A method, system and medium for allocating communication network resources of a drone formation

Abstract

The present application relates to the technical field of wireless communication, and in particular to a method and system for allocating communication network resources of a UAV formation, and a medium, which solves the technical problems of information lag in high-speed mobile scenarios and inaccurate allocation of limited network resources and poor real-time performance caused by wireless channel sharing conflict domains due to the dependence on periodic feedback of channel state information in the prior art. The method comprises: obtaining state information of a plurality of UAVs in a UAV formation and preset state information; determining flight deviation information of each UAV in the plurality of UAVs according to the state information and the preset state information; determining the communication demand urgency of each UAV according to the flight deviation information of the plurality of UAVs; dividing the plurality of UAVs into a plurality of communication groups according to the communication demand urgency of the plurality of UAVs; and allocating corresponding communication network resources to different communication groups according to the division results of the plurality of communication groups.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a method, system, and medium for allocating communication network resources for unmanned aerial vehicle (UAV) formations. Background Technology

[0002] With the rapid development of drone technology, multi-drone collaborative formation has become the mainstream application mode. Through information sharing and collaborative decision-making, drone formation has greatly expanded the capabilities of individual drones. Reliable and efficient communication transmission within the formation serves as the core support for collaborative operations, and its importance is becoming increasingly prominent.

[0003] Drone formations need to make flexible changes in formation and path planning according to actual mission requirements. They may also face situations where members dynamically join or leave. The complex and ever-changing operational scenarios place higher demands on the adaptability and flexibility of communication network resource allocation, requiring resource allocation schemes to respond promptly to changes in the environment and formation status.

[0004] Traditional distributed communication resource allocation methods rely on real-time channel state information periodically fed back by UAVs, which makes it difficult to achieve global resource coordination and optimization and differentiated service quality assurance in scenarios with rapidly changing formations. At the same time, due to the single collision domain characteristic of wireless channels, collisions or conflicts are prone to occur when multiple UAVs share limited network resources, further exacerbating the problem of inaccurate resource allocation. Summary of the Invention

[0005] To address the technical problems in existing technologies, such as information lag in high-speed mobile scenarios due to reliance on periodic feedback of channel state information from UAVs, and inaccurate allocation of limited network resources and poor real-time performance caused by shared collision domains of wireless channels, the present invention aims to provide a communication network resource allocation method, system, and medium for UAV formations. The specific technical solution adopted is as follows:

[0006] Firstly, a method for allocating communication network resources for a drone swarm is provided, comprising: acquiring status information and preset status information of multiple drones in the drone swarm; determining flight deviation information of each drone based on the status information and preset status information; determining the communication demand urgency of each drone based on the flight deviation information; the communication demand urgency is used to characterize the priority of the corresponding drone's demand for communication network resources; dividing the multiple drones into multiple communication groups based on the communication demand urgency of the multiple drones; and allocating corresponding communication network resources to different communication groups based on the division results of the multiple communication groups.

[0007] Based on the above technical solution, in the communication network resource allocation method for UAV formation provided by the present invention, by acquiring the status information and preset status information of each UAV in the UAV formation, the flight deviation information of each UAV is accurately determined, thereby quantifying the urgency of communication demand priority of UAV communication resource demand, and then grouping UAVs according to the urgency and allocating corresponding communication network resources. This can adapt to the dynamic changes in the flight status of UAV formation, avoid the conflict problem when multiple UAVs share limited resources, achieve balanced and accurate allocation of communication network resources, and ensure the reliability and efficiency of communication transmission during UAV formation collaborative operation.

[0008] In conjunction with the first aspect above, in one possible implementation, the method for determining the flight deviation information of each of the multiple UAVs based on state information and preset state information specifically includes: for each UAV, aligning the actual state in the state information with the desired state in the preset state information; determining the flight deviation between the actual state and the desired state at multiple alignment time points; and forming a flight deviation sequence of the UAVs based on the flight deviations at the multiple alignment time points, as the flight deviation information.

[0009] In conjunction with the first aspect above, in one possible implementation, the method for determining the urgency of communication needs for each UAV based on the flight deviation information of multiple UAVs specifically includes: analyzing the relative degree of each UAV's flight deviation within the overall formation deviation based on the flight deviation sequence of multiple UAVs to obtain the individual offset weight of each UAV; determining the offset contribution of each UAV based on the flight deviation sequence and individual offset weight of multiple UAVs; and determining the urgency of communication needs for each UAV based on the offset contribution, the flight deviation at the current moment, and the matching information between the UAV and the network edge node.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the individual offset weight of each UAV specifically includes: for each UAV, analyzing the flight deviation change parameters that characterize the degree of fluctuation in the UAV's flight state based on the flight deviation sequence; and determining the individual offset weight of each UAV based on the flight deviation change parameters of multiple UAVs.

[0011] In conjunction with the first aspect above, in one possible implementation, the method for determining the offset contribution of each UAV specifically includes: performing a first clustering of multiple UAVs based on individual offset weights to form multiple initial groups; for each UAV, determining the intra-group offset feature within its respective initial group; the intra-group offset feature is used to characterize the relative deviation of the UAV within its respective initial group; and determining the offset contribution of each UAV based on the historical changes of the individual offset weights and intra-group offset features of the UAVs.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the method for determining the communication urgency of each UAV based on the offset contribution, the flight deviation at the current moment, and the matching information between the UAV and the network edge node specifically includes: obtaining the matching information between the UAV and the network edge node; determining the communication demand index of the UAV based on the offset contribution, the flight deviation at the current moment, and the matching information; and normalizing the communication demand index of multiple UAVs to obtain the communication demand urgency.

[0013] In conjunction with the first aspect above, in one possible implementation, the method of dividing multiple drones into multiple communication groups based on the urgency of their communication needs specifically includes: using the urgency of communication needs or an intermediate parameter of the urgency of communication needs as a clustering feature, and employing a preset clustering algorithm to cluster the multiple drones to form multiple communication groups.

[0014] In conjunction with the first aspect above, in one possible implementation, the method of allocating corresponding communication network resources to different communication groups based on the division results of multiple communication groups specifically includes: calculating the average communication demand urgency within each communication group; and allocating communication network resources of different priorities or different bandwidths to the corresponding communication groups according to the order of the average communication demand urgency within the group.

[0015] Secondly, a communication network resource allocation system for UAV formations is provided, comprising: a data acquisition module, a deviation analysis module, a demand assessment module, a dynamic grouping module, and a resource allocation module; the data acquisition module is used to acquire the status information and preset status information of multiple UAVs in the UAV formation; the deviation analysis module is used to determine the flight deviation information of each UAV based on the status information and preset status information; the demand assessment module is used to determine the communication demand urgency of each UAV based on the flight deviation information of multiple UAVs; the communication demand urgency is used to characterize the priority of the corresponding UAV's demand for communication network resources; the dynamic grouping module is used to divide the multiple UAVs into multiple communication groups based on the communication demand urgency of the multiple UAVs; and the resource allocation module is used to allocate corresponding communication network resources to different communication groups based on the division results of the multiple communication groups.

[0016] Thirdly, a computer storage medium is provided, which stores instructions that, when executed by a computer, enable the performance of actions as described in the first aspect and any possible implementation thereof.

[0017] The present invention has the following beneficial effects:

[0018] By acquiring the status information and preset status information of each drone in the drone formation, the flight deviation information of each drone can be accurately determined. This allows for the quantification of the urgency of the drone's communication resource needs, prioritizing the drones' communication resource requirements. Based on this urgency, the drones are grouped for communication and allocated corresponding communication network resources. This approach can adapt to the dynamic changes in the drone formation's flight status, avoid conflicts when multiple drones share limited resources, achieve balanced and precise allocation of communication network resources, and ensure the reliability and efficiency of communication transmission during collaborative drone formation operations. Attached Figure Description

[0019] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A system architecture diagram of a communication network resource allocation system for unmanned aerial vehicle (UAV) formations provided in one embodiment of the present invention;

[0021] Figure 2 A flowchart illustrating a method for allocating communication network resources for unmanned aerial vehicle (UAV) formations, as provided in one embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the hardware structure of a communication network resource allocation device for unmanned aerial vehicle (UAV) formations, provided as an embodiment of the present invention. Detailed Implementation

[0023] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a communication network resource allocation method, system, and medium for unmanned aerial vehicle (UAV) formations proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] The following description, in conjunction with the accompanying drawings, details a specific scheme for a communication network resource allocation method, system, and medium for unmanned aerial vehicle (UAV) formations provided by this invention.

[0026] Please see Figure 1The diagram illustrates a system architecture of a communication network resource allocation system for a drone formation according to an embodiment of the present invention. The communication network resource allocation system for a drone formation includes: a data acquisition module 1, a deviation analysis module 2, a demand assessment module 3, a dynamic grouping module 4, and a resource allocation module 5.

[0027] Among them, the data acquisition module 1 is the data foundation support module of the system, which specifically includes the status acquisition submodule 11, the data conversion submodule 12, and the database integration submodule 13.

[0028] The status acquisition submodule 11 uses physical devices such as the global positioning system (GPS) positioning module, signal strength detection sensor, battery power monitoring unit, and flight attitude sensor carried by the UAV to collect real-time status information such as channel bandwidth, signal strength, remaining battery power, GPS positioning coordinates, flight attitude and mission type of the UAV, while obtaining preset status information from the ground mission planning terminal.

[0029] The data conversion submodule 12 can be implemented through the data processing chip built into the UAV or the ground edge computing node to convert the collected unstructured state information into structured data with timestamps and UAV identity documents (IDs), ensuring that the data format is uniform and standardized.

[0030] The database integration submodule 13 is built on the ground central control server. It summarizes and integrates all structured data to form a complete formation flight database. This database provides core data input for the subsequent deviation analysis module 2 and is the basis for all subsequent analysis and calculation of the entire system.

[0031] Deviation analysis module 2 takes over the output results of data acquisition module 1 and focuses on mining flight deviation-related information. It includes state alignment submodule 21, deviation calculation submodule 22, and deviation sequence generation submodule 23. These submodules can be implemented through the computing unit of the ground central control server or the cloud computing platform.

[0032] The state alignment submodule 21 uses an interpolation algorithm to perform time axis alignment processing on the actual state data and preset state data provided by the data acquisition module 1, eliminating the time difference problem caused by data transmission delay. The aligned result is directly transmitted to the deviation calculation submodule 22.

[0033] For each aligned time point, the deviation calculation submodule 22 calculates the flight deviation between the actual state and the preset state, accurately captures the flight deviation of each UAV, and sends the calculation results to the deviation sequence generation submodule 23.

[0034] The deviation sequence generation submodule 23 sorts the flight deviations of all aligned time points in chronological order to form a flight deviation sequence for each UAV. This sequence comprehensively reflects the historical flight deviation characteristics of the UAV and will serve as a key basis for the demand assessment module 3 to calculate the urgency of communication requirements.

[0035] The demand assessment module 3 quantifies the priority of each UAV's communication resource demand based on the flight deviation sequence output by the deviation analysis module 2. This module can be implemented through a high-performance computing server or an edge computing node cluster and includes an individual offset weight calculation submodule 31, an offset contribution calculation submodule 32, and a communication demand urgency calculation submodule 33.

[0036] The individual offset weight calculation submodule 31 analyzes the flight deviation sequence of each UAV, determines the degree of fluctuation in its flight state, and then calculates the flight deviation change parameters of each UAV. Combined with the flight deviation change parameters of all UAVs in the formation, the individual offset weight of each UAV is obtained. This weight quantifies the proportion of the deviation of a single UAV relative to the overall formation. The calculation result is passed to the offset contribution calculation submodule 32.

[0037] The offset contribution calculation submodule 32 receives the output of the individual offset weight calculation submodule 31 and, together with the initial grouping information fed back by the dynamic grouping module 4, determines the intra-group offset characteristics of each UAV in its respective initial group. Then, based on the historical changes of the individual offset weight and intra-group offset characteristics, it calculates the offset contribution of each UAV, comprehensively reflecting the urgency of the UAV's demand for communication resources. The calculation results are sent to the communication demand urgency calculation submodule 33.

[0038] The communication demand urgency calculation submodule 33 obtains the matching information between the UAV and the network edge node, integrates the offset contribution, the flight deviation at the current moment and the matching information, determines the communication demand index of each UAV, and then normalizes the communication demand index of all UAVs to obtain the communication demand urgency. This result is directly provided to the dynamic grouping module 4 to provide the core basis for the accurate grouping of UAVs.

[0039] The dynamic grouping module 4 takes the urgency of communication needs output by the demand assessment module 3 as the core basis to realize the reasonable grouping of UAVs. This module can be implemented through the intelligent scheduling unit of the ground central control server or the cloud clustering analysis platform, including the clustering algorithm execution submodule 41 and the grouping optimization submodule 42.

[0040] The clustering algorithm execution submodule 41 uses the urgency of communication needs or its corresponding intermediate parameters as clustering features, and uses a preset clustering algorithm to cluster all UAVs to form multiple initial groups. The initial group information is fed back to the offset contribution calculation submodule 32 of the demand assessment module 3 to assist it in accurately calculating the offset contribution, and is also passed to the group optimization submodule 42.

[0041] The grouping optimization submodule 42 performs a rationality check on the initial grouping. Combining the real-time flight status and mission requirements of the UAV formation, it adjusts and optimizes the grouping results to ensure that the flight status and communication resource requirements of the UAVs in each group are highly similar. Finally, multiple communication groups are formed, and the grouping results are sent to the resource allocation module 5 to lay the foundation for the differentiated allocation of resources in the future.

[0042] The resource allocation module 5 is based on the final grouping result of the dynamic grouping module 4 to achieve efficient allocation of communication network resources. This module can be implemented through physical devices such as communication network controllers, bandwidth allocators and ground base stations, including a grouping demand statistics submodule 51, a resource allocation submodule 52 and a resource distribution submodule 53.

[0043] The group demand statistics submodule 51 receives the communication groups output by the dynamic group module 4, calculates the average communication demand urgency within each group, clarifies the resource demand priority of different groups, and passes the statistics results to the resource allocation submodule 52.

[0044] The resource allocation submodule 52 allocates communication network resources of different priorities or different bandwidths according to the average communication demand urgency within the group provided by the group demand statistics submodule 51, in order of priority, to ensure that resources are tilted towards the group with more urgent needs, and sends the allocation results to the resource distribution submodule 53.

[0045] The resource distribution submodule 53 accurately distributes the pre-defined communication network resource parameters to each UAV in the corresponding communication group via the wireless communication link, ensuring that each group obtains the appropriate communication resources. At the same time, it feeds back the resource allocation results to the data acquisition module 1, providing a reference for the data acquisition module 1 to dynamically adjust the data acquisition frequency, thus forming a closed-loop optimization of the system.

[0046] Please see Figure 2 The diagram illustrates a flowchart of a method for allocating communication network resources for a drone formation according to an embodiment of the present invention. This method includes:

[0047] S1. Obtain the status information and preset status information of multiple drones in the drone formation.

[0048] Status information includes at least one of the following: real-time location, flight speed, flight attitude, channel status information, remaining battery power, and mission type; preset status information includes at least one of the following: preset flight path, preset trajectory points, and preset flight speed.

[0049] In some implementations, the UAV swarm network is first modeled as a two-layer system containing a physical layer and a virtual layer. The physical layer corresponds to each UAV entity in the swarm, and the virtual layer corresponds to the network logical association model of the swarm. The effect of this modeling operation is to achieve multi-dimensional management of the relationship between UAV entities and the network, which facilitates the accurate collection and classification of subsequent data.

[0050] Subsequently, a threshold for the flight cycle is preset, for example, the flight cycle of the drone is preset to 30 seconds, and then the flight cycle is divided into several time slices, such as five time slices of 6 seconds each. At the beginning of each time slice, a status reporting operation is executed through a preset heartbeat packet trigger rule. For example, the preset trigger threshold is: when the communication delay between the drone and the ground control system is less than 100 milliseconds, a full status report is triggered, reporting all status information to ensure complete synchronization of status information; when the communication delay is greater than 100 milliseconds, an incremental status report is triggered, reporting only the information that has changed compared to the previous cycle, in order to reduce the amount of data transmission and reduce the load on the communication link.

[0051] Next, the status information of each UAV is collected, including real-time location, flight speed, flight attitude, channel status information, remaining battery power, and mission type: real-time location is collected through the GPS positioning module on the UAV, flight speed and flight attitude are obtained through the inertial measurement unit, channel status information (including current channel bandwidth and signal strength) is collected through the channel detection unit, remaining battery power is read through the battery monitoring module, and mission type is obtained through the UAV's mission control unit. This comprehensively obtains the current operating status, communication environment, and mission requirements of the UAV, providing actual data basis for subsequent comparison with preset status.

[0052] Simultaneously, preset state information is acquired, including preset flight paths, preset trajectory points, and preset flight speeds. Specifically, these are pre-configured and generated in the ground central control system using a preset path planning algorithm (such as the A Star path planning algorithm). The preset flight path is the overall route that the formation should follow, the preset trajectory points are key position nodes on the route, and the preset flight speed is the standard flight rate for each stage. This provides an ideal baseline state for UAV operation, facilitating subsequent analysis of the deviation between the actual state and the ideal state.

[0053] The collected status information is then formatted. Using a preset data formatting algorithm (such as a JavaScript object notation (JSON) serialization algorithm), different types of unstructured data (such as raw values ​​output by sensors and text descriptions of task types) are converted into structured JSON format data. At the same time, a timestamp accurate to milliseconds and a unique drone ID are added to each data item to unify the data storage and retrieval format, avoid format conflicts between different types of data, and facilitate subsequent data integration and retrieval.

[0054] Finally, the converted structured data is transmitted to the ground central control system via a wireless communication link. The ground system uses a preset data aggregation algorithm (such as a grouping aggregation algorithm based on UAV ID) to sort the data of multiple time slices of the same UAV in chronological order and classify the data of different UAVs by formation number, integrating them to form a formation flight database. This constructs a unified data set covering the historical and real-time status of all UAVs in the formation, providing complete and orderly data support for the analysis of deviation information in subsequent steps.

[0055] S2. Based on the status information and preset status information, determine the flight deviation information of each of the multiple drones.

[0056] In one possible implementation, the method for determining flight deviation information can be specifically implemented through the following steps S21 to S23, which are explained in detail below:

[0057] S21. For each UAV, align the actual state in the state information with the expected state in the preset state information.

[0058] In some implementations, the expected state of each UAV at the corresponding timestamp is first extracted from a predefined flight plan. This expected state includes the expected position, expected speed, and expected trajectory points. This information is then integrated into an estimated expected state value for the UAV. Simultaneously, real-time collected and fused actual state data of the same UAV is retrieved from the formation flight database to obtain the estimated actual state value for that UAV. Due to the latency in data reception by the central control system, the timestamps of the actual and expected states may not perfectly match. In this case, a preset interpolation algorithm (e.g., linear interpolation) is used to align the estimated actual and expected states on the time axis. A preset time alignment error threshold of 50 milliseconds is used. If the time difference after alignment exceeds this threshold, corresponding optimization strategies are adopted based on the cause of the error.

[0059] If data points are sparse due to data transmission delay: wait for the data in the next preset time slice to arrive, and then re-execute the interpolation operation based on the newly added data points;

[0060] If linear interpolation is not applicable due to a sudden change in the drone's motion state: switch to a higher-order interpolation algorithm (such as cubic spline interpolation) for realignment;

[0061] If multiple interpolations fail to meet the error requirements, a state prediction algorithm based on historical flight patterns is used to generate supplementary data points, followed by time alignment.

[0062] The above layered optimization strategy can specifically eliminate the time misalignment problem caused by data latency, ensuring that the alignment accuracy meets the requirements of subsequent analysis.

[0063] S22. At multiple aligned time points, determine the flight deviation between the actual state and the desired state.

[0064] In some implementations, after time alignment is completed, for each aligned time point, the deviation of each dimension of information between the actual state and the expected state is calculated (i.e., the absolute value of the difference between the actual state value and the expected state value), including the spatial deviation between the actual position and the expected position, the rate deviation between the actual speed and the expected speed, and the node deviation between the actual trajectory point and the expected trajectory point. To eliminate dimensional differences and unify the evaluation scale, the deviation values ​​of each dimension are normalized: spatial deviation is divided by a preset maximum tolerance deviation (e.g., 50 meters); velocity deviation is divided by a preset maximum velocity deviation (e.g., 10 meters / second); node deviation is divided by the maximum allowable index difference (e.g., the maximum allowable index difference for high-precision formation flight (e.g., drone performance, dense formation cruise) is 2, and the maximum allowable index difference for medium-precision area patrol (e.g., area surveillance, logistics delivery) is 4). Then, according to preset dimension weights (e.g., position deviation weight is set to 0.5, velocity deviation weight is set to 0.3, and trajectory point deviation weight is set to 0.2), the normalized deviations of each dimension are weighted and summed to obtain the flight deviation at that time point, which simultaneously represents multiple dimensions, thus comprehensively quantifying the degree of deviation between the actual operating state of the drone and the ideal state at that time point.

[0065] S23. Based on the flight deviations at multiple alignment time points, a flight deviation sequence of the UAV is formed as flight deviation information.

[0066] In some implementations, the flight deviations corresponding to each alignment time point are arranged sequentially according to time to form a flight deviation sequence for the UAV. At the same time, a preset length threshold of 20 time points is set for the flight deviation sequence. When the sequence length exceeds this threshold, a sliding window mechanism is used to retain the deviation data of the latest 20 time points and discard the earliest deviation data, so as to fully reflect the deviation changes of the UAV over a period of time. The resulting flight deviation sequence can serve as the core basis for subsequent analysis of the stability of the UAV's flight status and determination of communication resource requirements.

[0067] S3. Based on the flight deviation information of multiple drones, determine the urgency of communication needs for each drone.

[0068] The urgency of communication needs is used to characterize the priority of a corresponding UAV's demand for communication network resources.

[0069] In one possible implementation, the method for determining the urgency of communication needs can be specifically implemented through the following steps S31 to S33, which are explained in detail below:

[0070] S31. Based on the flight deviation sequences of multiple UAVs, analyze the relative degree of each UAV's flight deviation in the overall formation deviation, and obtain the individual offset weight of each UAV.

[0071] In some implementations, for each UAV, a flight deviation change parameter characterizing the degree of fluctuation in the UAV's flight state is analyzed based on the flight deviation sequence; and based on the flight deviation change parameters of multiple UAVs, the individual offset weight of each UAV is determined.

[0072] Specifically, for each drone, a preset threshold of 20 historical moments is set. This number of deviation data is extracted from its flight deviation sequence, the absolute value of the difference between the flight deviations of two adjacent moments is calculated, and the average of these absolute values ​​is taken to obtain the flight deviation change parameter of the drone. This quantifies the deviation fluctuation of the drone in continuous moments into a single parameter, accurately representing the degree to which its flight status is affected by external environmental interference.

[0073] Subsequently, the flight deviation changes of all UAVs in the formation were statistically analyzed, and their sum was calculated. To prevent the sum from being zero and causing calculation errors, a very small positive correction term (e.g., with a value of 10) was added to the denominator. -6 Then, the flight deviation change parameters of each UAV are divided by the sum of the total and the positive correction term to obtain the individual offset weight of the UAV, clarifying the proportion of the deviation fluctuation of a single UAV in the overall deviation fluctuation of the formation, and realizing the quantitative correlation between the individual and the whole.

[0074] S32. Determine the offset contribution of each UAV based on the flight deviation sequence and individual offset weight of multiple UAVs.

[0075] In some implementations, multiple UAVs are first clustered based on individual offset weights to form multiple initial groups; for each UAV, the intra-group offset feature in its initial group is determined, which is used to characterize the relative deviation of the UAV within its initial group; and the offset contribution of each UAV is determined based on the historical changes of the individual offset weights and intra-group offset features of the UAVs.

[0076] Specifically, firstly, a feature vector is constructed for each UAV, which contains the UAV's real-time position coordinates and individual offset weights. A preset clustering algorithm (such as hierarchical clustering algorithm) is used, and the individual offset weights are used as the weights of the distance metric of the algorithm. All UAVs are clustered to form multiple initial groups, and UAVs with similar flight deviation features and position features are grouped together.

[0077] For each initial group, the average individual offset weight of all UAVs in the group is calculated. Then, the individual offset weight of each UAV is divided by the average individual offset weight of its group to obtain the intra-group offset characteristics of the UAV in the corresponding group, clarify the relative deviation level of a single UAV in its group, and distinguish the deviation differences within the group.

[0078] Subsequently, with a preset historical time window of 10 time points, the intra-group offset features of each UAV within this window are extracted. The absolute value of the difference between the intra-group offset features of adjacent time points is calculated (reflecting the stability of the intra-group offset feature changes; a larger change indicates greater instability). The offset contribution of the UAV is then calculated.

[0079]

[0080] In the formula, This represents the offset contribution of the i-th UAV within the I-th group at time t, used to characterize the urgency of communication needs; Let be the individual offset weight for the i-th drone; The intra-group offset feature of the i-th UAV within the i-th group at time k; is the intra-group offset feature of the i-th UAV within the i-th group at time k-1; N is the length of the preset historical time window.

[0081] Unless otherwise specified, the normalization function (norm) and normalization method mentioned in the embodiments of this invention all employ maximum and minimum value normalization. The maximum and minimum values ​​are preset empirical extreme values ​​derived from a large amount of historical experimental data. If the calculation result exceeds the interval [0, 1], it is restricted to the range [0, 1] by a truncation function (i.e., if the result is less than 0, it is taken as 0; if it is greater than 1, it is taken as 1) to eliminate the influence of outliers on the result.

[0082] The final offset contribution score combines the stability of individual offset degree and intra-group offset characteristics to quantify the urgency of communication resource demand.

[0083] S33. Based on the offset contribution, the flight deviation at the current moment, and the matching information between the UAV and the network edge node, determine the urgency of each UAV's communication needs.

[0084] In some implementations, matching information between the UAV and network edge nodes is obtained; based on the offset contribution, the flight deviation at the current moment, and the matching information, the communication demand indicators of the UAV are determined; and the communication demand indicators of multiple UAVs are normalized to obtain the urgency of communication demand.

[0085] The method for obtaining matching information between the UAV and network edge nodes specifically includes: determining matching information based on at least one factor among the signal quality between the UAV and multiple candidate network edge nodes and the current load status of the candidate network edge nodes. The matching information is used to characterize the priority level of the UAV accessing different candidate network edge nodes.

[0086] Specifically, for each candidate network edge node of a drone, the signal quality (such as reference signal received power) and current load status (such as the number of connected drones) of the node are collected. For example, the weight of signal quality is preset to 0.6 and the weight of load status is preset to 0.4. After normalizing the two respectively, the weighted sum is obtained to obtain the matching priority of the node corresponding to the drone, which is used as matching information.

[0087] Subsequently, the flight deviation and matching priority at the current moment are normalized and mapped to the [0,1] interval. Then, the normalized offset contribution is multiplied by the flight deviation at the current moment, and the result is divided by the normalized matching priority to obtain the communication requirement index of the UAV.

[0088]

[0089] In the formula, Let be the communication requirement index for the i-th drone, used to quantify the urgency of communication needs; The offset contribution is a normalized indicator; This represents the normalized flight deviation at the current moment. This represents the normalized matching priority. The parameter is set to a very small coefficient, such as 0.01, to avoid the denominator being zero. The offset contribution is multiplied by the flight deviation to reflect the basic urgency of the demand. Then, it is divided by the matching priority to reflect the amplifying effect of access difficulty on the demand, thus quantifying the urgency of the communication demand.

[0090] Finally, a normalization function is used to map all the communication demand indicators of UAVs to a standardized range to obtain the urgency of communication demand, and the demand indicators are transformed into a unified standard priority.

[0091] S4. Based on the urgency of the communication needs of multiple drones, divide the multiple drones into multiple communication groups.

[0092] In some implementations, the urgency of communication needs or an intermediate parameter of the urgency of communication needs is used as a clustering feature, and a preset clustering algorithm is used to cluster multiple drones to form multiple communication groups.

[0093] Specifically, using the urgency of communication needs as a clustering feature, a pre-defined clustering algorithm is employed to cluster multiple drones. The urgency of each drone's communication needs is used as a feature dimension, and the distance (e.g., Euclidean distance or Manhattan distance) between any two drones in this feature space is calculated; the smaller the distance, the closer the urgency of their communication needs. Subsequently, a pre-defined hierarchical clustering algorithm is used to group drones with close distances into the same communication group, ensuring that the drones within each group have highly similar communication urgency.

[0094] After clustering is completed, the resulting communication groups are synchronized to the large-scale UAV swarm flight system.

[0095] Furthermore, grouping can also be achieved by using intermediate parameters of communication demand urgency as clustering features. These intermediate parameters refer to the intermediate feature parameters generated during the calculation of communication demand urgency, including the individual offset weights in S31 and the within-group offset features or offset contribution in S32 (the intermediate parameters are pre-normalized). Using these intermediate parameters as features, and employing the same clustering method as for communication demand urgency, the distance of the UAV in the intermediate parameter feature space is calculated, and clustering is performed to form communication groups.

[0096] S5. Based on the division results of multiple communication groups, allocate corresponding communication network resources to different communication groups.

[0097] In some implementations, the average communication demand urgency within each communication group is calculated; based on the order of the average communication demand urgency within the group, communication network resources of different priorities or different bandwidths are allocated to the corresponding communication groups.

[0098] Specifically, the communication urgency data of all drones in each group is retrieved, and the mean is calculated using an arithmetic mean algorithm to obtain the average communication urgency of the group. The communication needs of individuals within the group are then integrated into a unified quantitative indicator at the group level.

[0099] Next, all communication groups are sorted in descending order of their average communication demand urgency within each group. Simultaneously, priority levels and corresponding thresholds for resource allocation are preset: for example, groups with an average urgency greater than or equal to 0.8 are classified as high priority, those between 0.6 and 0.8 as medium priority, and those less than 0.6 as low priority. After sorting, high-priority groups will receive better access to communication resources, clearly defining the priority order of resource needs for each group and ensuring that resources are allocated to groups with more urgent needs, thus avoiding indiscriminate resource allocation.

[0100] Subsequently, based on the total available resources of the communication network, different specifications of communication resources are allocated according to priority: for example, if the total bandwidth of the current communication network is 120MHz, 45% of the total bandwidth (i.e., 54MHz) is allocated to high-priority groups, and the highest priority of the transmission queue is configured (preset as level 1 queue, where data packets are forwarded first); 35% of the total bandwidth (i.e., 42MHz) is allocated to medium-priority groups, and level 2 transmission queues are configured; and 20% of the total bandwidth (i.e., 24MHz) is allocated to low-priority groups, and level 3 transmission queues are configured. This ensures that groups with more urgent needs receive more sufficient and reliable communication resources, matching their requirements for high-frequency and stable communication, and solving the problem of insufficient communication support for high-demand UAVs in high-speed mobile scenarios.

[0101] Simultaneously, each communication group is input into a large-scale UAV swarm flight system. This system will schedule the communication links of the UAVs within the corresponding group based on the resource allocation parameters (bandwidth, transmission priority) of each group, ensuring that the allocated resources are used accurately, and implementing the resource allocation scheme into actual communication link configuration, so as to ensure that the communication transmission of UAVs within the group meets the resource allocation expectations.

[0102] Finally, the communication resource allocation module monitors the communication status of each group in real time and presets a communication quality threshold: for example, a signal transmission success rate of ≥96% is considered acceptable. If the communication quality of a group is lower than this threshold for three consecutive time slices (the preset time slice is 6 seconds), a temporary resource adjustment mechanism will be triggered, adding an extra 5MHz of bandwidth to the group until its communication quality recovers to above the threshold. This achieves closed-loop optimization of resource allocation, avoids communication resource shortages caused by environmental changes, and ensures the continuous reliability of formation communication.

[0103] Based on the above technical solution, by acquiring the status information and preset status information of each UAV in the UAV formation, the flight deviation information of each UAV can be accurately determined. Then, the urgency of the communication demand priority of UAV communication resource requirements can be quantified. Based on the urgency, UAVs are grouped for communication and corresponding communication network resources are allocated. This can adapt to the dynamic changes in the flight status of the UAV formation, avoid the conflict problem when multiple UAVs share limited resources, achieve balanced and accurate allocation of communication network resources, and ensure the reliability and efficiency of communication transmission during the collaborative operation of UAV formation.

[0104] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0106] In this embodiment of the invention, the communication network resource allocation device for UAV formations can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0107] This invention also provides a hardware structure diagram of a communication network resource allocation device for unmanned aerial vehicle (UAV) formations, see [link / reference]. Figure 3 The communication network resource allocation device 300 for the drone formation includes a processor 301, and optionally, a memory 302 connected to the processor 301.

[0108] In the first possible implementation, see Figure 3 The communication network resource allocation device 300 for UAV formations also includes a transceiver 303. The processor 301, memory 302, and transceiver 303 are connected via a bus. The transceiver 303 is used to communicate with other devices or communication networks. Optionally, the transceiver 303 may include a transmitter and a receiver. The device in the transceiver 303 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of the present invention. The device in the transceiver 303 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of the present invention.

[0109] Based on the first possible implementation method Figure 3 The schematic diagram shown can be used to illustrate the structure of the communication network resource allocation device for the UAV formation involved in the above embodiments.

[0110] in, Figure 3 The diagram can also illustrate the system chip in the communication network resource allocation device for the drone formation. In this case, the actions performed by the aforementioned communication network resource allocation device for the drone formation can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0111] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0112] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for allocating communication network resources for unmanned aerial vehicle (UAV) formations, characterized in that, include: Acquire the status information and preset status information of multiple drones in a drone formation; For each drone, align the actual state in the state information with the desired state in the preset state information; At multiple aligned time points, determine the flight deviation between the actual state and the desired state; Based on the flight deviations at multiple alignment time points, a flight deviation sequence for each UAV is formed; Based on the flight deviation sequences of multiple UAVs, the relative degree of each UAV's flight deviation in the overall formation deviation is analyzed to obtain the individual offset weight of each UAV. The offset contribution of each UAV is determined based on the flight deviation sequences and individual offset weights of multiple UAVs. Obtain matching information between drones and network edge nodes; Based on the offset contribution, the flight deviation at the current moment, and the matching information between the UAV and the network edge node, the communication requirement indicators of each UAV are determined. The communication demand indicators of multiple drones are normalized to obtain the communication demand urgency of each drone; the communication demand urgency is used to characterize the priority of the corresponding drone's demand for communication network resources. Based on the urgency of the communication needs of multiple drones, the drones are divided into multiple communication groups; Based on the division results of multiple communication packets, corresponding communication network resources are allocated to different communication packets.

2. The method for allocating communication network resources for unmanned aerial vehicle (UAV) formations according to claim 1, characterized in that, The individual offset weights for each drone are obtained, including: For each UAV, flight deviation change parameters characterizing the degree of fluctuation in the UAV's flight state are analyzed based on flight deviation sequence analysis; The individual offset weight of each UAV is determined based on the flight deviation change parameters of multiple UAVs.

3. The method for allocating communication network resources for unmanned aerial vehicle (UAV) formations according to claim 1, characterized in that, Determine the offset contribution of each drone, including: Based on the individual offset weights, multiple UAVs are first clustered to form multiple initial groups; For each UAV, determine its intra-group offset feature within its initial group; the intra-group offset feature is used to characterize the relative deviation of the UAV within its initial group. The offset contribution of each UAV is determined based on the historical changes in the individual offset weights and intra-group offset characteristics of the UAVs.

4. The method for allocating communication network resources for unmanned aerial vehicle (UAV) formations according to claim 1, characterized in that, Based on the urgency of the communication needs of multiple drones, the drones are divided into multiple communication groups, including: Using the urgency of the communication demand or an intermediate parameter of the urgency of the communication demand as clustering features, a preset clustering algorithm is used to cluster multiple drones to form the multiple communication groups.

5. The method for allocating communication network resources for unmanned aerial vehicle (UAV) formations according to claim 4, characterized in that, Based on the division results of multiple communication packets, corresponding communication network resources are allocated to different communication packets, including: Calculate the average urgency of communication needs within each communication group; Based on the order of the urgency of the average communication demand within the group, communication network resources of different priorities or different bandwidths are allocated to the corresponding communication groups.

6. A communication network resource allocation system for unmanned aerial vehicle (UAV) formations, characterized in that, include: The module includes a data acquisition module, a deviation analysis module, a demand assessment module, a dynamic grouping module, and a resource allocation module. The data acquisition module is used to acquire the status information and preset status information of multiple drones in the drone formation; The deviation analysis module is used to align the actual state in the state information with the expected state in the preset state information for each UAV. At multiple aligned time points, the flight deviation between the actual state and the desired state is determined; based on the flight deviation at multiple aligned time points, a flight deviation sequence for each UAV is formed. The demand assessment module is used to analyze the relative degree of each UAV's flight deviation within the overall formation deviation based on the flight deviation sequences of multiple UAVs, obtaining the individual offset weight of each UAV; determine the offset contribution of each UAV based on the flight deviation sequences and individual offset weights of multiple UAVs; obtain the matching information between the UAVs and network edge nodes; determine the communication demand index of each UAV based on the offset contribution, the current flight deviation, and the matching information between the UAVs and network edge nodes; normalize the communication demand indexes of multiple UAVs to obtain the communication demand urgency of each UAV; the communication demand urgency is used to characterize the priority of the corresponding UAV's demand for communication network resources; The dynamic grouping module is used to divide multiple drones into multiple communication groups based on the urgency of their communication needs. The resource allocation module is used to allocate corresponding communication network resources to different communication groups based on the division results of multiple communication groups.

7. A computer storage medium, characterized in that, The computer storage medium stores instructions that, when executed by the computer, enable the implementation of the steps of the communication network resource allocation method for UAV formations as described in any one of claims 1-5.

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