, this embodiment constructs a task state machine based on the task configuration matrix. The state machine configuration table defines state sets (area coverage S_cov, target tracking S_trk, casualty evacuation S_med, etc.), state transition conditions (e.g., target confidence of a sensing node exceeds a threshold, rescue boat approaches the target to a safe radius, cross-domain nodes issue a convergence command), and inference parameters (sea state level, crew density, target appearance rate). The state machine is used in a Monte Carlo simulation engine: with time as the axis, given the initial task partition and node distribution, it repeatedly samples target appearance and movement, link fading, and node joining / leaving to drive state transitions. On each sample path, based on the current state, the message period and priority are retrieved from the task configuration matrix and converted into a transmission requirement per unit time frame. The impact of sampling parameters such as target density, node spacing, and link SNR on the results follows natural laws: increased target density increases the frequency of perception broadcasting and tracking reporting; increased node spacing leads to an increase in routing hops; each message needs to occupy more time slots to offset packet loss; and decreased SNR triggers an increase in retransmission budget. All of these are reflected through the intra-frame acknowledgment and retransmission upper limit model.

[0045] After generating the task requirement prediction table through deduction, this embodiment converts the "service-side dimensions" into "time-slot-side dimensions." A resource mapping table is constructed, with entries including communication performance (latency tolerance, reliability level), data capacity (message length statistics, peak / average ratio), and quality indicators (allowed retransmission count, maximum jitter). The mapping process uses frame parameters (frame length, time slot length, guard interval) and message length fields to calculate the nominal occupancy of each type of message within a frame. When an acknowledgment / retransmission mechanism exists, the retransmission expectation is estimated using link quality parameters and added to the nominal occupancy. To facilitate resource orchestration, this embodiment sums the time slot requirements for each type of node and each type of message in each state and arranges them hierarchically by priority to obtain a time slot resource matrix. Its rows correspond to nodes / levels, and columns correspond to categorized time slot pools within a frame (such as control priority pool, general data pool, reclaimable synchronization pool). The matrix elements are the requirement quota and the tolerable fluctuation range.

[0046] It is important to emphasize that the simulation is not a one-time offline conclusion. In maritime search and rescue scenarios, the transition from area coverage to target tracking is often triggered by sensing nodes, which immediately changes the resource structure: the proportion of high-priority control frames and tracking reports increases, and periodic broadcasts are appropriately compressed; during casualty evacuation, the confirmation link between the search and rescue execution node and the gateway control node must be more stable, and the proportion reserved for confirmation frames in the time slot pool is increased. The state machine statistically analyzes these phased structural changes during the simulation, and the time slot resource matrix accordingly forms two sets of quotas: "steady state + transition state." Based on this, the resource allocation unit reduces oscillations when switching strategies during runtime.

[0047] To ensure the resource matrix can be directly consumed by subsequent connectivity and occupancy table matching operations, this embodiment embeds two types of key values ​​within the matrix: hierarchical keys (gateway / sensing / search and rescue) and message keys (command / situation / reporting / heartbeat / synchronization). After receiving the matrix, the resource allocation unit saves it as a versioned entry and marks it with a task status stamp. Subsequent steps, S201 and later, can then perform weighted matching on the same time slice when reading the network connectivity matrix, avoiding fragmented idle runs where there is a resource requirement but no connectivity support. Considering that synchronization frames can be relinquished as data time slots when there is no synchronization requirement, this embodiment configures a reclaimable proportion for the synchronization area in the matrix, which can be briefly supplemented to high-priority event streams during state transitions.

[0048] In terms of effectiveness, this embodiment solves three specific problems. First, traditional static time slot planning is difficult to adjust with task evolution. This embodiment uses a state machine and deduction to pre-quantify the message structure changes driven by task events. The time slot resource matrix has built-in priority and retransmission budget, avoiding passive congestion on the spot. Second, reliability fluctuations caused by electromagnetic drift at sea are addressed by introducing link quality and retransmission limits into the mapping table, reflecting the natural SNR-packet loss-time slot overhead relationship in the quota. Resource scheduling no longer relies on arbitrary thresholds. Third, cross-domain and intra-group hierarchical communication coexist. The matrix distinguishes control and workflow with hierarchical keys, avoiding mutual compression between high-level broadcasting and close-range tracking, and facilitating subsequent matching operations with layer-by-layer weighting.

[0049] To help readers connect the abstract process with actual operations, two short segments are presented. Segment 1: In the early morning, with poor visibility, the sensing node triggers two suspected targets in the southeast sector. The state machine transitions from S_cov to S_trk. The quota for tracking reporting and control confirmation in the matrix is ​​increased, while the gateway broadcast is reduced to the lower limit. The resource allocation unit records this version and waits for the connectivity matrix to match. Segment 2: The salvage vessel successfully approaches, transitioning to S_med. The confirmation ratio between the search and rescue node and the gateway further increases. The group's situational awareness broadcast is maintained, but a portion of the short burst stream supporting salvage status reporting is periodically shifted. In both segments, the resource matrix is ​​written to the resource allocation unit in chronological order, preparing to jointly decide with the time slot occupancy table and connectivity matrix after S102, completing the closed loop from "task - requirement - time slot".

[0050] Step S102: Divide the time frame into a time synchronization area and a data interaction area, write the time frame structure into the control unit of each surface unmanned node, read the buffer queue length of the adjacent search and rescue node, generate a time slot occupancy table reflecting the communication status of the unmanned cluster, calculate the network connectivity matrix between surface nodes according to the time slot occupancy table, perform matching operation between the network connectivity matrix and the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario, and write the initial time slot allocation scheme into the resource scheduling unit;

[0051] This embodiment focuses on the chain of "time frame structure → status acquisition → occupancy table → connectivity matrix → matching with resource matrix → initial allocation scheme" in S102, with the scenario limited to maritime search and rescue formation: the gateway control node is responsible for cross-domain backhaul, the surface sensing node undertakes zone search and situation broadcasting, and the execution search and rescue node frequently reports and triggers events during close maneuvers. In the previous step S101, a time slot resource matrix with priority and retransmission budget has been generated. In S102, this matrix is ​​implemented into concrete time frames and an executable allocation scheme.

[0052] This embodiment first constructs a time frame structure table, with fields including synchronization frame length, data frame length, and protection interval. Time is divided into time frames with fixed periods, each frame consisting of a time synchronization zone (which can be yielded when there is no synchronization requirement) and a data interaction zone. Each data time slot is further subdivided into four segments: data transmission, data propagation protection interval, acknowledgment transmission, and acknowledgment propagation protection interval. To unify the behavior of multiple nodes, the time frame structure table and the frame parameter table (frame format, number of time slots, time slot length) are distributed to the control units of each surface unmanned node, enabling nodes to perform distributed selection and avoidance under a unified time base. Considering the time-varying nature of electromagnetic propagation at sea, the yieldable proportion reserved in the synchronization zone is marked, and resource scheduling can be temporarily incorporated into the data interaction zone during emergency events.

[0053] With a unified time base established, this embodiment begins collecting neighborhood load. Each node, following a backpack frame header, listens for data packets from neighboring search and rescue nodes, reads the `buffer_size` field and the `occupy_next` field, and maintains a corresponding neighbor status table (node ​​address, queue length, and Time-to-Live (TTL)). The update process follows natural propagation and activity patterns: if a neighbor had a packet in the previous frame, its TTL is reset to its initial value; if no packet is seen for several consecutive frames, its TTL is decremented, and when it reaches 0, it is considered out of the domain or temporarily inactive and removed from the table. This results in a buffer queue length that accumulates over a two-hop range, providing a benchmark for subsequent fairness and on-demand processing.

[0054] Based on the above monitoring results, this embodiment generates a time slot occupancy table. The occupancy table records the status by time slot index: idle, occupied, busy, and adds two key supplementary fields—OCPNXT and HOPCNT. OCPNXT indicates whether the next frame will continue to be unavailable, and HOPCNT records the hop count from which the status originates to the local node. In conflict determination, the "closer priority" principle is adopted: if the previous frame records that two hop node B has declared that a time slot is reserved, and the current frame receives actual data from another hop node in the same time slot, then the time slot is marked as occupied by C and HOPCNT is reset to 1, effectively resolving cross-hop reservation conflicts. If the same time slot is detected to be reserved by two or more nodes at the same time, the status is marked as busy to prevent reselection and collision. Since the confirmation short frame and the data frame are closed in the same time slot, this embodiment treats the confirmation segment as the implicit occupancy of the time slot and does not list the time slot separately, but marks the retransmission probability in the state machine to facilitate consideration of additional costs during subsequent quota matching.

[0055] The process of calculating the network connectivity matrix is ​​not limited to physical adjacency. This embodiment extracts the activity and conflict levels of visible links from the time slot occupancy table: if a pair of nodes has a stable, conflict-free receive-acknowledge sequence in the most recent W frames, then that edge is assigned "strong connectivity" in the connectivity matrix; if it is visible but has been marked as busy for a long time, it is marked as "weak connectivity"; if the TTL has reached zero or there are only conflicting reservation traces across two hops, then it is temporarily set to zero. The connectivity matrix metric reflects three natural constraints: nearest neighbor prior (nearest neighbors are better than distant neighbors), conflict penalty (the more busy it is, the lower the effective connectivity), and acknowledgment integrity (lacking acknowledgments is considered unreliable). To avoid falling into an overly pessimistic graph when sea conditions deteriorate, this embodiment allows weak connectivity to accept small control-type quotas in boundary scenarios, used as exploratory paths for extended routes.

[0056] Matching the connectivity matrix and the time slot resource matrix is ​​the core of S102. This embodiment constructs a matching strategy table containing three types of fields: resource requirements, connectivity status, and allocation weights. The weights reflect service priority (from S101), link strength (given by the connectivity matrix), and conflict cost (statistically calculated from the occupancy table). The matching operation follows a hierarchical weighted approach: control frames and upper-layer protocol control frames are preferentially satisfied on strongly connected edges; general data flows can overflow to weakly connected edges when strong connectivity is insufficient, but the length of consecutive time slots is reduced to lower the risk of collisions; reclaimable synchronization zones are injected proportionally into high-priority flows under event conditions. To define the computational relationship, this embodiment provides a weighted expression for the total weight: W(i,j,c)=α·P(c)+β·C(i,j)-γ·K(i,j), where P(c) is the priority of service category c, representing the urgency of this category of messages on the task side; C(i,j) is the connectivity strength between nodes i and j, reflecting the degree of reliable interaction within the past window; K(i,j) is the conflict cost, derived from the busy count of the i-j related time slots; α, β, and γ are weighting coefficients built into the system, used to weigh service urgency, link reliability, and collision risk under different sea states and task stages. The dimensions and physical meanings in this formula are derived from the objective attributes of the task and the link, without introducing assumptions that violate the laws of natural propagation.

[0057] Based on the aforementioned weights, this embodiment generates an initial time slot allocation scheme suitable for search and rescue scenarios in each time frame. The generation process consists of two steps: First, high-priority classes (upper-layer control, commands, tracking reports) are subjected to "integer binning" on strongly connected pairs, prioritizing currently idle time slots with OCPNXT=0; then, general data and heartbeats, as well as synchronization, are loaded with residual resources, time slots marked as busy are skipped directly, and time slots occupied but originating from two hops are not allocated if there are closer alternatives. If resources are scarce, this embodiment scales the number of time slots per node according to the length of the neighbor queue to avoid saturation and congestion of the neighborhood by a single node. After scaling, the necessary confirmation budget is converted into the reservation of adjacent available time slots to avoid overlap between confirmation segments and other reservations.

[0058] After the initial plan is generated, it is written into the resource scheduling unit, along with a version number and timestamp, for subsequent dynamic adjustments. To illustrate its actual behavior, two short examples are given: First, in the southeast partition, multiple targets suddenly appear, causing the queue length of the sensing nodes to rise rapidly. The occupancy table shows increased busy activity in several low-numbered time slots. The connectivity matrix assigns a high C value to the sensing-gateway edge, and the matching layer migrates tracking and reporting to mid-to-high-numbered idle time slots and borrows part of the synchronization area. Second, when the search and rescue nodes approach the wounded, more frequent confirmations are needed in a short time. The initial plan continuously arranges several short time slot pairs on the strong connectivity edge. Each pair contains a data and confirmation loop, ensuring reliable feedback during rapid maneuvers on the close-range water surface.

[0059] From the perspective of technical issues and effects, this embodiment solves three problems: First, it projects the abstract task resource requirements onto time frames and connectivity graphs, eliminating the gap of "having quotas but no capacity"; second, it uses multi-source marking and hop count adjudication of the occupancy table to handle reservation conflicts, reducing collisions in a distributed environment; and third, it considers priority, connectivity strength and conflict cost in parallel during matching, so that the initial solution not only meets the urgency of search and rescue operations but also does not violate the objective constraints of maritime wireless propagation, leaving an operable margin for subsequent dynamic adjustments based on queue status.

[0060] Step S103: Write control information into the data frame header in a backpack structure, generate a frame configuration table based on the electromagnetic propagation characteristics at sea, use the frame configuration table for data frame processing, read the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, establish an unmanned node queue status table based on the network connectivity matrix, calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table, correct the allocation scheme according to the dynamic adjustment coefficient, write the corrected allocation scheme into the time slot resource scheduling unit, and generate a time slot resource scheduling strategy that supports unmanned cluster maritime maneuvering.

[0061] This embodiment focuses on S103, which "carries" network-side control information into the frame header, enabling control and services to be propagated simultaneously. This allows for frame processing, distributed time slot selection, and dynamic correction based on queue status within surface search and rescue formations operating under varying sea conditions and link fluctuations. This assumes that the initial time slot allocation scheme and connectivity matrix have been obtained in S102, and that each node locally maintains a time slot occupancy table and a neighbor status table. The goal is to transform the initial scheme into a scheduling strategy that can be executed in each frame, maintaining stable and on-demand resource allocation under disturbances such as maneuvers, network entry, and link fading.

[0062] This embodiment first defines a dedicated frame header table in the data processing unit. `Type` is used to distinguish group types, `occupy_next` is used for cross-frame reservation and release, and `buffer_size` carries the total length of the node's pending queue. Reserved bits are aligned according to the protocol. Back-to-back writing of control information follows two constraints: first, it must not change the upper-layer service fragmentation boundary; second, the frame header length is constant so that neighboring nodes can resolve with a fixed offset. Considering multipath propagation and non-line-of-sight reflections at sea, this embodiment generates a framing configuration table based on wireless link statistics under a combination of ship height, sea state, and carrier frequency. This table specifies the ratio range of synchronization frames, data frames, and protection intervals within a single frame. The propagation protection required for the acknowledgment segment is reserved according to the upper limit of link delay jitter. When there is no synchronization requirement, the configuration table indicates the proportion of the synchronization zone that can be relinquished, serving as a flexible resource pool for contingencies. The framing configuration table is distributed to each node, and subsequent framing processing uses it to frame upper-layer messages and arrange acknowledgment timing, ensuring that acknowledgments return in a closed loop within the same time slot, reducing cross-slot interference.

[0063] In the distributed time slot selection phase, this embodiment reads the idle / occupied / busy flags of the local time slot occupancy table, as well as the OCPNXT and HOPCNT side markers, and performs a secondary decision based on the initial allocation scheme issued in S102. The logical path is as follows: first, busy time slots and time slots with OCPNXT=1 are filtered out to avoid conflicts and cross-frame reservations; then, for occupied time slots with two-hop origins, they are prioritized when a single-hop alternative to idle selection exists in the current frame, following the principle of proximity priority; finally, the target number of transmission time slots are filled according to the priority sequence of service categories. For event-type reporting, if the initial scheme allocation is insufficient and the configuration table allows for the use of synchronization areas, short time slots are added within the allowed proportion, with the length depending on the total overhead of the event frame and acknowledgments. Distributed selection does not rely on central coordination but achieves "visible fairness" through local listening and protocol control bits, with nodes converging due to consensus on the same occupancy table and initial scheme.

[0064] To ensure that dynamic adjustments are based on objective evidence, this embodiment establishes a queue state table for unmanned nodes based on the network connectivity matrix, and counts the buffer_size, TTL, and successful / failed transmission counts of the last T frames for one-hop neighbors. The state table summarizes information in three directions according to node identifier: communication demand intensity (queue length and its growth rate), carrying potential (connectivity strength and acknowledgment integrity), and local congestion risk (busy ratio of the time slots associated with the node). The inherent relationship between these indicators is natural: a longer queue and a faster growth rate indicate a high demand for short-term transmission; stronger connectivity indicates that it is easier to convert allocated time slots into effective throughput under the current topology and sea conditions; a high busy ratio indicates a high risk of conflict within the same neighborhood, requiring suppression of occupancy expansion. To map the above relationships to the resource layer, this embodiment calculates the dynamic adjustment coefficient δ for time slot occupancy of each node, using a set of weighted normalized constructions: δ = f(ρd, ρc, ρk), where ρd represents the demand intensity dimension, such as the normalized amount of the node's buffer_size and its time difference; ρc represents the connectivity dimension, such as the weighted average of the edge weights of the connectivity matrix; and ρk represents the conflict cost dimension, such as the busy percentage in the occupancy table statistics. The function f monotonically increases with ρd and ρc and decreases with ρk, ensuring that nodes with "demand and reachability" are appropriately expanded, while "high-conflict" neighborhoods are appropriately contracted. The physical meaning of the parameters is clear: ρd characterizes business pressure, ρc characterizes feasibility, and ρk characterizes the risk of shared media, without involving arbitrary factors detached from the scenario.

[0065] Based on δ, this embodiment modifies the initial allocation scheme. The modification is carried out in two layers: within a node, the time slot share within the category is redistributed by multiplying the priority sequence of the service category by the node's own δ, ensuring the basic foundation for upper-layer protocol control frames and critical tracking reporting; within the neighborhood, the total time slot budget is relatively scaled, with the principle of not exceeding the maximum load and acknowledgment protection lower bound of the frame allocation table. If there are multiple high-δ nodes in the neighborhood, proportional splitting is adopted and a small number of balanced time slots are reserved to prevent starvation; if a node's δ remains low for several consecutive frames and its TTL approaches zero, its cross-frame reservation is released, shortening the OCPNXT retention time and enabling rapid resource return. The modified output is still expressed as a specific time slot index and written to the time slot resource scheduling unit with a version stamp for direct execution in the next frame.

[0066] To test the mechanism's behavior under maneuver and emergency conditions, this embodiment presents two scenarios closely related to rescue operations. Scenario 1: A sensing node detects a new target in the southeast sector. Within a short time, the buffer_size jumps. The connectivity matrix shows a stable strong edge with the gateway, with ρd and ρc rising synchronously, and δ increasing. After correction, a time slot is added to the idle slot numbered in the middle, and part of the synchronization area is repurposed. Since the co-domain busy ratio does not increase, ρk remains constant, and the expansion can be converted into effective reporting. Scenario 2: A salvage boat approaches a person who has fallen into the water at high speed. Vehicle obstruction and wave surface cause occasional confirmation failures within the time slot, resulting in a short-term decrease in connectivity, a drop in ρc, and suppression of δ. The scheduler splits the long string of time slots into multiple short slots and aligns them with the confirmations to reduce the duration of collisions. The time slots are then refilled after connectivity is restored.

[0067] This embodiment addresses three key issues. First, by using a backpack frame header to propagate queue length and reservation intent within the actual service packet, it avoids additional control overhead and ensures consistency between perception and scheduling. Second, the frame configuration table, combined with the latency and jitter boundaries of maritime electromagnetic propagation, confirms that timing and protection intervals are no longer set based on experience, reducing cross-slot acknowledgment failures. Third, by using a queue status table and dynamic coefficient δ to form a three-dimensional feedback mechanism oriented towards "demand-availability-conflict," the revised scheme suppresses local collisions while ensuring the completion of critical flows, maintaining the communication resilience of the rescue formation during maneuver. The final strategy written into the time-slot resource scheduling unit carries a timestamp and status tag, facilitating subsequent adjustments based on version backtracking and window statistics without disrupting the established distributed consensus.

[0068] As described above, the unmanned cluster collaborative self-organizing network time slot resource allocation method provided in this application can achieve effective mapping of requirements through an innovatively designed task configuration system, using state machines and resource matrices. It constructs a time slot allocation mechanism, combining connectivity analysis and resource matching to establish a reliable scheduling scheme. Dynamic optimization is introduced, ensuring the adaptability of the allocation through state monitoring and policy adjustment. This method effectively solves the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, providing technical support for unmanned cluster collaboration.

[0069] In one embodiment of the unmanned cluster cooperative self-organizing network time slot resource allocation method of this application, it may further include the following:

[0070] Step S201: Construct a cluster organization table containing control hierarchy, command relationship, and operation area; read the cross-domain cluster node identifier and map it as a gateway control node; read the operation group node identifier and map it as a surface sensing node; read the unmanned platform node identifier and map it as an execution search and rescue node; and write the cluster organization table into the node management unit.

[0071] Step S202: Construct a task planning table containing task attributes, message types, and communication cycles; divide node communication levels according to the cluster organization table; generate a task configuration matrix containing node identifiers, level attributes, and communication requirements; and write the task configuration matrix into the task management unit.

[0072] This embodiment focuses on steps S201-S202, aiming to transform the loose node list into two core data structures that can be consumed by the task-driven allocator: a cluster organization table and a task configuration matrix. The scenario continues with maritime search and rescue formation: cross-domain cluster nodes handle satellite / 5G backhaul and cross-domain command; operational group nodes handle air-sea perception and zone relay; and unmanned platform nodes are responsible for close-range search, approach, and salvage. The higher-level resource allocation unit will then read the hierarchy, command relationships, and message payloads from these structures to form an initial scheme that matches the time slots and connectivity graph.

[0073] In this embodiment, in step S201, basic node metadata is first collected, derived from reports submitted upon network entry and link testing. Fields include hardware platform, wireless standard, carrier frequency and bandwidth, antenna height, power margin, sensor / payload type, location, and operational sector. A cluster organization table is constructed, containing control hierarchy, command relationships, and operational areas. The table header is divided into three sets of keys: hierarchy key (group leader / team leader / team member), relationship key (uplink command object, downlink jurisdiction, redundant takeover person), and spatial key (geographic polygon or grid index). The mapping rules are not statically specified by name, but rather introduce capability thresholds and responsibility matching: nodes with cross-domain links and stability exceeding the threshold are mapped as gateway control nodes; nodes with multi-source sensing load and relay power margin are mapped as surface sensing nodes; the remaining nodes primarily for operational functions are assigned to search and rescue execution nodes. To avoid single points of failure, command relationships allow one-to-many and redundant connections. Each team leader in the organization table is configured with a candidate team leader field, and the version and timestamp are recorded when writing to the node management unit for easy retrospective reconstruction.

[0074] There's an easily overlooked detail in the mapping: the operational area and command relationship need to be coupled. Maritime search and rescue is typically divided by sectors. If the effective coverage of a sensing node is inconsistent with the responsible sector of its superior group leader, it can easily lead to cross-boundary situational awareness broadcasts. This embodiment performs consistency checks on the spatial keys when generating the organization table. If a cross-boundary is detected, the intra-group boundary is adjusted first, or the command relationship is reconnected. For dynamically added nodes, the read mapping process executed by S201 adds a "cold start listening" stage: the node first listens to only one frame, collects the group's internal beats and synchronization, and only writes it to the organization table after confirming reachability, avoiding the introduction of jittery upper and lower level bindings in topology critical states.

[0075] After solidifying the organizational structure, S202 moves on to generating the task planning table and task configuration matrix. The task planning table faces the business layer, and its fields include task attributes (coverage / tracking / transfer / synchronization, etc.), message type (cross-domain broadcast, intra-group status, status reporting, heartbeat, synchronization), communication period and its upper and lower limits, message length statistics, reliability level, and latency tolerance. For example, the group leader broadcasts status to group members at 0.5 Hz with overhead linearly increasing according to the number of group members, status reporting is at 1 Hz, and heartbeats and synchronization are broadcast with short frame periods. After the planning table is established, this embodiment divides the nodes into communication levels according to the hierarchical relationship in the cluster organization table, and further considers the actual impact of differences in group size, routing hop count, and link quality on the period and reliability: for branches with large intra-group size and high hop count, even if the nominal period on the task side is the same, time slot redundancy corresponding to the retransmission probability needs to be reserved in the matrix.

[0076] The construction of the task configuration matrix is ​​crucial in S202. It compresses the three-dimensional elements of "node × message type × communication cycle / reliability" into a calculable communication requirement profile. The matrix rows are indexed by node identifiers, and the columns are expanded by message type. Each cell records four items: expected message rate, average bytes per frame, reliability level, and priority label. Auxiliary columns record route hop estimation and acknowledgment budget. To enable subsequent matching and use of the matrix, this embodiment externalizes "hierarchical attributes" into the matrix's metadata, allowing resource scheduling to allocate quotas by layer. Furthermore, the matrix is ​​also attached with task status labels, specifying the cell switching rules in states such as coverage, tracking, and transit, facilitating subsequent state machine readings based on stage transitions.

[0077] For determining the matrix values, this embodiment employs task-link joint reasoning. First, the nominal period is given by the task planning. Then, based on the spatial key and historical routes in the organization table, the average hop count and link reliability for each type of packet are estimated, and the expected overhead for acknowledgments and retransmissions is calculated. To provide a clear dimensional conversion, a calculation relationship is listed: S_req = r_msg·L_msg / τ_slot + p_rt·Δ_ack, where S_req represents the nominal time slot requirement for the packet per unit time frame, r_msg is the message rate for this packet type, L_msg is the average number of bytes, τ_slot is the number of bytes that a single time slot can carry, p_rt is the expected number of retransmission triggers, and Δ_ack is the additional overhead of acknowledgments and protection on the time slot. r_msg and L_msg come from the task planning table, τ_slot comes from the framing parameters, p_rt is given by a combination of connectivity quality and hop count estimation, and Δ_ack depends on the acknowledgment embedding timing and the protection interval. Each parameter in this relation has a physical meaning and follows the constraints of maritime wireless propagation and MAC confirmation mechanisms.

[0078] To prevent the matrix from deviating from the actual topology, this embodiment projects redundant command relationships in the organization table as resource constraints: once the main group leader fails and the alternate group leader switches, the "group status" column of the members to which the group belongs is automatically redirected to the new superior, the cycle remains unchanged, but the hop count and confirmation budget are recalculated according to the new path. In the highly variable scenario of disaster relief, this method of reflecting command switching in the communication budget can reduce jitter in the subsequent time slot allocation stage. Another detail is the handling of synchronization and heartbeat. This embodiment sets a transferable flag and upper limit ratio for the "synchronization" column in the matrix, which can be borrowed by resource scheduling in emergency events.

[0079] Two short examples illustrate how the matrix reflects task differences. First, during the day, with good visibility and coverage as the primary focus, the "Group Status" column for surface sensing nodes increases linearly with group size, while search and rescue nodes only retain 1 Hz status reporting. The matrix priority label is more biased towards upper-level control and status broadcasting. Second, at night, with a sudden fall-over signal, status tracking is initiated. Search and rescue nodes activate high priority in the "Event Reporting" column, increasing the message rate. The matrix correspondingly increases the Δ_ack for confirmation budget to mitigate nighttime link fluctuations. The matrix retains the minimum overhead for heartbeats and synchronization in both states, but marks the transferable proportion of synchronization in the event state.

[0080] From a technical and effectiveness perspective, this embodiment integrates "who commands whom," "where to operate," and "what messages to send and at what pace," avoiding the drawbacks of traditional approaches where organizational relationships and communication profiles are disconnected. By coupling message cycles and link reliability into quantifiable time slot requirements, subsequent matching and scheduling phases no longer rely on empirical coefficients. Through the explicit implementation of redundant command and transferable synchronization at the matrix level, in the face of node failures or sudden state changes, the task-to-resource mapping does not need to be rebuilt; it only needs to switch to an adjacent version, reducing downtime. Finally, the cluster organization table and task configuration matrix are written into the node management unit and task management unit, respectively, and solidified with version numbers, timestamps, and status tags, awaiting resource allocation and time slot scheduling units to complete the task-to-resource mapping in S102-S103.

[0081] In one embodiment of the unmanned cluster cooperative self-organizing network time slot resource allocation method of this application, it may further include the following:

[0082] Step S301: Construct a state machine configuration table containing state transition conditions, task triggering conditions, and simulation parameters; generate a task state machine based on the task configuration matrix; perform Monte Carlo simulations on search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the state machine; generate a task requirement prediction table; and write the task requirement prediction table into the simulation engine.

[0083] Step S302: Construct a resource mapping table containing communication performance, data capacity, and quality indicators; calculate communication resource requirements based on the task requirement prediction table; generate a time slot resource matrix; and write the time slot resource matrix into the resource allocation unit according to priority.

[0084] This embodiment focuses on steps S301-S302, aiming to project the state evolution on the task side into the communication resource side in a computable manner, forming an executable input at the time slot level. Given that steps S201-S202 provide the task configuration matrix (node ​​identifier, hierarchical attributes, message type, and periodic requirements), steps S102-S103 will match and schedule the time slot resource matrix output from this step. The maritime search and rescue environment is unstable and event-driven, and state transitions are not linear processes. Therefore, it is necessary to use state machines and Monte Carlo simulations to depict multiple possible scenario paths to avoid resource mismatches caused by a single-path assumption.

[0085] In this embodiment, a state machine configuration table is first constructed in S301, containing three core fields: state transition conditions, task triggering conditions, and inference parameters. The state set includes search and rescue area coverage (S_cov), target search and tracking (S_trk), casualty transfer and salvage (S_med), and auxiliary states such as idle and convergence. The state transition conditions reflect natural causality, such as "if the confidence of a sensing node in a certain sector exceeds a threshold and remains stable within a preset number of consecutive windows, then S_cov → S_trk"; "if the distance between the execution search and rescue node and the target is less than the safe radius and authorized by the superior, then S_trk → S_med". The task triggering conditions are driven by upper-level command or external events, such as convergence orders issued by the cross-domain gateway, re-partitioning search, and temporary no-navigation zone updates. The inference parameters cover sea state level (affecting channel jitter and movement speed), node density, target occurrence rate, link SNR statistics, node entry / exit probability, etc. All parameters are consistent with the physical processes in the scenario and do not introduce assumptions that contradict the laws of propagation or movement.

[0086] Based on this configuration table, this embodiment generates a task state machine according to the task configuration matrix. Each state of the state machine is bound to a set of communication profile pointers, pointing to the message period, length, priority, and reliability level of the corresponding state in the matrix. Then, a Monte Carlo simulation is performed: using time axis discrete frames, a large number of sample paths are repeatedly simulated. In each path, target appearance and movement, link quality fluctuations, neighbor topology changes, and command triggering times are randomly sampled. State transitions are driven by state transition conditions, and the bound communication profile is read on each frame and converted into a "demand slice" for that frame. To make the simulation more realistic, this embodiment embeds the linkage between routing hop count and acknowledgment cost in the samples: sparse topology or deteriorating sea conditions increase the average hop count and packet loss, thereby increasing the acknowledgment and retransmission budget; conversely, in dense grouping and stable sea conditions, the demand slice tends towards the nominal value. After accumulating multiple sample paths, a task demand prediction table is obtained, with fields including time slice, state label, message rate distribution for each message type, average length, expected retransmission count, and priority range. This table is written into the simulation engine as direct input for subsequent resource mapping.

[0087] Proceeding to S302, this embodiment constructs a resource mapping table to establish a correspondence between communication performance, data capacity, quality indicators, and time frame parameters. Communication performance includes latency tolerance, reliability level, and acknowledgment timing model; data capacity includes the length statistics (mean / peak) and arrival burst rate of each message; quality indicators include the maximum allowed retransmission count and acceptable jitter. Time frame parameters are uniformly provided by the system (frame length, time slot length, guard interval, and acknowledgment segment ratio). The core of the mapping process is to convert "the service-side requirements of each type of message in each state" into "the time slot requirements per unit time frame". This embodiment adopts a fractional combination: the nominal bearer is determined by the message rate and length, the acknowledgment and retransmission overhead is determined by the link quality and hop count estimation, and the guard interval depends on the upper bound of the propagation latency jitter at sea. For ease of review, this embodiment describes the transformation using a relational formula: S_req(u,c,t)=r(u,c,t)·L(u,c) / τ_slot + E_rt(u,c,t)·Δ_ack, where S_req represents the unit time-slot requirement of node u for class c messages at time t; r(u,c,t) is the derived message rate; L(u,c) is the average number of bytes for this type of message; τ_slot is the number of bytes that a single time slot can carry (determined by the time slot length and modulation coding); E_rt(u,c,t) is the expected number of retransmissions at time t (derived from connectivity quality, hop count, and SNR statistics); and Δ_ack is the equivalent time-slot overhead for a single acknowledgment and protection. The physical meaning of each parameter is consistent throughout, satisfying the natural relationship that "the greater the load, the worse the link, the more stringent the acknowledgment, and the more resources are consumed."

[0088] To ensure the time slot resource matrix reflects both demand intensity and priority / transferability, this embodiment performs hierarchical aggregation of S_req by level and category. The first layer consists of control frames and upper-layer protocol control frames, supporting multi-hop routing and command and control, and is marked as non-degradable quotas. The second layer comprises critical task flows (tracking and reporting, retrieval process confirmation), which can utilize synchronization zones in emergency situations. The third layer consists of general data and situation broadcasting, with scalable labels. The resource mapping table provides the priority sequence and degradation strategy for each layer. For example, when the total S_req exceeds the available time slots for the current frame, the continuous occupancy of the third layer is reduced first to maintain the integrity of the confirmation loop. After aggregation, a time slot resource matrix is ​​obtained. Rows are indexed by nodes or levels, and columns are expanded by categorized time slot pools (control pool, critical pool, general pool, transferable synchronization pool). Cells record quotas and fluctuation limits, and include status labels for S102 matching.

[0089] It should be noted that the coupling of inference and mapping avoids the distortion problem of "static period → rigid quota". Two scenarios illustrate its underlying logic. In the early morning, with low wind and sparse targets, the S_cov dwell time is long, the message rate is low, and E_rt is close to zero. Matrix allocation is mainly focused on situation broadcasting and basic heartbeats, with a low proportion in the critical pool. In the evening, with increased wind and thermal noise, S_trk is frequently triggered, E_rt shifts upward, and the matrix automatically increases the acknowledgment budget in the critical pool. Some synchronization transferable flags are activated, while the control pool remains unchanged. Both scenarios follow the natural chain of wireless link quality—retransmission budget—time slot occupancy, avoiding allocations that contradict physical conditions.

[0090] From an engineering perspective, this embodiment explicitly defines task triggering and state transitions through a state machine configuration table, utilizes Monte Carlo simulation to cover multiple feasible paths, and outputs a task requirement prediction table with temporal and uncertainty semantics. A resource mapping table unifies performance, capacity, and quality indicators into time slot dimensions, forming a time slot resource matrix with priority and degradation strategies. This eliminates the need for experience-based matching and distributed selection, instead implementing them in a "demand-availability-risk" order. Even when encountering node network entry or link degradation, the matrix can switch to an adjacent state version, reducing the cost of replanning. Finally, the time slot resource matrix is ​​written to the resource allocation unit in priority order, recording the version and state stamp, providing a solid upstream basis for S102 matching and S103 dynamic correction.

[0091] In one embodiment of the unmanned cluster cooperative self-organizing network time slot resource allocation method of this application, it may further include the following:

[0092] Step S401: Construct a time frame structure table containing synchronization frame length, data frame length, and protection interval; divide the time frame into a time synchronization area and a data interaction area according to a fixed interval; generate a sub-frame parameter table containing frame format, number of time slots, and time slot length; and write the sub-frame parameter table into the control unit of each unmanned surface node.

[0093] Step S402: Construct a neighbor status table containing node address, queue length, and lifetime; read the cache queue length information of neighboring search and rescue nodes; generate a time slot occupancy table containing time slot status, reservation information, and hop count information; and write the time slot occupancy table into the status management unit.

[0094] This embodiment focuses on the framing and neighborhood status acquisition of maritime search and rescue formations under medium- and short-wave / microwave links, specifically steps S401-S402. The goal is to apply a unified physical time base and load observation to each unmanned surface node, ensuring a consistent reference plane for subsequent matching and scheduling under distributed conditions. Steps S201-S202 have already defined the hierarchy and message period, and steps S301-S302 have provided the time slot dimensions for resource mapping. This step encapsulates the time frame structure and neighbor information into two types of basic tables and writes them into the node-side control / state unit.

[0095] In this embodiment, a time frame structure table is first constructed in step S401, with fields including synchronization frame length Ts, data frame length Td, and guard interval Tg. The time axis is divided into continuous time frames at fixed intervals, each frame consisting of a time synchronization area and a data interaction area. Each data time slot within the data interaction area is further subdivided into four segments: data transmission, data propagation protection, acknowledgment transmission, and acknowledgment propagation protection. The guard interval Tg is set with reference to the maximum round-trip time delay and the upper limit of multipath widening of electromagnetic propagation at the sea surface, ensuring that the same-slot acknowledgment can be reliably received after physical layer fallback, without cross-slot leakage. Based on different sea state levels (stable, swell, windy), this embodiment provides suggested combination intervals of Ts:Td:Tg and marks the yieldable proportion of the synchronization area, which is converted into reclaimable data slots to handle sudden tasks under low synchronization load. Subsequently, a frame parameter table is generated, unifying the frame format, the number of time slots Nslot, and the time slot length τslot, ensuring that nodes perform the parsing and reservation release of back-to-back control information on the same time base. To avoid clock drift caused by heterogeneous bandwidth deployment, the framing parameter table includes modulation and coding indicators and payload thresholds, which establish a mapping between τslot and the number of bytes that can be carried. After being written into the control unit of each unmanned surface node, its MAC timer completes frame alignment and slot counting accordingly.

[0096] The distribution of framing parameters does not equate to readiness. In this embodiment, a local self-check is performed at each node: the RF front-end timing deviation is read, and compared with the synchronization broadcast timestamp received in the previous frame. If the deviation exceeds a threshold, the synchronization zone is temporarily extended by several sub-slots to complete re-alignment. This is because changes in sea state or platform maneuvers can cause the local oscillator frequency offset to accumulate, and reserving a self-healing window in advance can reduce subsequent collisions. For gateway control nodes carrying satellite / 5G backhaul, a cross-domain bridging flag is also added to the framing parameter table, indicating that the node needs to reserve some fixed-numbered time slots to connect to the uplink backhaul, avoiding intra-group contention affecting cross-domain commands.

[0097] In step S402, this embodiment constructs a neighbor status table on the node side. Fields include the neighbor node address (MAC), queue length (buffer_size), time-to-live (TTL), and optional last received time and source layer. When each node receives a data frame or upper-layer protocol control frame, it parses the buffer_size and occupy_next (cross-frame reservation intent) from the dedicated frame header, refreshes the neighbor's queue length to its local table, and resets the TTL to a preset initial value. If a neighbor has no packets received for several consecutive frames, this embodiment decays its TTL frame by frame, deleting the entry when it reaches zero to avoid outdated information affecting the local time slot selection probability. The reason for using TTL instead of a simple timestamp is that link interruptions in wide-area ad hoc networks can be caused by wave action or attitude changes. TTL provides a tolerance window for "possibly still present but not yet receiving," preventing excessive contraction of scheduling due to short-term shadow areas.

[0098] Based on the stable updating of neighbor states, this embodiment generates a slot occupancy table. Each node maintains a state table indexed by slot number for the current frame, containing three states: idle, occupied, and busy, and records reservation information OCPNXT and hop count information HOPCNT. Occupancy is defined according to the observation priority principle: if a local or one-hop neighbor successfully transmits data and receives an acknowledgment in a slot, the slot is marked as occupied and HOPCNT=1; if two or more neighbors are found to have declared reservations in the same slot, the slot is marked as busy, and new reservations are prohibited; if only two-hop neighbors declared OCPNXT=1 in the previous frame and actual transmission occurs in the current frame, the two-hop reservation is overridden according to the proximity priority principle, and HOPCNT is reset to 1. OCPNXT records the intention to reserve across frames, which is used to pre-reserve the unavailable set at the beginning of the next frame, reducing the collision probability of newly connected nodes. The rolling update of the occupancy table occurs at the boundary of each frame: first, the state is preset according to the OCPNXT of the previous frame, then it is corrected according to the transmit and receive events within the frame, and finally it is fixed and written to the state management unit at the end of the frame for S102 / S103 to read.

[0099] To ensure the occupancy table reflects the natural constraints of physical propagation and MAC acknowledgment, this embodiment treats acknowledgment frames as "endogenous occupancy" sharing the same slot as data frames, without creating a separate acknowledgment slot; however, an acknowledgment result flag is appended to the status field for subsequent estimation of the slot's reliability. If a peer-to-peer pair exhibits a "data success - acknowledgment missing" pattern across multiple frames, although it is not marked as busy, a "soft collision" count will be generated locally for the matching operator to lower the slot's priority. This approach is based on the fact that maritime reflection paths may cause the effective SNR of the acknowledgment segment to be lower than that of the data segment, which can still be compensated for in the short term through retransmission, but it is not advisable to continue to concentrate critical flows.

[0100] This embodiment further illustrates the logical relationship between the occupancy table and the neighbor status. A neighbor's buffer_size reflects its transmission pressure. If the buffer_size of multiple neighbors in a region increases synchronously, and the busy count for the same numbered slot appears more frequently in the occupancy table, it can be determined that the region is facing a contention amplification effect. Nodes should tend to skip these high-contention slots when making reservations, distributing reservations to medium-to-high numbered intervals to avoid the vicious cycle of "congestion—collision—retransmission—more congestion." Conversely, when TTL generally decreases and busy counts are scarce, it indicates a sparse neighborhood or good link performance. This embodiment allows for allocating more continuous time slots to the node without exceeding the guard interval and acknowledgment loop, improving throughput consistency.

[0101] To illustrate the details of the landing process, two clips are presented. Clip 1: In the morning, the sea conditions are calm. The leader node broadcasts the situation to the team members at a 0.5 Hz rhythm. The nodes read from the frame header that the leader's `occupy_next` is 0, indicating that it does not reserve cross-frame occupancy. Therefore, the occupancy table shows intermittent occupancy stripes in lower-numbered gaps. Neighboring nodes prioritize filling blanks during reservations to avoid crowding out continuous confirmation chains. Clip 2: In the evening, the wind and waves rise. The search and rescue node continuously reports suspected targets, causing the `buffer_size` to jump. To ensure confirmation receipts, it sets `OCPNXT=1` in several consecutive gaps. Neighboring nodes accordingly pre-set these gaps as unavailable, shifting contention pressure to unreserved intervals, and the collision probability decreases naturally.

[0102] From a technical and effectiveness perspective, this embodiment parameterizes the length, structure, and protection boundary of the time frame in S401, resolving the reservation inconsistency problem caused by the lack of a unified time base among multiple nodes. In S402, it solves the factual problem of how to reliably perceive "who is using," "who will use," and "how far away" in a distributed environment through a neighbor status table driven by a backpack frame header and an occupancy table containing OCPNXT / HOPCNT. The combination of the occupancy table and the neighbor status provides a verifiable underlying evidence chain for subsequent connectivity matrix calculation and matching, enabling resource scheduling to converge without relying on central broadcasts, and maintaining reasonable avoidance and carrying capacity during maritime maneuvers, node joining and leaving, and sea state fluctuations. The above two types of tables are written to the node control unit and the status management unit respectively, with version stamps and timestamps, facilitating cross-frame association and backtracking in subsequent steps.

[0103] In one embodiment of the unmanned cluster cooperative self-organizing network time slot resource allocation method of this application, it may further include the following:

[0104] Step S501: Construct a network topology table containing node identifiers, link status, and time slot status; calculate the connectivity between water surface nodes based on the time slot occupancy table; generate a network connectivity matrix; and write the network connectivity matrix into the topology management unit.

[0105] Step S502: Construct a matching strategy table containing resource requirements, connectivity status, and allocation weights; perform a weighted matching operation between the network connectivity matrix and the time slot resource matrix to generate an initial time slot allocation scheme; and write the initial time slot allocation scheme into the resource scheduling unit.

[0106] This embodiment revolves around steps S501-S502, weaving the distributed occupancy data maintained by the nodes and the quota requirements of the task side into an executable initial time slot allocation output. Steps S401-S402 have already issued framing parameters and formed a time slot occupancy table; steps S301-S302 provide a time slot resource matrix layered by status and service category; and step S102 provides a preliminary framework for the matching logic. This step further clarifies the topology reachability and, based on this, completes the closed-loop calculation of weighted matching.

[0107] In this embodiment, a network topology table is first constructed in step S501. The core fields are node identifier, link status, and time slot status. The node identifier comes from the node management unit. The link status is derived from the transmit / receive records, acknowledgment success rate, and RSSI / SNR statistics within the last W frames. The time slot status is directly taken from the local time slot occupancy table, including idle / occupied / busy status and cross-frame reservations OCPNXT and hop count HOPCNT. Two constraints are introduced during construction: first, a complete and successful transmit / receive pair is used as strong evidence, weakening the "visibility" obtained solely from energy detection; second, a soft penalty is set for acknowledgment loss caused by multipath, preventing a single loss from directly reducing the connection to non-connectivity. Subsequently, the connectivity between surface nodes is calculated by using the "intersection of available slots" of each pair of nodes within the window as the basic quantity of carrying capacity, and then reducing it by using the busy rate and OCPNXT coverage as conflict factors. For pairs with only two hop reservations but no actual flow, they are marked as weakly connected to prevent mistakenly treating "heard it will be used in the future" as "currently available". Finally, the target network connectivity matrix is ​​obtained. The element Cij reflects the reachability strength between nodes i and j under the current frame structure and occupancy facts. The matrix with timestamps and window parameters is written into the topology management unit for use in the matching stage and subsequent dynamic adjustment.

[0108] The generation of the connectivity matrix is ​​not a simple topology extraction; it also involves timing judgments consistent with MAC behavior. This embodiment treats consecutive "busy" occurrences on the same numbered time slot as neighborhood preemption hotspots, applying local yield to the Cij values ​​of adjacent edges. For peers with a long-term OCPNXT=1 on a fixed-numbered time slot, they are considered cross-frame reserved channels, with Cij weighted in the neighborhood of that slot, but not propagated to the entire slot domain to avoid erroneous amplification. For example, if a group leader and three group members form a stable "broadcast-confirm" closed loop in a medium-numbered time slot, then the Cij values ​​of these pairs of edges are high in those slots. If another search and rescue node repeatedly makes reservations but collides in the same numbered time slot, then its Cij value with its neighbors decreases in that slot, but does not affect its potential reachability in higher-numbered free slots. This processing makes C both "number-sensitive" and preserves the overall carrying capacity of node pairs.

[0109] Entering S502, this embodiment constructs a matching strategy table with fields including resource requirements (quotas and priorities from the time slot resource matrix), connectivity status (edge ​​strength and hotspot / reservation flags from C), and allocation weights (for multi-factor trade-offs). The matching objective is to place high-priority flows onto highly accessible, low-conflict edges and slot numbers within a limited set of available slots, ensuring the integrity of the control and critical flow confirmation loop while reserving scalable space for general flows. To avoid abstract descriptions, this embodiment defines a total weight value W(i,j,c,s) for each candidate allocation, representing the attractiveness of carrying category c traffic on the time slot numbered s for node pair (i,j). The weight composition considers three things: task-side urgency P(c), topology reachability strength Cij(s), and conflict cost Kij(s).

[0110] The matching operation is performed in the order of "hard constraints first, then weights". The first round is hard constraint filtering: candidates marked as busy or OCPNXT=1 on gap s and reserved by others are eliminated; for categories that need to confirm loop closure, only numbers that can carry confirmation in the same gap are considered. The second round is weight-driven "binding": candidates in descending order of Cij(s) are traversed according to P(c) from high to low, and the quota is split into the smallest available fragments to fill them. During this process, the remaining capacity and conflict accumulation of each edge and each gap are tracked. To avoid local congestion, a "dispersion threshold" is set in the matching strategy table. When the Kij(s) of a certain gap increases too quickly, subsequent binding will be forced to migrate to an empty gap with a far number, even if C is slightly lower, in exchange for a lower collision risk. The third round processes the transferable synchronization pool: in the event state and when the critical flow is over-supplied, the reserved proportion in the synchronization area is allowed to be converted into the critical pool. The rule is to maintain the minimum synchronization requirement of the next frame and not cross the guard interval boundary.

[0111] After generating the initial time slot allocation scheme, this embodiment performs two consistency checks. First, a closed-loop verification: each allocated control / critical flow occupancy point can form a "data-confirmation" path within the same time slot, and this confirmation path does not conflict with other nodes' reservations. Second, a rapid neighborhood fairness check: the percentage of effective slots obtained by each node within a window is statistically analyzed. If this percentage significantly exceeds the neighborhood median and its neighbor queues are generally high, a slight backoff is triggered, transferring a small amount of general flow to a higher-numbered spare slot, leaving room for the secondary correction in S103 based on the queue coefficient. These two checks directly correspond to the engineering requirements of maritime rescue: stable and reliable critical links, and no excessive crowding in the neighborhood.

[0112] To illustrate the adaptability of the matching to different sea states and mission phases, this embodiment presents two segments. Segment 1: During the daytime, in stable sea states, Cij(s) is relatively high across multiple numbering intervals. The resource matrix primarily uses situation broadcasting and heartbeats, and bin packing tends to form a periodic rhythm in low-numbering gaps, while some numbers are fixed for cross-domain gateway backhaul. Segment 2: At night, with high winds and waves and suspected cases of people falling overboard, the critical flow P(c) increases, E_rt increases during the resource mapping period, and W rises in mid-to-high numbering idle gaps. The matching results relocate the critical flow to these gaps and initiate synchronization pool transfer, sacrificing the continuity of general flows to a small extent. This migration follows the natural logic of "worse link → higher retransmission budget → need for more distributed and low-collision bearers."

[0113] In summary, this embodiment uses a network topology table to converge scattered occupancy facts and confirmation evidence into a connectivity matrix C, and expresses link reachability in a numbered and sensitive manner. A matching strategy table synthesizes task priority, connectivity strength, and conflict risk into computable weights to construct the initial time slot allocation scheme. Through closed-loop and fairness checks, reliable delivery of critical messages and sustainable carrying capacity of the neighborhood are guaranteed. The final scheme is written into the resource scheduling unit in the form of a time slot index, node pair, and message category triplet, with a version stamp and status tag, for fine-grained dynamic correction in step S103 in conjunction with the queue status coefficient δ.

[0114] In one embodiment of the unmanned cluster cooperative self-organizing network time slot resource allocation method of this application, it may further include the following:

[0115] Step S601: Construct a dedicated frame header table containing frame type, reservation field, and queue length; write control information into the data frame header in a backpack structure; generate a sub-frame configuration table based on the electromagnetic propagation characteristics at sea; and write the sub-frame configuration table into the data processing unit.

[0116] Step S602: Construct a time slot status table containing idle, occupied, and busy states; read the time slot occupancy table to determine the status; generate a distributed selection matrix containing sending time slots, receiving time slots, and reserved time slots; and write the distributed selection matrix into the time slot scheduling unit.

[0117] This embodiment addresses link fluctuations in maritime search and rescue formations under conditions of maneuver, obstruction, and multipath propagation. It encapsulates control information and operational data into a unified data frame around steps S601-S602, and selects and reserves available time slots for each frame in a distributed manner at the node side. The preceding steps S401-S402 have already established framing parameters and time slot occupancy tables, and S501-S502 have output the initial allocation scheme and connectivity matrix. The current step strings together the "control information carried in the frame header—framing configuration—occupancy awareness—selection matrix" into an executable closed loop.

[0118] In this embodiment, a dedicated frame header table is first constructed in S601. Fields include frame type (Type, distinguishing data frames, upper-layer protocol control frames, and acknowledgment frames), the reservation field `occupy_next` (declaring whether to continue using this time slot number in the next frame), and queue length `buffer_size` (the current total length of the sender's MAC queue). The remaining reserved bits are aligned byte-wise. Control information is written to the data frame header in a back-to-back structure, requiring a fixed frame header length to maintain the upper-layer packet fragmentation boundary, allowing neighboring nodes to resolve with a fixed offset, eliminating the need for an additional control channel. To avoid interference between control bits and service payloads, a CRC checksum is added to the frame header field at the link layer. Data segments maintain independent upper-layer checks, separating their error handling paths and reducing false triggering of retransmissions. Addressing the latency jitter and multipath spread of electromagnetic propagation at sea, this embodiment generates a framing configuration table based on historical SNR, platform altitude, and sea state level. This table provides upper and lower bounds for synchronization frame length, data frame length, acknowledgment segments, and guard intervals, clarifying which sub-slots can be relinquished for data when synchronization is not required. The configuration table is written to the data processing unit, so that the MAC allocates the timing according to the table during encapsulation: data transmission - protection - acknowledgment - protection, ensuring that the acknowledgment can return in a closed loop within the same gap and does not cross the protection boundary.

[0119] The framing configuration table is not a static constant but contains scenario-based keys. This embodiment maintains two configuration profiles in the data processing unit: a stable sea state profile and a severe sea state profile. When the confirmation missing rate or round-trip delay variance within the most recent window exceeds a threshold, it switches to the "expand protection, shrink data" profile; conversely, it reverts to the previous profile. The switching is based on natural propagation principles: deteriorating sea state leads to multipath widening, requiring the protection segment to be lengthened, while the number of bytes carried per slot decreases, which translates to more time slots per unit load at the upper layer; during stable periods, it shrinks in the opposite direction, returning to the nominal capacity. A dedicated frame header table is issued in conjunction with the framing configuration table. When sending packets, nodes write the currently used configuration profile number into a reserved bit, allowing neighbors to interpret and confirm the timing sequence, reducing cross-node misjudgments of clock cycles.

[0120] Entering S602, this embodiment constructs a slot status table on the node side. The status set includes three categories: idle, occupied, and busy, with the index aligned to the system's unified slot number. Status judgment reads the local slot occupancy table and overlays the observations of the current frame: if a slot resolves to two or more neighbors declaring reservations or a collision has occurred, it is marked as busy; if a local or one-hop neighbor successfully transmits and receives with acknowledgment, it is marked as occupied; the rest are idle. Occupancy entries include the source hop count HOPCNT and whether the peer has declared OCPNXT=1 as reserved information for pre-screening in the next frame. Subsequently, a distributed selection matrix is ​​generated. The process is not simply filling the idle slots, but rather combining it with the initial allocation scheme of S502 and the local service queue: first, the target transmit / receive / reservation share for this frame is determined according to the initial scheme; then, the slot status table is filtered for busy slots and slots with OCPNXT=1 from others, prioritizing the selection of the slot set corresponding to a one-hop strongly connected edge; for upper-layer control and critical reporting requiring closed-loop confirmation, slots that can carry acknowledgments in the same slot are forcibly selected. If there is insufficient free space, this embodiment scales the request volume of this node according to the length of the neighbor queue, releasing some reservations to more "hungry" neighbors to avoid triggering a chain of retransmissions in high-contest areas.

[0121] To express the selection logic as a reusable computation, this embodiment calculates the score for each candidate number s locally and fills it in according to the score. The score depends on natural indicators of task urgency, connectivity, and conflict, which can be expressed by a linear combination: Score(s) = α·P_local + β·C_local(s) - γ·K_local(s). Here, P_local is the urgency of the current highest priority queue of this node (mapped by queue length and expiration time), C_local(s) is the reachability strength of number s on a one-hop peer (derived from a local slice of the connectivity matrix), and K_local(s) is the busy / collision history strength of number s in its neighborhood; α, β, and γ are the scheduling bias coefficients of this node, written in the node configuration. A higher P_local indicates a need to empty the critical queue faster, a higher C_local(s) indicates a higher probability of a successful response on the first attempt for that number, and a higher K_local(s) means that uploading will trigger a conflict risk. This linear relationship is consistent with the physical common sense of wireless shared media. The score is only used for local sorting and does not participate in cross-node coordination, avoiding the introduction of centralized dependencies.

[0122] The selection matrix consists of three parts: the transmit time slot set S_tx, the receive time slot set S_rx, and the reserved time slot set S_res. S_tx is selected locally based on the score and quota; S_rx is derived from reverse inference of the initial scheme and neighbor OCPNXT, ensuring a receive window is reserved for the numbers declared by the peer; S_res is used to declare numbers to be used in the next frame, with the rule that control and critical reporting temporarily reserve numbers that have been confirmed to be stable, while ordinary data is not reserved across frames, reducing unnecessary masking. After the matrix is ​​generated, it is written to the time slot scheduling unit, along with the configuration file number and timestamp, so that the MAC timer can trigger transmission and reception as planned in that frame.

[0123] Here are two scenario snippets. First, under calm sea conditions, the sensing node broadcasts at 0.5 Hz, confirming stability. The score is higher in the lower number range, S_tx shows periodic occupation, and S_res is sparse. Neighbors reserve S_rx based on reserved bits, resulting in fewer collisions. Second, with increased waves and a suspected drowning victim, the search and rescue node's critical queue expands, P_local increases, the frame configuration switches to expanded protection mode, C_local decreases in some numbers, K_local increases in lower numbers, the sorting results migrate critical reporting to the mid-to-high number gaps, and OCPNXT=1 is set for several numbers. Upon seeing this declaration, surrounding nodes avoid the area in the next frame, maintaining a stable confirmation loop during the event period.

[0124] From a technical and effectiveness perspective, this embodiment uses a dedicated frame header to naturally propagate queue length and reservation intent within the business flow, avoiding additional control load and ensuring information freshness. A frame configuration table maps the latency jitter of electromagnetic propagation at sea into a time budget for acknowledgment and protection, reducing cross-slot acknowledgment failures. A time-slot status table and a distributed selection matrix merge "visible occupancy facts" and "task-side priorities" locally on the node, forming a decentralized synchronous decision-making process and reducing collisions and idle time caused by rapid topology changes. Finally, the selection matrix is ​​written into the time-slot scheduling unit, working in conjunction with the dynamic adjustment coefficient δ in S103 to support continuous communication of the unmanned swarm during maritime maneuvers.

[0125] In one embodiment of the unmanned cluster cooperative self-organizing network time slot resource allocation method of this application, it may further include the following:

[0126] Step S701: Construct a status monitoring table containing node identifier, queue length, and life cycle; calculate the queue status distribution between nodes based on the network connectivity matrix; generate an unmanned node queue status table; calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table; and write the dynamic adjustment coefficient into the adjustment control unit.

[0127] Step S702: Construct a resource reallocation table containing an initial scheme, adjustment coefficients, and priorities; modify the initial time slot allocation scheme according to the dynamic adjustment coefficients to generate a dynamic time slot allocation scheme; and write the dynamic time slot allocation scheme into the time slot resource scheduling unit.

[0128] This embodiment proposes an implementation path S701-S702 for resource adaptation during the operation of maritime search and rescue formations. It inherits the network connectivity matrix C and initial time slot allocation scheme generated by S501-S502, and combines the neighbor queue length and time slot occupancy facts continuously maintained by each node in S401-S402 to form a three-dimensional feedback of "on-demand-reachability-conflict" to dynamically correct time slot occupancy and ensure that critical services can still be reliably carried out under maneuver, network entry and exit and sea state fluctuations.

[0129] In this embodiment, a status monitoring table is first constructed in step S701. Fields include node identifier (u), queue length (buffer_size, including the mean and increment within the time window), and time-to-live (TTL) (used to determine activity level). Two types of operational evidence are also included: a snapshot of edge strength from the connectivity matrix C, and the busy ratio and cross-frame reservation ratio of the slots associated with the node in the slot occupancy table. The status monitoring table is updated frame-by-frame: when a node receives a neighbor's back-mounted frame header, it refreshes the buffer_size and resets the TTL; if no packets are seen for several consecutive frames, the TTL decays to zero and the node is temporarily removed from the table. After constructing the monitoring table, the queue status distribution among nodes is calculated based on the connectivity matrix. This embodiment employs a one-hop aggregation and two-hop dilution strategy; the former reflects direct competition, while the latter is used to indicate the risk of regional congestion spread. The resulting unmanned node queue status table is compiled, with the core component being the generation of three types of metrics for each node: demand intensity ρd (normalized from queue length and its growth rate), reachability ρc (composed of the weighted average of C's edge weights and confirmation completeness), and conflict pressure ρk (given by the busy ratio and the OCPNXT overlap probability). The inherent relationship between these three metrics conforms to natural laws: the more business accumulates and the more accessible the links, the more meaningful resource expansion becomes; however, higher conflict levels mean that blind expansion will lead to increased retransmissions and be counterproductive.

[0130] Based on the above three indicators, this embodiment calculates the dynamic adjustment coefficient δ(u) for time slot occupancy. Considering the non-stationarity of sea state and topology, δ needs to be insensitive to short-term shocks and responsive to sustained pressure. This embodiment introduces time smoothing and threshold constraints in the calculation, taking a monotonic increasing relationship with ρd and ρc, and a monotonic decreasing relationship with ρk. To facilitate integration with different load scales, δ is normalized to the [0,1] interval, with a larger value indicating that the node's occupancy share in the next frame should be increased. To clarify the dimensional relationship, a calculation formula is given for explanation: δ(u)=σ{α·ρd(u)+β·ρc(u)-γ·ρk(u)}, where σ is the truncation-normalization function, α, β, and γ are system trade-off coefficients; ρd(u) represents the transmission demand intensity of the node within the monitoring window; ρc(u) represents the possibility of the node converting the quota into effective throughput under the current topology; and ρk(u) represents the conflict risk in the neighborhood of the node. Each term in the formula can be directly calculated from the monitoring table and the connectivity matrix, without relying on unobservables, and is consistent with the physical intuition of "load-reachability-conflict". The calculated δ and node identifier are written to the adjustment control unit, and a timestamp and status label are recorded for subsequent reallocation.

[0131] In S702, this embodiment constructs a resource redistribution table based on the three-element information of "initial scheme + adjustment coefficient + priority". The initial scheme provides the packing results of specific slot numbers, node pairs and message categories; the adjustment coefficient comes from S701 and reflects the current expansion and contraction needs of each node; the priority is inherited from the time slot resource matrix and marks the rigidity and transferable boundaries of different categories. The first step of redistribution is the re-segmentation of quotas within nodes: for each node, its quotas for each category are scaled by δ(u), high δ nodes maintain or slightly expand key categories (control, tracking and reporting, transfer confirmation), low δ nodes compress general categories (situation broadcasting, non-urgent data), but do not break the minimum guarantee line of key categories. The second step is neighborhood conflict resolution: if a slot with a certain number experiences a rapid accumulation of K in its neighborhood (a sharp increase in busy count), the allocation of general categories falling on that slot is migrated, preferentially moved to idle slots with a greater number distance and the second highest C value, and key categories are retained in their original positions to form a confirmation loop. The third step is cross-node balancing: when multiple adjacent nodes have high δ values ​​and insufficient available gaps, this embodiment proportionally divides the available synchronization pool into the general pool to ensure that there is no long-term starvation at a single point; when a node's TTL approaches zero or ρc drops sharply, its cross-frame reservation is revoked, OCPNXT is released, and resources are returned to active and reachable entities.

[0132] To ensure that reallocation can be implemented within a single frame, this embodiment maintains a "number-sensitive" binning granularity, without altering the timing of the confirmation-data co-slot closed loop, thus avoiding the potential for cross-slot acknowledgment failures. After the resource reallocation table is generated, two types of consistency checks are performed: first, a critical flow assurance check, verifying that control and tracking reports still have co-slot confirmation paths on their corresponding edges; second, a time base and protection boundary check, preventing expansion operations from exceeding protection intervals or covering reserved slots of cross-domain gateways. After passing these checks, a dynamic time slot allocation scheme is output, represented by a four-tuple sequence of "node pair—slot number—category—occupancy period," with a version stamp written to the time slot resource scheduling unit, allowing each node to execute the allocation in the next frame using a back-to-back reservation mechanism.

[0133] This embodiment tests the mechanism in two scenarios closely resembling maritime rescue. In scenario one, a sudden surge in wind and waves in the southeast sector causes a temporary drop in the confirmation success rate of several search and rescue nodes, resulting in a decrease in ρc, an increase in neighborhood K, and suppression of δ. The system reassigns the previously long, general class allocations, dispersing them and maintaining the confirmation budget in more dispersed gaps, waiting for sea conditions to stabilize before resuming continuous loading. In scenario two, a new suspected target appears at night, causing a sharp increase in the buffer_size of a surface sensing node while maintaining a high position with the gateway C, leading to a jump in δ. The system allocates a proportion of the synchronization pool's transferable portion to this node for high-priority reporting, while the general class portions of other nodes in the group are gently reduced. The lower limit quota for heartbeats and synchronization is retained to maintain the basic time base.

[0134] From a technical and effectiveness perspective, this embodiment uses a status monitoring table to bind node load, activity, and local conflict facts together. The combination of the queue status table and the connectivity matrix provides an objective basis for "which to expand and which to shrink." Through lightweight calculation of the δ coefficient and number-sensitive secondary binning, the initial scheme can be modified as needed without disrupting the confirmation loop and protection interval. By explicitly indicating priorities and transferable boundaries, critical tasks are guaranteed steady-state operation rather than being diluted by averaging when resources are scarce. The output dynamic time slot allocation scheme has timestamps and status tags, which can subsequently work in conjunction with the backpack reservation and local monitoring in S103 to maintain a consistent resource allocation order in sea surface formations with frequent maneuvers and network entry / exit.

[0135] To effectively address the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, and to provide technical support for unmanned swarm collaboration, this application provides an embodiment of an unmanned swarm collaborative self-organizing network time slot resource allocation device for implementing all or part of the aforementioned unmanned swarm collaborative self-organizing network time slot resource allocation method. See [link to embodiment]. Figure 2 The unmanned cluster collaborative self-organizing network time slot resource allocation device specifically includes the following components:

[0136] The task configuration module 10 is used to set cross-domain cluster nodes as gateway control nodes, work group nodes as surface sensing nodes, and unmanned platform nodes as search and rescue execution nodes, generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation calculations on search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit.

[0137] The resource scheduling module 20 is used to divide the time frame into a time synchronization area and a data interaction area, write the time frame structure into the control unit of each surface unmanned node, read the buffer queue length of the adjacent search and rescue node, generate a time slot occupancy table reflecting the communication status of the unmanned cluster, calculate the network connectivity matrix between the surface nodes according to the time slot occupancy table, perform matching operation between the network connectivity matrix and the time slot resource matrix, generate an initial time slot allocation scheme suitable for the search and rescue scenario, and write the initial time slot allocation scheme into the resource scheduling unit.

[0138] The time slot adjustment module 30 is used to write control information into the data frame header in a backpack structure, generate a frame configuration table based on the electromagnetic propagation characteristics at sea, use the frame configuration table for data frame processing, read the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, establish an unmanned node queue status table based on the network connectivity matrix, calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table, correct the allocation scheme according to the dynamic adjustment coefficient, write the corrected allocation scheme into the time slot resource scheduling unit, and generate a time slot resource scheduling strategy that supports unmanned cluster maritime maneuvering.

[0139] As described above, the unmanned cluster collaborative self-organizing network time slot resource allocation device provided in this application embodiment can achieve effective mapping of requirements through an innovatively designed task configuration system, using a state machine and resource matrix. It constructs a time slot allocation mechanism, combining connectivity analysis and resource matching to establish a reliable scheduling scheme. Dynamic optimization is introduced, ensuring the adaptability of the allocation through state monitoring and policy adjustment. This method effectively solves the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, providing technical support for unmanned cluster collaboration.

[0140] To further illustrate this solution, this application also provides a specific application example of using the aforementioned unmanned cluster cooperative self-organizing network time slot resource allocation device to implement the unmanned cluster cooperative self-organizing network time slot resource allocation method, which specifically includes the following:

[0141] This study utilizes simulation technology to apply a task-driven time-slot resource allocation method to a constructed maritime unmanned swarm collaborative operation scenario, verifying the method's adaptive network reconstruction capability under task-driven conditions. The scenario involves a maritime unmanned swarm, with three different tasks—carrying a payload, adding new nodes to the network, and changing formation—for verification.

[0142] The mission to carry out tasks refers to tasks that change the type of workload according to operational needs; the mission to add new nodes to the network refers to the mission of adding UAVs to the network in actual battlefield; the mission to change formation refers to the mission of changing the formation of UAV swarms according to the actual environment under the command of the operation.

[0143] (1) Verification of network reconfiguration driven by bearer task

[0144] The network reconfiguration verification driven by bearer tasks primarily tests the technology's ability to adaptively change time slot allocation strategies when the proportion of different types of services changes. See Table 1 below:

[0145] Table 1. Dynamically Scalable Access Protocol Parameter Settings

[0146]

[0147] When the simulation reached 30 seconds, the frequency of the formation control message changed from 2Hz to 5Hz, and the frequency of the status reporting message changed from 0.5Hz to 0.25Hz.

[0148] As can be seen from the key parameter settings, the frame length is 100ms, and the 300th frame corresponds to 30s of the simulation time. When the periodic service is set to 30s in the scenario, the periodic time slot allocation increases after the 300th frame. This shows that the network can reconstruct time slot resources in carrying tasks.

[0149] (2) Verification of network reconstruction driven by new node joining task

[0150] The network reconstruction verification driven by the new node joining task mainly tests whether this technology can reconstruct the intra-cluster network when a new node joins the network. See Table 2 below:

[0151] Table 2 C-OLSR Parameter Settings

[0152]

[0153] At 20 seconds into the simulation, ten silent nodes in the scenario were added to the network.

[0154] In a clustered architecture, cluster members report information to the cluster head, which communicates with other nodes through the backbone network. When a newly joined member discovers the cluster head via routing, it can then communicate with other nodes in the network through the cluster head. Therefore, the network entry time is defined as the time from when a node powers on to when it can discover the cluster head. Simulation results show the network entry time for nodes within a cluster, as shown in Table 3 below.

[0155] Table 3 Node Network Entry Time

[0156]

[0157] (3) Network reconstruction verification driven by formation change task

[0158] The network reconfiguration test driven by formation change demonstrates that this technique can maintain network topology stability under motion. See Table 4 below:

[0159] Table 4 C-OLSR Parameter Settings

[0160]

[0161] After the simulation started, the cluster of 100 nodes was gradually divided into four groups. By collecting data transmitted through the communication links, it was found that even when the node groups changed and the distance between the nodes and the cluster head was far, the link communication could still be maintained normally.

[0162] From a hardware perspective, in order to effectively address the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, and to provide technical support for unmanned swarm collaboration, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned unmanned swarm collaborative self-organizing network time slot resource allocation method. The electronic device specifically includes the following components:

[0163] The system comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the unmanned cluster collaborative self-organizing network time slot resource allocation device and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the unmanned cluster collaborative self-organizing network time slot resource allocation method and the embodiments of the unmanned cluster collaborative self-organizing network time slot resource allocation device in the embodiments, the contents of which are incorporated herein, and repeated details will not be described again.

[0164] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0165] In practical applications, some parts of the unmanned cluster cooperative self-organizing network time slot resource allocation method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific choice depends on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0166] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0167] Figure 3 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 3 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0168] In one embodiment, the unmanned cluster cooperative self-organizing network time slot resource allocation method function can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0169] Step S101: Set the cross-domain cluster node as the gateway control node, the work group node as the surface sensing node, and the unmanned platform node as the search and rescue execution node. Generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on the search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert the task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit.

[0170] Step S102: Divide the time frame into a time synchronization area and a data interaction area, write the time frame structure into the control unit of each surface unmanned node, read the buffer queue length of the adjacent search and rescue node, generate a time slot occupancy table reflecting the communication status of the unmanned cluster, calculate the network connectivity matrix between surface nodes according to the time slot occupancy table, perform matching operation between the network connectivity matrix and the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario, and write the initial time slot allocation scheme into the resource scheduling unit;

[0171] Step S103: Write control information into the data frame header in a backpack structure, generate a frame configuration table based on the electromagnetic propagation characteristics at sea, use the frame configuration table for data frame processing, read the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, establish an unmanned node queue status table based on the network connectivity matrix, calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table, correct the allocation scheme according to the dynamic adjustment coefficient, write the corrected allocation scheme into the time slot resource scheduling unit, and generate a time slot resource scheduling strategy that supports unmanned cluster maritime maneuvering.

[0172] As described above, the electronic device provided in this application, through an innovatively designed task configuration system, achieves effective mapping of requirements via state machines and resource matrices. A time slot allocation mechanism is constructed, combining connectivity analysis and resource matching to establish a reliable scheduling scheme. Dynamic optimization is introduced, ensuring the adaptability of allocation through state monitoring and policy adjustment. This method effectively solves the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, providing technical support for unmanned swarm collaboration.

[0173] In another embodiment, the unmanned cluster collaborative self-organizing network time slot resource allocation device can be configured separately from the central processing unit 9100. For example, the unmanned cluster collaborative self-organizing network time slot resource allocation device can be configured as a chip connected to the central processing unit 9100, and the function of the unmanned cluster collaborative self-organizing network time slot resource allocation method can be realized through the control of the central processing unit.

[0174] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 3 All components shown; in addition, the electronic device 9600 may also include Figure 3 For components not shown, please refer to existing technologies.

[0175] like Figure 3As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0176] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0177] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0178] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0179] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0180] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0181] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored audio via the speaker 9131.

[0182] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method with the execution subject being a server or client in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method with the execution subject being a server or client in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0183] Step S101: Set the cross-domain cluster node as the gateway control node, the work group node as the surface sensing node, and the unmanned platform node as the search and rescue execution node. Generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on the search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert the task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit.

[0184] Step S102: Divide the time frame into a time synchronization area and a data interaction area, write the time frame structure into the control unit of each surface unmanned node, read the buffer queue length of the adjacent search and rescue node, generate a time slot occupancy table reflecting the communication status of the unmanned cluster, calculate the network connectivity matrix between surface nodes according to the time slot occupancy table, perform matching operation between the network connectivity matrix and the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario, and write the initial time slot allocation scheme into the resource scheduling unit;

[0185] Step S103: Write control information into the data frame header in a backpack structure, generate a frame configuration table based on the electromagnetic propagation characteristics at sea, use the frame configuration table for data frame processing, read the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, establish an unmanned node queue status table based on the network connectivity matrix, calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table, correct the allocation scheme according to the dynamic adjustment coefficient, write the corrected allocation scheme into the time slot resource scheduling unit, and generate a time slot resource scheduling strategy that supports unmanned cluster maritime maneuvering.

[0186] As described above, the computer-readable storage medium provided in this application, through an innovatively designed task configuration system, achieves effective mapping of requirements via state machines and resource matrices. It constructs a time slot allocation mechanism, combining connectivity analysis and resource matching to establish a reliable scheduling scheme. Dynamic optimization is introduced, ensuring the adaptability of allocation through state monitoring and policy adjustment. This method effectively addresses the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, providing technical support for unmanned cluster collaboration.

[0187] Embodiments of this application also provide a computer program product capable of implementing all steps in the unmanned cluster cooperative self-organizing network time slot resource allocation method described above, where the execution subject is a server or client. When executed by a processor, this computer program / instruction implements the steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method. For example, the computer program / instruction implements the following steps:

[0188] Step S101: Set the cross-domain cluster node as the gateway control node, the work group node as the surface sensing node, and the unmanned platform node as the search and rescue execution node. Generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on the search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert the task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit.

[0189] Step S102: Divide the time frame into a time synchronization area and a data interaction area, write the time frame structure into the control unit of each surface unmanned node, read the buffer queue length of the adjacent search and rescue node, generate a time slot occupancy table reflecting the communication status of the unmanned cluster, calculate the network connectivity matrix between surface nodes according to the time slot occupancy table, perform matching operation between the network connectivity matrix and the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario, and write the initial time slot allocation scheme into the resource scheduling unit;

[0190] Step S103: Write control information into the data frame header in a backpack structure, generate a frame configuration table based on the electromagnetic propagation characteristics at sea, use the frame configuration table for data frame processing, read the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, establish an unmanned node queue status table based on the network connectivity matrix, calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table, correct the allocation scheme according to the dynamic adjustment coefficient, write the corrected allocation scheme into the time slot resource scheduling unit, and generate a time slot resource scheduling strategy that supports unmanned cluster maritime maneuvering.

[0191] As described above, the computer program product provided in this application, through an innovative design of a task configuration system, achieves effective mapping of requirements via state machines and resource matrices. It constructs a time slot allocation mechanism, combining connectivity analysis and resource matching to establish a reliable scheduling scheme. Dynamic optimization is introduced, ensuring the adaptability of allocation through state monitoring and policy adjustment. This method effectively solves the shortcomings of traditional technologies in task configuration, time slot allocation, and dynamic adjustment, providing technical support for unmanned swarm collaboration.

[0192] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0193] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0194] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0195] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0196] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for allocating time slot resources in an unmanned cluster cooperative self-organizing network, characterized in that, The method includes: Set the cross-domain cluster node as the gateway control node, the work group node as the surface sensing node, and the unmanned platform node as the search and rescue execution node. Generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on the search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert the task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit. A time frame structure table containing synchronization frame length, data frame length, and guard interval is constructed. The time frame is divided into a time synchronization area and a data interaction area according to a fixed interval. A frame sub-parameter table containing frame format, number of time slots, and time slot length is generated. The frame sub-parameter table is written into the control unit of each surface unmanned node. The buffer queue length of the adjacent search and rescue node is read to generate a time slot occupancy table reflecting the communication status of the unmanned cluster. The network connectivity matrix between surface nodes is calculated according to the time slot occupancy table. The network connectivity matrix is ​​matched with the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario. The initial time slot allocation scheme is written into the resource scheduling unit. Control information is written into the header of the data frame in a backpack structure. A frame configuration table is generated based on the electromagnetic propagation characteristics at sea. The frame configuration table is used for data frame processing. The time slot occupancy table is read to perform distributed time slot selection for search and rescue nodes. An unmanned node queue status table is established based on the network connectivity matrix. The time slot occupancy dynamic adjustment coefficient is calculated based on the unmanned node queue status table. The allocation scheme is corrected according to the dynamic adjustment coefficient. The corrected allocation scheme is written into the time slot resource scheduling unit to generate a time slot resource scheduling strategy that supports the maritime maneuvering of unmanned swarms.

2. The unmanned cluster cooperative self-organizing network time slot resource allocation method according to claim 1, characterized in that, The step of setting cross-domain cluster nodes as gateway control nodes, setting work group nodes as surface sensing nodes, and setting unmanned platform nodes as search and rescue execution nodes, and generating a task configuration matrix, includes: Construct a cluster organization table that includes control hierarchy, command relationship and operation area, read the cross-domain cluster node identifier and map it as gateway control node, read the operation group node identifier and map it as surface sensing node, read the unmanned platform node identifier and map it as search and rescue execution node, and write the cluster organization table into the node management unit. Construct a task planning table that includes task attributes, message types, and communication cycles. Divide the node communication levels according to the cluster organization table, generate a task configuration matrix that includes node identifiers, level attributes, and communication requirements, and write the task configuration matrix into the task management unit.

3. The unmanned cluster cooperative self-organizing network time slot resource allocation method according to claim 1, characterized in that, The process of constructing a task state machine based on the task configuration matrix, performing task simulation calculations on search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, converting task requirements into a time-slot resource matrix, and writing the time-slot resource matrix into a resource allocation unit includes: Construct a state machine configuration table that includes state transition conditions, task triggering conditions, and simulation parameters. Generate a task state machine based on the task configuration matrix. Perform Monte Carlo simulations on search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the state machine. Generate a task requirement prediction table and write the task requirement prediction table into the simulation engine. A resource mapping table containing communication performance, data capacity, and quality indicators is constructed. Communication resource requirements are calculated based on the task requirement prediction table, a time slot resource matrix is ​​generated, and the time slot resource matrix is ​​written into the resource allocation unit according to priority.

4. The unmanned cluster cooperative self-organizing network time slot resource allocation method according to claim 1, characterized in that, The step of reading the buffer queue length of neighboring search and rescue nodes and generating a time slot occupancy table reflecting the communication status of the unmanned cluster includes: A neighbor status table containing node address, queue length, and lifetime is constructed. The cache queue length information of neighboring search and rescue nodes is read, and a time slot occupancy table containing time slot status, reservation information, and hop count information is generated. The time slot occupancy table is then written into the status management unit.

5. The unmanned cluster cooperative self-organizing network time slot resource allocation method according to claim 1, characterized in that, The step of calculating the network connectivity matrix between surface nodes based on the time slot occupancy table, matching the network connectivity matrix with the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario, and writing the initial time slot allocation scheme into the resource scheduling unit includes: A network topology table containing node identifiers, link status, and time slot status is constructed. The connectivity between water surface nodes is calculated based on the time slot occupancy table, a network connectivity matrix is ​​generated, and the network connectivity matrix is ​​written into the topology management unit. A matching strategy table containing resource requirements, connectivity status, and allocation weights is constructed. The network connectivity matrix and the time slot resource matrix are subjected to weighted matching operations to generate an initial time slot allocation scheme. The initial time slot allocation scheme is then written into the resource scheduling unit.

6. The unmanned cluster cooperative self-organizing network time slot resource allocation method according to claim 1, characterized in that, The steps include writing control information into the data frame header in a backpack structure, generating a frame configuration table based on the electromagnetic propagation characteristics at sea, using the frame configuration table for data frame processing, and reading the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, including: A dedicated frame header table containing frame type, reservation field, and queue length is constructed. Control information is written into the data frame header in a back-mounted structure. A sub-frame configuration table is generated based on the electromagnetic propagation characteristics at sea. The sub-frame configuration table is then written into the data processing unit. A time slot status table containing idle, occupied, and busy states is constructed. The time slot occupancy table is read to determine the status, and a distributed selection matrix containing sending time slots, receiving time slots, and reserved time slots is generated. The distributed selection matrix is ​​then written into the time slot scheduling unit.

7. The unmanned cluster cooperative self-organizing network time slot resource allocation method according to claim 1, characterized in that, The process involves establishing an unmanned node queue status table based on the network connectivity matrix, calculating a dynamic adjustment coefficient for time slot occupancy based on the unmanned node queue status table, correcting the allocation scheme according to the dynamic adjustment coefficient, writing the corrected allocation scheme into the time slot resource scheduling unit, and generating a time slot resource scheduling strategy that supports unmanned swarm maritime maneuvering, including: Construct a status monitoring table that includes node identifier, queue length, and lifespan; calculate the queue status distribution among nodes based on the network connectivity matrix; generate an unmanned node queue status table; calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table; and write the dynamic adjustment coefficient into the adjustment control unit. A resource reallocation table containing an initial scheme, adjustment coefficients, and priorities is constructed. The initial time slot allocation scheme is modified according to the dynamic adjustment coefficients to generate a dynamic time slot allocation scheme. The dynamic time slot allocation scheme is then written into the time slot resource scheduling unit.

8. A time slot resource allocation device for an unmanned cluster cooperative self-organizing network, characterized in that, The device includes: The task configuration module is used to set cross-domain cluster nodes as gateway control nodes, work group nodes as surface sensing nodes, and unmanned platform nodes as search and rescue execution nodes, generate a task configuration matrix, construct a task state machine based on the task configuration matrix, perform task simulation and calculation on search and rescue area coverage, target search and tracking, and casualty transfer and retrieval based on the task state machine, convert task requirements into a time slot resource matrix, and write the time slot resource matrix into the resource allocation unit. The resource scheduling module is used to construct a time frame structure table containing synchronization frame length, data frame length, and protection interval; divide the time frame into a time synchronization area and a data interaction area according to a fixed interval; generate a sub-frame parameter table containing frame format, number of time slots, and time slot length; write the sub-frame parameter table into the control unit of each surface unmanned node; read the buffer queue length of adjacent search and rescue nodes; generate a time slot occupancy table reflecting the communication status of the unmanned cluster; calculate the network connectivity matrix between surface nodes based on the time slot occupancy table; perform matching operations between the network connectivity matrix and the time slot resource matrix to generate an initial time slot allocation scheme suitable for the search and rescue scenario; and write the initial time slot allocation scheme into the resource scheduling unit. The time slot adjustment module is used to write control information into the data frame header in a backpack structure, generate a frame configuration table based on the electromagnetic propagation characteristics at sea, use the frame configuration table for data frame processing, read the time slot occupancy table to perform distributed time slot selection for search and rescue nodes, establish an unmanned node queue status table based on the network connectivity matrix, calculate the time slot occupancy dynamic adjustment coefficient based on the unmanned node queue status table, correct the allocation scheme according to the dynamic adjustment coefficient, write the corrected allocation scheme into the time slot resource scheduling unit, and generate a time slot resource scheduling strategy that supports unmanned swarm maritime maneuvering.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the unmanned cluster cooperative self-organizing network time slot resource allocation method according to any one of claims 1 to 7.

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