Consistency control method and device for unmanned aerial vehicle cluster system

Abstract

The invention provides a consistency control method and device for an unmanned aerial vehicle cluster system. Each unmanned aerial vehicle in the system collects an initial state value of at least one state quantity of a target object, decomposes the initial state value into a plurality of sub-state values, and maintains a total state variable, a total quantity variable and an auxiliary variable; the method comprises the steps that each unmanned aerial vehicle judges whether the unmanned aerial vehicle meets a message sending condition or not based on a preset state non-zero constraint and a topological memory constraint; when a message sending condition is met, generating a consistency updating message carrying a part of non-zero sub-state values and auxiliary variable values, sending the consistency updating message to a neighbor, and setting the part of local non-zero sub-state values and the auxiliary variable values of the unmanned aerial vehicle to be zero; and when the unmanned aerial vehicle receives the consistency updating message, updating a local variable value based on the consistency updating message, and performing consensus judgment based on the updated variable value. According to the technical scheme, the communication efficiency, the safety and the operation reliability of the consistency control process can be improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and specifically to a consistency control method and apparatus for a UAV swarm system. Background Technology

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, wireless communication technology, and intelligent control theory, UAV swarm systems, composed of multiple UAVs, are widely used in environmental monitoring, target search and tracking, disaster relief, military reconnaissance, and collaborative transportation due to their advantages such as flexible deployment, strong robustness, and high mission execution efficiency. UAV swarms typically operate in a distributed manner, with each UAV interacting locally with neighboring UAVs to achieve collaborative decision-making and consistent control. This avoids dependence on a centralized controller and improves the system's scalability and fault tolerance.

[0003] In the collaborative control of UAV swarms, consistency control is one of the key fundamental issues for achieving formation maintenance, state synchronization, and collaborative task execution. Traditional consistency control methods typically require UAVs to periodically exchange state information in continuous or discrete time intervals to ensure gradual convergence of the swarm state. However, in practical applications, UAV swarms are often constrained by factors such as wireless communication bandwidth, energy consumption, and communication interference. Frequent information exchange can significantly increase communication burden and energy consumption, reduce overall system efficiency, and even affect swarm stability and task completion capabilities. Furthermore, in UAV swarm systems, the state information of each UAV usually includes sensitive data such as position, velocity, attitude, trajectory, or control parameters. In open wireless communication environments, this state information is vulnerable to eavesdropping, inference, or malicious attacks during transmission, potentially leading to privacy leaks, increased security risks, and even swarm collaboration failure. Therefore, how to reduce communication frequency and minimize the direct exposure of state information while ensuring consistency control performance has become a crucial problem that urgently needs to be solved in UAV swarm systems.

[0004] To alleviate the aforementioned communication and privacy issues, event-triggered control mechanisms have been increasingly introduced into the field of UAV swarm consensus control. Unlike traditional periodic sampling control strategies, event-triggered mechanisms only exchange information when the system state meets specific triggering conditions, thereby effectively reducing the number of communications and energy consumption while ensuring system stability and control performance. Event-triggered distributed consensus control methods demonstrate significant advantages in reducing communication overhead and improving system operating efficiency.

[0005] Related event triggering consistency control methods can be classified according to their design characteristics, such as whether they rely on global topology information, whether the trigger threshold is fixed or adaptive, and the detection method of the trigger conditions. Among them, the fully distributed event triggering mechanism requires each drone to rely only on its own state and the local information of its neighbors for triggering judgment, without relying on any global network parameters, making it more suitable for large-scale drone swarms and resource-constrained practical application scenarios. However, existing related technologies still generally have the following shortcomings in practical applications:

[0006] First, there is insufficient identification of state validity. Related solutions typically use state error or threshold functions as triggering criteria, ignoring the validity judgment of the system state itself. Communication may still be triggered when the state change is minimal or even when the state is invalid, resulting in redundant information transmission.

[0007] Second, the neighbor selection strategy lacks utilization of historical information. Most schemes adopt a static or random neighbor selection strategy after communication is triggered, without introducing historical neighbor interaction information or topological memory mechanism. This makes it easy to repeatedly select the same neighbor in multi-step iteration, resulting in inefficient communication paths and even the formation of invalid state loops.

[0008] Third, the triggering mechanism design is complex or relies on global information. Some event triggering schemes ensure system stability by introducing auxiliary dynamic variables, global topological feature values, or continuous monitoring mechanisms. This not only increases the implementation complexity but also limits its application in fully distributed and resource-constrained environments.

[0009] In summary, existing UAV swarm consensus control technologies still have shortcomings in reducing communication redundancy, improving communication efficiency, and achieving fully distributed control. Therefore, it is necessary to propose a consensus control scheme for UAV swarm systems that, without relying on global network parameters, simultaneously considers state validity and communication history information, thereby improving the communication efficiency, security, and operational reliability of UAV swarm systems in practical applications. Summary of the Invention

[0010] In view of this, this application proposes a consistency control method and apparatus for a drone swarm system, aiming to improve the communication efficiency, security and operational reliability of the drone swarm system in practical applications.

[0011] Specifically, this application is implemented through the following technical solution:

[0012] According to a first aspect of the embodiments of this specification, a consistency control method for a drone swarm system is provided. The drone swarm system includes multiple drones, with at least one information transmission path existing between any two drones. Each drone collects an initial state value of at least one state variable of a target object and decomposes the initial state value into multiple sub-state values, each sub-state value being randomly generated locally by the drone. Each drone maintains a total state variable, a total quantity variable, and an auxiliary variable, wherein the total state variable and the total quantity variable are initialized to zero, and the auxiliary variable is initialized to 2. The method includes the following steps:

[0013] Step S1: Each drone in the drone swarm system determines whether it meets the message sending conditions based on preset non-zero state constraints and topology memory constraints. The non-zero state constraint means that when at least one sub-state value of the drone is not zero, it is allowed to send a consistency update message to the outgoing neighbor. The topology memory constraint means that when the sum of all sub-states of the drone in the previous moment is zero and the only incoming neighbor in the previous moment is the outgoing neighbor determined in the current moment, it is prohibited to send a consistency update message to the outgoing neighbor.

[0014] Step S2: When the drone meets the message sending conditions, the drone randomly selects some non-zero sub-state values ​​from its local sub-state values, generates a consistency update message based on the selected non-zero sub-state values ​​and the auxiliary variable values ​​and sends it to its outgoing neighbor, and sets the selected non-zero sub-state values ​​and the auxiliary variable values ​​in the drone's local area to zero.

[0015] Step S3: When each drone in the drone swarm system receives a consistency update message, it updates its local total state variable value, total quantity variable value, and auxiliary variable value based on the non-zero sub-state value and auxiliary variable value carried in the consistency update message. Based on the updated total state variable value and total quantity variable value, it determines whether the drone swarm has reached a consensus on the state quantity of the target object. If a consensus is reached, it determines the consensus value of the state quantity based on the total state variable value and total quantity variable value.

[0016] According to a second aspect of the embodiments of this specification, a consistency control device for a drone swarm system is provided. The drone swarm system includes multiple drones, with at least one information transmission path existing between any two drones. Each drone collects an initial state value of at least one state variable of a target object and decomposes the initial state value into multiple sub-state values, each sub-state value being randomly generated locally by the drone. Each drone maintains a total state variable, a total quantity variable, and an auxiliary variable, wherein the total state variable and the total quantity variable are initialized to zero, and the auxiliary variable is initialized to 2. The device includes:

[0017] The trigger judgment unit is used by each drone in the drone swarm system to determine whether it meets the message sending conditions based on preset non-zero state constraints and topology memory constraints. The non-zero state constraint means that when at least one sub-state value of the drone is not zero, it is allowed to send a consistency update message to the outgoing neighbor. The topology memory constraint means that when the sum of all sub-states of the drone in the previous moment is zero and the only incoming neighbor in the previous moment is the outgoing neighbor determined in the current moment, it is prohibited to send a consistency update message to the outgoing neighbor.

[0018] The message sending unit is configured to, when the UAV meets the message sending conditions, randomly select a portion of non-zero sub-state values ​​from its local sub-state values, generate a consistency update message based on the selected non-zero sub-state values ​​and the auxiliary variable values ​​and send it to its outgoing neighbor, and set both the selected non-zero sub-state values ​​and the auxiliary variable values ​​locally of the UAV to zero.

[0019] The message receiving unit is used so that when each UAV in the UAV swarm system receives a consistency update message, it updates its local total state variable value, total quantity variable value, and auxiliary variable value based on the non-zero sub-state value and auxiliary variable value carried in the consistency update message. Based on the updated total state variable value and total quantity variable value, it determines whether the UAV swarm has reached a consensus on the state quantity of the target object. If a consensus has been reached, it determines the consensus value of the state quantity based on the total state variable value and total quantity variable value.

[0020] According to a third aspect of the embodiments of this specification, an electronic device is provided, including a processor; and a computer-readable storage medium storing computer program instructions that, when executed by the processor, cause the processor to perform the method described in the first aspect.

[0021] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being executed by a processor of the method described in the first aspect.

[0022] The embodiments of this application have at least the following technical effects:

[0023] (1) The embodiments of this application can significantly reduce communication redundancy and improve bandwidth utilization. The embodiments of this application introduce a non-zero state constraint, which allows communication only when the UAV has at least one non-zero sub-state value, thus suppressing invalid data transmission from the source; and introduce a topology memory constraint, which prohibits sending messages when the sum of all sub-states of the UAV in the previous time step is zero and the only incoming neighbor in the previous time step is the outgoing neighbor determined in the current time step, so as to avoid falling into a closed loop of invalid transmission;

[0024] (2) The embodiments of this application can get rid of the dependence on global information. Related schemes rely on global topology parameters such as the eigenvalues ​​of the Laplace matrix, which will limit the application of the scheme in large-scale dynamic networks. However, the embodiments of this application only rely on local state and neighbor information, without any global parameters, and can achieve fully distributed deployment;

[0025] (3) The embodiments of this application can enhance privacy protection capabilities and reduce exposure risks. The embodiments of this application, through random decomposition of state values ​​and dual constraint mechanism, reduce communication frequency while avoiding continuous exposure of sensitive state information, and can significantly improve the privacy security of the system under eavesdropping and inference attacks without introducing additional noise or encryption overhead;

[0026] (4) The embodiments of this application are applicable to resource-constrained scenarios. The dual constraint mechanism of the embodiments of this application is simple, requiring no complex auxiliary variables or continuous monitoring or predictive control modules. It has low computational complexity and low memory consumption, making it suitable for deployment on devices with limited computing and storage resources, such as drones and IoT nodes. Attached Figure Description

[0027] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Some specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings in an exemplary and non-limiting manner. The same reference numerals in the drawings indicate the same or similar parts or components. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0028] Figure 1 This is a flowchart illustrating a consistency control method for an unmanned aerial vehicle (UAV) swarm system according to an exemplary embodiment of this application;

[0029] Figure 2 This is a schematic diagram of the communication topology of a drone swarm, as illustrated in an exemplary embodiment of this application;

[0030] Figure 3 This is a schematic diagram illustrating the change curve of the total state variable value maintained by each UAV as a function of the number of iterations, as shown in an exemplary embodiment of this application.

[0031] Figure 4 This is a schematic diagram illustrating the change curve of the consensus value of each drone with the number of iterations, as shown in an exemplary embodiment of this application;

[0032] Figure 5 This is a schematic diagram illustrating the number of communications between various drones during the process of reaching a consensus, as shown in an exemplary embodiment of this application.

[0033] Figure 6This is a structural block diagram of an electronic device illustrated in an exemplary embodiment of this application;

[0034] Figure 7 This is a structural block diagram of a consistency control device for an unmanned aerial vehicle (UAV) swarm system, as illustrated in an exemplary embodiment of this application. Detailed Implementation

[0035] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0036] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0037] In the technical solution of this application, the UAV swarm system comprises a communication network consisting of N (N is a positive integer greater than 2) UAVs, where each UAV is represented by a node, and communication relationships are represented by directed edges. The communication topology of the UAV swarm system is a strongly connected directed graph, where there is at least one information transmission path between any two UAVs. Each UAV collects the initial state value of at least one state variable of the target object. The target object can be a drone in a drone swarm system or other physical targets. The state variables can be the drone's own physical state variables or control variables, or state variables of other physical targets, including but not limited to position, speed, power, trajectory parameters, or decision variables. To reduce the risk of state leakage during communication and to support fully distributed operation, this application embodiment decomposes and reconstructs the initial state value of each state variable. That is, this application embodiment decomposes the initial state value of each state variable into multiple sub-state values, each of which is randomly generated locally by the drone.

[0038] In some embodiments, each drone determines the number of sub-states corresponding to each state quantity based on the privacy level of that state quantity. The initial state value of the state variable Decompose into a corresponding number of substates ,in , here This represents the k-th sub-state value of the i-th drone, and the values ​​of each sub-state value are randomly generated locally by the drone. N max For a pre-set value, N for each drone max They can be the same or different.

[0039] To achieve consensus, the drone swarm system in this application requires each drone to maintain a total state variable, a total number variable, and auxiliary variables. For example, the total state variable of the i-th drone is... The total quantity variable is Auxiliary variables are The total state variable and the total quantity variable are both initialized to zero, and the auxiliary variable is initialized to 2.

[0040] In this embodiment, the total state variable and the total quantity variable are monotonically non-decreasing and bounded during the consistency control process. Combined with the strong connectivity and path memory mechanism of the UAV swarm system, it can be guaranteed that non-zero sub-state values ​​will eventually propagate to the entire communication network, so that the total state variable value of each UAV converges to the specified value. The total number of drones converges to 2N. Thus, when the total state variable and the total number variable converge, it can be determined that the drone swarm system has reached a consensus and achieved consistent control.

[0041] The embodiments described in this specification will now be described in detail.

[0042] This application provides a consistency control method for an unmanned aerial vehicle (UAV) swarm system. Figure 1 This is a flowchart illustrating a consistency control method for an unmanned aerial vehicle (UAV) swarm system according to an exemplary embodiment of this application, as shown below. Figure 1 As shown, the consistency control method includes at least the following steps:

[0043] Step S1: Each drone in the drone swarm system determines whether it meets the message sending conditions based on preset non-zero state constraints and topology memory constraints. The non-zero state constraint means that when at least one sub-state value of the drone is not zero, it is allowed to send a consistency update message to the outgoing neighbor. The topology memory constraint means that when the sum of all sub-states of the drone in the previous moment is zero and the only incoming neighbor in the previous moment is the outgoing neighbor determined in the current moment, it is prohibited to send a consistency update message to the outgoing neighbor.

[0044] In some embodiments, each drone determines whether it meets the message sending conditions at discrete moments. For example, it determines whether it meets the message sending conditions at preset intervals.

[0045] Step S2: When the drone meets the message sending conditions, the drone randomly selects some non-zero sub-state values ​​from its local sub-state values, generates a consistency update message based on the selected non-zero sub-state values ​​and the auxiliary variable values, and sends it to its outgoing neighbor, and sets the selected non-zero sub-state values ​​and the auxiliary variable values ​​in the drone's local environment to zero.

[0046] At the current moment, if drone i does not satisfy the topological memory constraint but satisfies the non-zero state constraint, then drone i is determined to satisfy the message sending condition; otherwise, if drone i satisfies the topological memory constraint or does not satisfy the non-zero state constraint, then drone i is determined to not satisfy the message sending condition.

[0047] The fact that drone i does not satisfy the topological memory constraint can be understood as follows: the sum of all substates of drone i in the previous time step is not zero, or the unique incoming neighbor of drone i in the previous time step is not the outgoing neighbor determined in the current time step; the fact that drone i satisfies the topological memory constraint can be understood as follows: the sum of all substates of drone i in the previous time step is zero and the unique incoming neighbor of drone i in the previous time step is the outgoing neighbor determined in the current time step.

[0048] The fact that drone i does not satisfy the non-zero state constraint can be understood as: all sub-state values ​​of drone i at the current time are zero; the fact that drone i satisfies the non-zero state constraint can be understood as: at least one sub-state value of drone i at the current time is not zero.

[0049] In some embodiments, each drone also maintains a trigger function. If and only if It is determined that UAV i satisfies the message sending condition at time k. That is, when UAV i does not satisfy the topological memory constraint but satisfies the non-zero state constraint, UAV i will... Set to 1; and when drone i satisfies the topological memory constraint, or does not satisfy the non-zero state constraint, drone i will... Set to zero.

[0050] Step S3: When each drone in the drone swarm system receives a consistency update message, it updates its local total state variable value, total quantity variable value, and auxiliary variable value based on the non-zero sub-state value and auxiliary variable value carried in the consistency update message. Based on the updated total state variable value and total quantity variable value, it determines whether the drone swarm has reached a consensus on the state quantity of the target object. If a consensus is reached, it determines the consensus value of the state quantity based on the total state variable value and total quantity variable value.

[0051] based on Figure 1As illustrated by the consistency control method, this embodiment of the application, under the condition of maintaining strong connectivity communication in the UAV swarm system, ensures that the messages exchanged between the various UAVs in the system are valid based on the non-zero state constraint and topological memory constraint. After a finite number of exchanges, the sub-state values ​​of all UAVs are propagated globally, thereby converging the consensus value of the state variables of all UAVs in the system towards the target object to the average value of the initial state values. This embodiment, through the non-zero state constraint and topological memory constraint, avoids an infinite number of message interactions and possesses Zeno-free behavior characteristics.

[0052] When the number of drones in a drone swarm system is N, the upper bound of the number of substates of all drones is N. max The topological diameter of the communication network is D. Considering the worst-case scenario of system convergence, a certain substate needs to traverse the longest path D, and each hop may be delayed by N because nodes need to send their own substates. max In this step, the last node needs to receive consistent update information from all the remaining N-1 drones, so the total number of iterations can be calculated to be no more than (N-1)*N. max *D.

[0053] In some embodiments, to prevent consistent update messages from looping infinitely in the topology path, each drone also maintains a path queue, which records the identifiers of drones that have participated in message passing, and the capacity of the path queue is the number N of drones in the drone swarm system.

[0054] Accordingly, step S3 includes: when the UAV receives the consistency update message, writing the UAV identifier that generated the consistency update message into the tail of the path queue, and when the size of the path queue reaches the upper limit of the queue capacity, performing a dequeue operation on the UAV identifier at the head of the queue according to the first-in-first-out principle.

[0055] It is worth noting that if the drone does not receive a consistency update message at the current moment, the path queue it maintains remains unchanged.

[0056] In some embodiments, step S2 determines outgoing neighbors by: randomly selecting outgoing neighbors from those that have never appeared in the path queue when sending a consistency update message; if all neighbors appear in the path queue, then selecting the neighbor that entered the path queue earliest as the outgoing neighbor. Thus, this embodiment prioritizes directing consistency update messages to the drones that have not been visited recently to avoid the consistency update messages getting stuck in an infinite loop under strongly connected topology conditions.

[0057] In some embodiments, since each UAV can decompose its initial state value into a different number of sub-state values, to facilitate consensus calculation during the consistency control process, this embodiment categorizes and accumulates the sub-state values ​​in the consistency update message according to the parity of their indices, in order to support distributed consensus calculation in subsequent steps. Specifically, the consistency update message also carries the sub-state index of the selected sub-state value.

[0058] Accordingly, step S3 updates the local sub-state value through the following steps: When the UAV receives the consistency update message, it determines the parity of the sub-state index of each sub-state carried in the consistency update message; if the sub-state index is odd, the sub-state value corresponding to the odd sub-state index is added to the UAV's local sub-state value with sub-state index 1, and the sum is updated to the new sub-state value corresponding to sub-state index 1; if the sub-state index is even, the sub-state value corresponding to the even sub-state index is added to the UAV's local sub-state value with sub-state index 2, and the sum is updated to the new sub-state value corresponding to sub-state index 2.

[0059] Accordingly, the maximum value among the sum of all updated sub-state values ​​and the total state variable value from the previous time step is used to update the total state variable value at the current time step.

[0060] In some embodiments, step S3 updates the local auxiliary variable value and the total quantity variable value through the following steps: when the UAV receives the consistency update message, it adds the auxiliary variable value carried in the consistency update message to the UAV's local auxiliary variable value, and after updating the sum to the UAV's new local auxiliary variable value, it updates the maximum value between the new auxiliary variable value and the total quantity variable value at the previous moment to the current total quantity variable value.

[0061] In this embodiment, the total state variable value and the total quantity variable value can be updated using the following formula:

[0062]

[0063]

[0064] In the above formula The expression represents the sub-state and value calculated by UAV i at time k based on the updated local sub-state value. This represents the new auxiliary variable value of drone i at time k.

[0065] In some embodiments, step S3 determines whether a consensus has been reached by the following steps: if the updated total state variable value converges and the updated total quantity variable value converges, it is determined whether the drone cluster has reached a consensus on the state quantity of the target object.

[0066] The drone i calculates , At that time, it is determined whether the drone cluster has reached a consensus on the state quantity of the target object.

[0067] In some embodiments, step S3 calculates the consensus value by the following steps: when a consensus is reached, the ratio of the converged total state variable value to the converged total quantity variable value is determined as the consensus value of the state variable.

[0068] In this embodiment, the consensus value of the state quantity can be calculated using the following formula. :

[0069]

[0070] In the event of a consensus, the consensus value The average value converges to the initial state value, which satisfies the following formula:

[0071]

[0072] Next, the consistency control process of the embodiments of this application will be described in detail.

[0073] In this embodiment, it is assumed that the drone swarm system includes 6 drones, and the communication topology is as follows: Figure 2 As shown, the strongly connected valid graph assumption is satisfied. Each UAV collects the initial value of a certain state of interest of the target object. For example, the target object is a building, and the state of interest is information such as the building's location and height.

[0074] First, perform system initialization.

[0075] Assume the initial state values ​​collected by each drone are as follows:

[0076] In other words, the consensus value of the system's interest state regarding this building should be: 7.

[0077] Each drone randomly selects the number of substates within the range [2, Nmax]. For example, the number of substates of drone i Each drone randomly decomposes its initial state value into... Sub-state values The decomposed sub-state values ​​satisfy the numerical conservation condition. .

[0078] For example, the decomposition process of the initial state value can be implemented using a local pseudo-random number generator to ensure the privacy of individual sub-state values.

[0079] Each drone also creates a total state variable. Total quantity variable Auxiliary variables And a path queue Q, which is initially empty and has a maximum capacity of 6. This path queue is used to record the identifiers of drones that have participated in message passing.

[0080] Next, determine whether the message sending conditions are met.

[0081] At each discrete time k, UAV i determines whether the message sending condition is met based on the non-zero state constraint and topological memory constraint. If the message sending condition is met, the function is triggered. Set to 1. This will trigger the function if the message sending conditions are not met. Set to zero.

[0082] Then, send the message.

[0083] Determine the difference between the outgoing neighbor set of UAV i and the path queue Q. If the difference is empty, select the earliest neighbor to enter the path queue Q as the outgoing neighbor; if the difference is not empty, randomly select a neighbor from the difference as the outgoing neighbor.

[0084] Furthermore, a set of non-zero sub-state values ​​is randomly selected from the local sub-state set of UAV i to generate a consistency update message. For example, if UAV i has five non-zero sub-states, the first, third, and fifth non-zero sub-states are randomly selected. For each selected non-zero sub-state, a consistency update message is constructed based on the non-zero sub-state value, the index value, and the auxiliary variable value of UAV i, and this consistency update message is sent to the determined outgoing neighbor. In other words, the consistency update message in this embodiment carries the non-zero sub-state value, the index of the non-zero sub-state, and the local auxiliary variable value of UAV i.

[0085] After generating or sending the consistency update message, the first, third, and fifth non-zero sub-state values ​​of UAV i are all set to zero, and the auxiliary variable values ​​of UAV i are also set to 0, in order to maintain the conservation of the total number of system states.

[0086] Finally, the drone receives the consistency update message.

[0087] When drone j receives a consistency update message, it writes the identifier of drone i to the tail of the path queue Q of drone j, and adds the non-zero sub-state value carried in the consistency update message to either the first or second sub-state value of drone j according to the parity of its index; and adds the auxiliary variable value carried in the consistency update message to the auxiliary variable value of drone j, and updates the sum to the new auxiliary variable value of drone j.

[0088] Upon receiving the consistency update message, UAV j also updates its local total state variable and total quantity variable values. Specifically, it updates the maximum value between the sum of all updated sub-state values ​​and the total state variable value from the previous time step to the current total state variable value. It also updates the maximum value between the new auxiliary variable value and the total quantity variable value from the previous time step to the current total quantity variable value. This update rule ensures that the total state variable... With total quantity variable Monotonous and undiminished.

[0089] In the drone swarm system of this embodiment, the total state variable Finally, under this update rule, it converges to the initial state value and 42 in about 28 iterations, and its change curve is as follows. Figure 3 As shown.

[0090] As consistent update messages are transmitted through the system, Eventually converges to the consensus value . The change curve is as follows Figure 4 As shown, the actual number of times each drone triggered communication is as follows: Figure 5 As shown, the total number of triggers was only 46.

[0091] As can be seen from the above embodiments of this application, the consensus control scheme of this application can achieve efficient and low-communication-overhead consensus convergence in a fully distributed environment, while ensuring system privacy and security and avoiding invalid state problems. Specifically, the embodiments of this application can significantly reduce communication redundancy and improve bandwidth utilization. Existing event triggering mechanisms mostly rely on error threshold judgment and cannot identify redundant transmissions caused by invalid states. The embodiments of this application introduce non-zero state constraints, which can suppress invalid data transmission from the source. The embodiments of this application can optimize communication path selection and improve system convergence speed. Traditional methods lack the use of historical information in neighbor selection and are prone to getting trapped in local loops. However, the queue-based topology memory mechanism designed in the embodiments of this application dynamically records message transmission paths and prioritizes unvisited neighbors, effectively avoiding closed-loop transmission and path duplication. Furthermore, most related event triggering methods rely on global topology parameters such as Laplace matrix eigenvalues, which limits their application in large-scale dynamic networks. However, the triggering function proposed in the embodiments of this application only relies on local state and neighbor information, without any global parameters, and can achieve fully distributed deployment. This application embodiment also employs a random state decomposition and dual constraint mechanism to reduce communication frequency while avoiding continuous exposure of sensitive state information. Compared to periodic communication or differential privacy methods, this application embodiment significantly improves the privacy security of the system under eavesdropping and inference attacks without introducing additional noise or encryption overhead. Furthermore, this application embodiment has low computational complexity and low memory consumption, making it particularly suitable for resource-constrained scenarios.

[0092] Figure 6 This is a schematic diagram of an electronic device illustrated in this specification according to an exemplary embodiment. Please refer to... Figure 6 At the hardware level, the device includes a processor 602, an internal bus 604, a network interface 606, memory 608, a hardware acceleration device 610, and non-volatile memory 612, and may also include other hardware required for its functions. One or more embodiments of this application can be implemented in software, for example, the processor 602 reads the corresponding computer program from the non-volatile memory 612 into memory 608 and then runs it. Of course, in addition to software implementation, one or more embodiments of this application do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the above processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0093] Figure 7 This is a structural block diagram illustrating a consistency control device for a drone swarm system according to an exemplary embodiment of this application. The consistency control device can be applied to, for example... Figure 6The electronic device shown implements the technical solution of this application. The consistency control device includes: a trigger judgment unit 710, a message sending unit 720, and a message receiving unit 730, wherein:

[0094] The trigger judgment unit 710 is used by each UAV in the UAV swarm system to determine whether it meets the message sending conditions based on preset non-zero state constraints and topology memory constraints. The non-zero state constraint means that when at least one sub-state value of the UAV is not zero, it is allowed to send a consistency update message to the outgoing neighbor. The topology memory constraint means that when the sum of all sub-states of the UAV at the previous moment is zero and the only incoming neighbor at the previous moment is the outgoing neighbor determined at the current moment, it is prohibited to send a consistency update message to the outgoing neighbor.

[0095] The message sending unit 720 is configured to, when the UAV meets the message sending conditions, randomly select a portion of non-zero sub-state values ​​from its local sub-state values, generate a consistency update message based on the selected non-zero sub-state values ​​and the auxiliary variable values ​​and send it to its outgoing neighbor, and set both the selected non-zero sub-state values ​​and the auxiliary variable values ​​locally of the UAV to zero.

[0096] The message receiving unit 730 is configured to, when each UAV in the UAV swarm system receives a consistency update message, update its local total state variable value, total quantity variable value, and auxiliary variable value based on the non-zero sub-state value and auxiliary variable value carried in the consistency update message; determine whether the UAV swarm has reached a consensus on the state quantity of the target object based on the updated total state variable value and total quantity variable value; and, if a consensus has been reached, determine the consensus value of the state quantity based on the total state variable value and total quantity variable value.

[0097] In some embodiments, each drone also maintains a path queue, the capacity of which is the number of drones in the drone swarm system, and the message receiving unit 730 includes a queue update module.

[0098] The queue update module is used to write the drone identifier that generated the consistency update message to the tail of the path queue when the drone receives the consistency update message, and to perform a dequeue operation on the drone identifier at the head of the queue according to the first-in-first-out principle when the size of the path queue reaches the upper limit of the queue capacity.

[0099] In some embodiments, the message receiving unit 730 includes a neighbor selection module;

[0100] The neighbor selection module is used to randomly select an out neighbor from the neighbors that have never appeared in the path queue when sending a consistency update message; if all neighbors appear in the path queue, the neighbor that entered the path queue earliest is selected as the out neighbor.

[0101] In some embodiments, the consistency update message also carries a sub-state index of the selected sub-state value, and the message receiving unit 730 includes an update module;

[0102] The update module is used to determine the parity of the sub-state index of each sub-state carried in the consistency update message when the UAV receives the consistency update message; if the sub-state index is odd, the sub-state value corresponding to the odd sub-state index is added to the UAV's local sub-state value with sub-state index 1, and the sum is updated to the new sub-state value corresponding to sub-state index 1; if the sub-state index is even, the sub-state value corresponding to the even sub-state index is added to the UAV's local sub-state value with sub-state index 2, and the sum is updated to the new sub-state value corresponding to sub-state index 2.

[0103] In some embodiments, the updating module is further configured to update the maximum value among the sum of all updated sub-state values ​​and the total state variable value at the previous time step to the total state variable value at the current time step.

[0104] In some embodiments, the update module is further configured to, when the UAV receives a consistency update message, add the auxiliary variable value carried in the consistency update message to the UAV's local auxiliary variable value, and after updating the sum to the new auxiliary variable value of the UAV, update the maximum value between the new auxiliary variable value and the total quantity variable value at the previous moment to the total quantity variable value at the current moment.

[0105] In some embodiments, the message receiving unit 730 includes a consensus calculation module;

[0106] The consensus calculation module is used to determine whether the drone cluster has reached a consensus on the state variables of the target object, provided that the updated total state variable value and the updated total quantity variable value have converged.

[0107] In some embodiments, each drone decomposes the initial state value into a sum of multiple sub-state values, which is half of the initial state value. The consensus calculation module is further configured to determine the ratio of the converged total state variable value to the converged total quantity variable value as the consensus value of the state quantity when a consensus is reached.

[0108] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0109] Accordingly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods described in any of the above embodiments.

[0110] Accordingly, embodiments of this application also provide a computer program product configured to perform the methods described in any of the above embodiments.

[0111] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0112] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0113] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0114] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0115] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0116] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0117] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0118] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0119] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A consistency control method for an unmanned aerial vehicle (UAV) swarm system, characterized in that, The drone swarm system includes multiple drones, with at least one information transmission path between any two drones. Each drone collects the initial state value of at least one state variable of the target object and decomposes the initial state value into multiple sub-state values, each sub-state value being randomly generated locally by the drone. Each drone maintains a total state variable, a total quantity variable, and an auxiliary variable, with the total state variable and total quantity variable initialized to zero, and the auxiliary variable initialized to 2. The method includes the following steps: Step S1: Each drone in the drone swarm system determines whether it meets the message sending conditions based on preset non-zero state constraints and topological memory constraints; the non-zero state constraint means that when at least one sub-state value of the drone is not zero, it is allowed to send a consistency update message to its outgoing neighbor. The topological memory constraint means that when the sum of all sub-states of the UAV in the previous moment is zero and the only incoming neighbor in the previous moment is the outgoing neighbor determined in the current moment, it is prohibited to send consistency update messages to the outgoing neighbor. Step S2: When the drone meets the message sending conditions, the drone randomly selects some non-zero sub-state values ​​from its local sub-state values, generates a consistency update message based on the selected non-zero sub-state values ​​and the auxiliary variable values ​​and sends it to its outgoing neighbor, and sets the selected non-zero sub-state values ​​and the auxiliary variable values ​​in the drone's local area to zero. Step S3: When each drone in the drone swarm system receives a consistency update message, it updates its local total state variable value, total quantity variable value, and auxiliary variable value based on the non-zero sub-state value and auxiliary variable value carried in the consistency update message. Based on the updated total state variable value and total quantity variable value, it determines whether the drone swarm has reached a consensus on the state quantity of the target object. If a consensus is reached, it determines the consensus value of the state quantity based on the total state variable value and total quantity variable value.

2. The method according to claim 1, characterized in that, Each drone also maintains a path queue, the capacity of which is the number of drones in the drone swarm system. Step S3 includes: When a drone receives a consistency update message, it writes the drone identifier that generated the consistency update message to the tail of the path queue. If the size of the path queue reaches the upper limit of the queue capacity, it performs a dequeue operation on the drone identifier at the head of the queue according to the first-in-first-out principle.

3. The method according to claim 2, characterized in that, Step S2 includes: When sending a consistency update message, a neighbor is randomly selected from the neighbors that have never appeared in the path queue; if all neighbors appear in the path queue, the neighbor that entered the path queue earliest is selected as the outgoing neighbor.

4. The method according to claim 1, characterized in that, The consistency update message also carries a sub-state index of the selected sub-state value, and step S3 includes: When the UAV receives a consistency update message, it determines the parity of the sub-state index of each sub-state carried in the consistency update message; If the sub-state index is odd, then the sub-state value corresponding to the odd sub-state index is added to the sub-state value of the UAV with sub-state index 1, and the sum is updated to the new sub-state value corresponding to sub-state index 1. If the sub-state index is even, then the sub-state value corresponding to the even sub-state index is added to the sub-state value with sub-state index 2 in the local sub-state of the UAV, and the sum is updated to the new sub-state value corresponding to sub-state index 2.

5. The method according to claim 4, characterized in that, Step S3 includes: The sum of all updated substate values ​​and the maximum value among the total state variable values ​​from the previous time step are used to update the total state variable value at the current time step.

6. The method according to claim 1, characterized in that, Step S3 includes: When the UAV receives a consistency update message, it adds the auxiliary variable value carried in the consistency update message to the UAV's local auxiliary variable value. After updating the sum to the new auxiliary variable value of the UAV, it updates the maximum value between the new auxiliary variable value and the total quantity variable value of the previous time step to the total quantity variable value of the current time step.

7. The method according to claim 1, characterized in that, Step S3 includes: If the updated total state variable value converges and the updated total quantity variable value converges, determine whether the drone cluster has reached a consensus on the state quantity of the target object.

8. The method according to claim 1, characterized in that, Each drone decomposes the initial state value into multiple sub-state values, the sum of which is half of the initial state value. Step S3 includes: When a consensus is reached, the ratio of the converged total state variable value to the converged total quantity variable value is determined as the consensus value of the state variable.

9. A consistency control device for an unmanned aerial vehicle (UAV) swarm system, characterized in that, The drone swarm system includes multiple drones, with at least one information transmission path between any two drones. Each drone collects the initial state value of at least one state variable of the target object and decomposes the initial state value into multiple sub-state values, each of which is randomly generated locally by the drone. Each drone also maintains a total state variable, a total quantity variable, and an auxiliary variable. The total state variable and the total quantity variable are initialized to zero, and the auxiliary variable is initialized to 2. The device includes: The trigger judgment unit is used by each drone in the drone swarm system to determine whether it meets the message sending conditions based on preset non-zero state constraints and topology memory constraints. The non-zero state constraint means that when at least one sub-state value of the drone is not zero, it is allowed to send a consistency update message to the outgoing neighbor. The topology memory constraint means that when the sum of all sub-states of the drone in the previous moment is zero and the only incoming neighbor in the previous moment is the outgoing neighbor determined in the current moment, it is prohibited to send a consistency update message to the outgoing neighbor. The message sending unit is configured to, when the UAV meets the message sending conditions, randomly select a portion of non-zero sub-state values ​​from its local sub-state values, generate a consistency update message based on the selected non-zero sub-state values ​​and the auxiliary variable values ​​and send it to its outgoing neighbor, and set both the selected non-zero sub-state values ​​and the auxiliary variable values ​​locally of the UAV to zero. The message receiving unit is used so that when each UAV in the UAV swarm system receives a consistency update message, it updates its local total state variable value, total quantity variable value, and auxiliary variable value based on the non-zero sub-state value and auxiliary variable value carried in the consistency update message. Based on the updated total state variable value and total quantity variable value, it determines whether the UAV swarm has reached a consensus on the state quantity of the target object. If a consensus has been reached, it determines the consensus value of the state quantity based on the total state variable value and total quantity variable value.

10. An electronic device, characterized in that, include: processor; A computer-readable storage medium storing computer program instructions that, when executed by the processor, cause the processor to perform the method as described in any one of claims 1 to 8.

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