Unmanned aerial vehicle swarm management and control method and device based on swarm intelligence emergence elements

Abstract

The present disclosure provides a UAV swarm management and control method based on swarm intelligence emergent elements, the swarm intelligence emergent elements including autonomous individual, common behavior rule, expected environmental entropy input, and feedback action between swarm and individual, the method including: detecting the aggregation degree of the UAV swarm to evaluate the autonomy of the individual UAV in the swarm; analyzing the motion direction accuracy of the swarm to evaluate the behavior rule of the swarm; perceiving the external environment where the swarm is located to determine the interference signal to be applied to the swarm as the environmental entropy input; constructing the feedback input to be perceived by the swarm to change the feedback action between the swarm and the individual UAV; and determining the management and control strategy for the UAV swarm based on the aggregation degree of the swarm, the motion direction accuracy of the swarm, the interference signal to be applied, and the constructed feedback input, wherein the management and control strategy is determined to destroy the swarm intelligence emergent elements of the swarm, thereby counteracting the swarm intelligence of the UAV swarm.

Description

Technical Field

[0001] This disclosure relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, apparatus, equipment, medium, and program product for UAV swarm management based on emergent swarm intelligence elements. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are widely used in various fields of modern technology due to their advantages such as high mobility, rapid deployment, low cost, and low manpower requirements. This gives them significant advantages in terms of size, weight, range, flight time, flight altitude, flight speed, and mission capabilities. Currently, control measures for UAV swarms are extremely limited and therefore urgently need improvement.

[0003] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for managing unmanned aerial vehicle (UAV) swarms based on emergent swarm intelligence elements.

[0005] According to one aspect of this disclosure, a method for managing a drone swarm based on emerging elements of swarm intelligence is provided. These emerging elements include autonomous individuals, shared behavioral rules, desired environmental entropy input, and feedback mechanisms between the swarm and individuals. The method includes: detecting the aggregation degree of the drone swarm to assess the autonomy of individual drones within the swarm; analyzing the accuracy of the drone swarm's movement direction to assess its behavioral rules; sensing the external environment of the drone swarm to determine an interference signal to be applied to the drone swarm as environmental entropy input; constructing a feedback input to be perceived by the drone swarm to alter the feedback mechanisms between the drone swarm and individual drones; and determining a management strategy for the drone swarm based on the aggregation degree, the accuracy of the drone swarm's movement direction, the interference signal to be applied, and the constructed feedback input. The management strategy is determined to disrupt the emerging elements of swarm intelligence, thereby counteracting the swarm intelligence of the drone swarm.

[0006] According to another aspect of this disclosure, an apparatus for managing a drone swarm is provided, comprising: a detection module configured to detect the degree of aggregation of the drone swarm to assess the autonomy of individual drones within the swarm; an analysis module configured to analyze the accuracy of the movement direction of the drone swarm to assess the behavioral rules of the drone swarm; a perception module configured to perceive the external environment in which the drone swarm is located to determine an interference signal to be applied to the drone swarm as an environmental entropy input; a construction module configured to construct a feedback input to be perceived by the drone swarm to alter the feedback interaction between the drone swarm and individual drones; and a determination module configured to determine a management strategy for the drone swarm based on the degree of aggregation of the drone swarm, the accuracy of the movement direction of the drone swarm, and the constructed feedback input.

[0007] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to said at least one processor; wherein said memory stores a computer program that, when executed by said at least one processor, implements the above-described method.

[0008] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing a computer program, wherein the computer program implements the above-described method when executed by a processor.

[0009] According to another aspect of this disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the above-described method when executed by a processor.

[0010] According to one or more embodiments of this disclosure, a method for managing and controlling unmanned aerial vehicle (UAV) swarms based on swarm intelligence emergence factors is provided. This method analyzes the generation mechanism of swarm intelligence and swarm emergence factors from the bottom layer based on system dynamics, and further utilizes swarm emergence factors to achieve effective management and control of UAV swarm swarm intelligence.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] The accompanying drawings exemplify embodiments and form part of the specification, serving to explain exemplary implementations of the embodiments together with the textual description. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements. In the drawings:

[0013] Figure 1 A flowchart of a method for managing unmanned aerial vehicle (UAV) swarms based on swarm intelligence emergent elements according to an embodiment of the present disclosure is shown;

[0014] Figure 2 A flowchart illustrating a method for determining an interference signal to be applied as an environmental entropy input to a swarm of unmanned aerial vehicles (UAVs) according to an embodiment of this disclosure is shown.

[0015] Figure 3 A schematic diagram illustrating the feedback mechanism between a group and individuals within the group according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A block diagram of an apparatus for managing a swarm of unmanned aerial vehicles (UAVs) according to an embodiment of the present disclosure is shown; and

[0017] Figure 5 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0018] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0020] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0021] The term "group" is used in contrast to "individual" to refer to a community of individuals. Different individuals come together according to certain elemental characteristics to form a group. Groups include single-element groups and heterogeneous groups. A group is not simply defined as the sum of its parts. Different groups vary greatly in type, size, scale, and nature, exhibiting different external forms.

[0022] The term "emergence" refers to the process by which new properties and phenomena suddenly emerge at the system level when units within a system follow simple rules and interact locally to form a whole. This process is not a simple summation of the behaviors of individual systems, but rather an emergence resulting from the interactions between individuals. When the number of individuals reaches a certain level, based on synergetics theory, under certain conditions, they can all exhibit a macroscopically ordered structure, demonstrating the effect of swarm intelligence.

[0023] The inventors recognized that during foraging, ants are capable of finding the shortest path from their nest to a food source without any visible cues, and can dynamically search for new paths as the environment changes, thus generating new choices. Individual ants can sense the presence and intensity of pheromones secreted by other ants and use this as a navigation route, causing the colony to continuously move towards areas with higher pheromone concentrations. As a positive feedback phenomenon, the probability of the colony choosing a particular path gradually increases with the number of individual ants. When the number of ants on a path becomes too large, some individuals will choose other paths, generating negative feedback, reducing the density of ants on the original path, and creating competition with the original path. As self-organizing individuals, ants exhibit different queue behaviors during coordinated movement through information transmission (visual signals, sound signals, tactile signals, limb movements, and pheromones, etc.), enabling them to overcome obstacles, quickly adapt to their surroundings, and find the optimal path during foraging. Therefore, four emergent elements can be summarized from ant foraging behavior: autonomous individuals, shared behavioral rules, expected environmental entropy input, and feedback between the group and individuals. However, the drone swarm management methods in related technologies have failed to take into account the emergent nature of drone swarms as intelligent groups.

[0024] To address the aforementioned technical problems, a novel method for managing and controlling unmanned aerial vehicle (UAV) swarms is proposed based on one or more embodiments of this disclosure.

[0025] This method differs from related technologies in managing drone swarms. It analyzes the generation mechanism and emergence factors of swarm intelligence from a fundamental level based on system dynamics, and further utilizes these factors to manage the swarm intelligence of drone swarms. This method significantly expands the factors that need to be considered in managing drone swarms and can suppress the emergence of drone swarms as intelligent groups, thereby achieving effective management of drone swarms.

[0026] Exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0027] Figure 1A flowchart of a drone swarm management method 100 based on swarm intelligence emergent elements according to an embodiment of this disclosure is shown. As described above, swarm intelligence emergent elements herein include autonomous individuals, shared behavioral rules, desired environmental entropy input, and feedback effects between the swarm and individuals. The following references... Figure 1 Detailed description of each step of method 100:

[0028] In step S110, the degree of aggregation of the drone swarm is detected to assess the autonomy of individual drones within the drone swarm.

[0029] As the foundation of swarm intelligence emergence, a large number of autonomous individuals exhibit their own behavioral logic, independent of the complete control of the upper-level system. If there are differences among individuals within the current group (i.e., each individual possesses individual exploratory capabilities), a self-organizing group is formed. When the number of individuals in the group is small, group autonomy is not easily identified, and emergence is unlikely. When the autonomy of individuals within the group is strong, the emergent behavior of the group is not easily predicted.

[0030] The autonomy of individuals within a group manifests as the differences in their characteristics. In one example, the group's dissimilarity can be calculated based on a measure of an individual's distance from the group's mean center, using the following formula:

[0031]

[0032] Where |P| represents the population size, |R| represents the maximum diagonal length of the search space, N represents the dimension of the search space, and s ij This represents the j-th dimension component of individual i within the group. This represents the j-th dimension component of the average center of the population at the current iteration. This method of measuring population dissimilarity is independent of the population size and the size of the search space, and can well describe the density of the distribution of individuals within the population.

[0033] In some embodiments, detecting the aggregation degree of the drone swarm to assess the autonomy of individual drones within the swarm includes calculating the aggregation degree of the drone swarm at the current time t using the following formula:

[0034]

[0035] Where i is the ID of the individual drone, N is the total number of individual drones in the drone swarm, and M is the number of drones in the swarm. i，side Let G(t) be the number of other individual drones within the communication radius of the i-th individual drone. The greater the aggregation degree G(t) of the drone swarm, the higher the autonomy of the individual drones within the swarm.

[0036] The higher the calculated aggregation degree G(t), the better the distribution of the current drone swarm, indicating a higher degree of autonomy among the individual drones within the swarm. This necessitates stronger control measures for the drone swarm. Therefore, when individual drones within a swarm exhibit high autonomy, technologies such as data links can be used to take over one or more drones within the swarm to achieve control and capture, thereby altering the autonomy of individual drones and achieving the goal of controlling (e.g., countering) the drone swarm.

[0037] In some embodiments, information about the degree of aggregation of the drone swarm can also be obtained through detection devices (e.g., radar detection devices, infrared detection devices, etc.).

[0038] In step S120, the accuracy of the movement direction of the drone swarm is analyzed to evaluate the behavior rules of the drone swarm.

[0039] Behavioral rules, also known as the set of behavioral rules, are a necessary condition for the emergence of swarm intelligence. Different individuals choosing different behavioral rules will lead to different emergent results. As the complexity of individuals and their choices of behavioral rules increases, the behavioral rules of the swarm are continuously expanded, resulting in a diversification of emergent outputs. However, when individuals are completely independent (i.e., each individual's behavioral rules do not overlap with each other), emergence is impossible. Therefore, the basis for emergence is a common set of behaviors based on overlapping behavioral rules of individuals.

[0040] Therefore, it is necessary to analyze the behavioral rules of drone swarms. In related technologies, to achieve decentralized and stable cluster structures, most drone swarm operations employ a hierarchical, echeloned approach, which results in a relatively even distribution of value among individual drones within the swarm. Consequently, the "capture the leader" tactic will not yield the expected results. Nevertheless, when such drone swarms encounter a target, they typically follow a priority strategy of first selecting target A (e.g., an intact target) and then target B (e.g., the closest target), leading to an uneven distribution of value among individual drones within the swarm. That is, some individual drones will act as leader drones, communicating with neighboring drones, and the resulting attack swarm will move towards the target.

[0041] In some embodiments, analyzing the accuracy of the drone swarm's motion direction to evaluate the drone swarm's behavior rules includes calculating the drone swarm's deviation angle at the current time t using the following formula:

[0042]

[0043] Where, p iThe leader drone is responsible for communicating with neighboring drones, indicating its preferred direction. i (t) is the position vector of the i-th individual UAV at the current time t, w i (t+Δt) represents the position vector of the i-th individual drone at the next long time interval t+Δt. The smaller the deviation angle θ of the drone swarm, the higher the accuracy of the swarm's movement direction and the more similar the behavior rules of the individual drones. Therefore, when the behavior rules of individual drones are highly similar, the accurate position of the drone swarm in the future can be predicted. By using the method of splitting individual behaviors, higher-value individual drones within the swarm can be selected for control measures.

[0044] In step S130, the external environment of the drone swarm is sensed to determine the interference signal to be applied to the drone swarm as environmental entropy input.

[0045] As a catalyst for the emergence of swarm intelligence, individuals within a group dynamically load different behavioral rules to cope with changing environmental conditions, resulting in new emergent effects through interactions with the environment and among individuals. Therefore, environmental entropy input becomes one of the key factors influencing group vulnerability. Due to the self-organization and robustness within a group, environmental entropy input will lead to the emergence of different expressive abilities within the group.

[0046] Figure 2 A flowchart of a method 200 for determining interference signals to be applied as environmental entropy input to a swarm of unmanned aerial vehicles (UAVs) according to an embodiment of this disclosure is shown. Reference is made below. Figure 2 Describe each step of method 200 in detail.

[0047] In step S210, the interference factors in the external environment of the drone swarm are determined.

[0048] In some embodiments, interference factors targeting drone swarms can be used to block environmental entropy channels, disrupt information interaction between individuals within the swarm, alter environmental entropy feedback paths, or influence the topology of the swarm, thereby affecting communication between individuals and the environment, and between individuals and the swarm, and thus enabling control over swarm intelligence.

[0049] In step S220, an interference signal corresponding to the interference factor is determined from a pre-built regional reconnaissance and early warning system for application to the UAV swarm.

[0050] To minimize interference from unknown factors and the intervention of uncontrollable factors, it is advisable to construct a regional reconnaissance and early warning system. This would create a distributed structure for multi-directional and multi-source information identification, taking into account costs, thereby improving battlefield environmental awareness.

[0051] In some embodiments, the determined interference signals to be applied to the drone swarm are configured to control the input environmental entropy of the drone swarm, for example, by using microwave means, laser means, etc. to interfere with the drones' detection and identification capabilities, thereby weakening and suppressing their communication, detection, and command capabilities.

[0052] Return to reference Figure 1 In step S140, a feedback input to be perceived by the drone swarm is constructed to change the feedback relationship between the drone swarm and individual drones.

[0053] Feedback between groups and individuals is also one of the key factors in the emergence of swarm intelligence. Figure 3 A schematic diagram of a feedback mechanism 300 between a group and individuals within the group according to an embodiment of the present disclosure is shown.

[0054] like Figure 3 As shown, as input parameters for individual operation, environmental entropy input 301 (which may or may not include the interference signal to be applied to the individual as determined in step S220 of method 200) is fed to individual operation 302. Individual operation 302 assigns corresponding environmental entropy inputs to each individual 303 (e.g., individual 1, individual 2, individual 3... individual n) within the group to enable the individual within the group to operate. The output signals of different individuals (e.g., y1, y2, y3, and y4) are input to the group 304 as different feedforward branches for processing. Accordingly, the signal generated after multi-path superposition (e.g., the group state signal y) is collected and then fed back to the individual input via feedback processing 306. It should be noted that this feedback is non-linear, and it will interact between individuals, and this effect can iterate continuously, resulting in more phenomena.

[0055] In some embodiments, the emergence of swarm intelligence can be suppressed by weakening swarm intelligence through means such as reducing effective feedback and providing false negative feedback.

[0056] refer to Figure 3 The described feedback mechanism is also based on the behavioral rules of individuals within the group. As a result of the emergent nature of swarm intelligence, drone swarms exhibit the characteristic of selecting targets based on their integrity. When the perceived integrity of a target object rapidly declines, some autonomous individual drones within the swarm will proactively choose other targets. If the perception system of the drone swarm can be interfered with, causing it to perceive erroneous information, the distributed structure of the swarm can be reduced, thereby enabling control over it. Typically, when taking action, drone swarms exhibit the characteristic of selecting targets with high tolerance; after perceiving the maximum tolerance of a target, they will dispatch individual drones corresponding to that maximum tolerance to take action against that target.

[0057] There are several countermeasures: First, attract drone swarms to send out individual drones at a rate lower than their own capacity, so as to at least effectively control these individual drones; second, attract drone swarms to send out more individual drones at a rate higher than their own capacity, thereby reducing the aggregation of drone swarms; third, create false targets to attract drone swarms to take action, and so on.

[0058] In some embodiments, constructing feedback inputs that will be perceived by the drone swarm to alter the feedback interaction between the drone swarm and individual drones includes constructing feedback inputs that will be perceived by the drone swarm based on the evaluated behavioral rules of the drone swarm. The feedback inputs include creating false targets and / or decoy targets to attract the drone swarm to take action against the false targets and / or decoy targets (e.g., infrared decoys). Thus, by adjusting the real feedback inputs of the drone swarm and / or establishing false negative feedback, it is possible to achieve cost-effective and efficient control of at least some individual drones, reducing the density of drone swarm aggregation and thereby facilitating precise control of individual drones.

[0059] Return to reference Figure 1 In step S150, a control strategy for the drone swarm is determined based on the aggregation level of the drone swarm, the accuracy of the drone swarm's movement direction, the interference signal to be applied, and the constructed feedback input. The control strategy is determined to disrupt the emergent elements of swarm intelligence, thereby counteracting the swarm intelligence of the drone swarm.

[0060] Therefore, the drone swarm management method according to the embodiments of this disclosure analyzes the generation mechanism and emergence elements of swarm intelligence from the bottom layer based on the system dynamics mechanism, and further utilizes the emergence elements of swarm intelligence to achieve effective management of drone swarm intelligence.

[0061] In some embodiments, the control strategy for the drone swarm includes a first control sub-strategy for controlling and capturing individual drones within the swarm, a second control sub-strategy for attacking selected individual drones within the swarm, a third control sub-strategy for interfering with the drone swarm, and a fourth control sub-strategy for constructing false feedback inputs to the drone swarm. Further, determining the control strategy for the drone swarm based on the degree of clustering of the drone swarm, the accuracy of the drone swarm's movement direction, the interference signal to be applied, and the constructed feedback inputs includes: increasing the weight of the first control sub-strategy when the degree of clustering of the drone swarm is greater than a first threshold; and increasing the weight of the second control sub-strategy when the deviation angle of the drone swarm is less than a second threshold.

[0062] It should be noted that the first and second thresholds mentioned above can be set according to actual needs, and are not limited here.

[0063] Optionally, the third control sub-strategy for interfering with the drone swarm and / or the fourth control sub-strategy for generating false feedback inputs to the drone swarm may be predetermined or may be dynamically adjusted (e.g., based on one or more of the factors mentioned above).

[0064] Figure 4 A block diagram of an apparatus 400 for managing a drone swarm according to an embodiment of the present disclosure is shown. The apparatus 400 includes: a detection module 410 configured to detect the aggregation degree of the drone swarm to assess the autonomy of individual drones within the swarm; an analysis module 420 configured to analyze the accuracy of the drone swarm's movement direction to assess the behavioral rules of the drone swarm; a perception module 430 configured to perceive the external environment in which the drone swarm is located to determine interference signals to be applied to the drone swarm as environmental entropy input; a construction module 440 configured to construct feedback inputs to be perceived by the drone swarm to alter the feedback interaction between the drone swarm and individual drones; and a determination module 450 configured to determine a management strategy for the drone swarm based on the aggregation degree of the drone swarm, the accuracy of the drone swarm's movement direction, and the constructed feedback inputs.

[0065] This device 400 differs from related devices for managing drone swarms. Based on system dynamics, it analyzes the generation mechanism and emergence factors of swarm intelligence from a fundamental level, and further utilizes these emergence factors to achieve control over the swarm intelligence of drone swarms. Device 400 significantly expands the factors that need to be considered in managing drone swarms and can suppress the emergence of drone swarms as intelligent groups, thereby achieving effective control over drone swarms.

[0066] According to another aspect of this disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that, when executed by the at least one processor, implements the method described above.

[0067] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing a computer program is also provided, wherein the computer program implements the method described above when executed by a processor.

[0068] According to another aspect of this disclosure, a computer program product is also provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the method described above.

[0069] This disclosed embodiment can be implemented on a Windows 10 system with an Intel(R) Core(TM) i5-9400 CPU @ 2.90GHz, Unity 2020.3.1f1c1, and VS Code 1.65.2. Through scripting, a set of common behaviors of a drone swarm based on partial rules and behavioral probability distributions is implemented. Control measures based on swarm intelligence emergence are then set to manage the drone swarm through target settings. All parameters have sufficient adjustability to ensure simulation of complex external environments and situations.

[0070] See Figure 5 The following description serves as a structural block diagram of the electronic device 500 disclosed herein, which is an example of a hardware device applicable to various aspects of this disclosure. The electronic device can be different types of computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the disclosure described and / or claimed herein.

[0071] Figure 5A block diagram of an electronic device according to an embodiment of the present disclosure is shown. (As follows) Figure 5 As shown, the electronic device 500 may include at least one processor 501, working memory 502, input unit 504, display unit 505, speaker 506, storage unit 507, communication unit 508 and other output units 509 that are capable of communicating with each other via system bus 503.

[0072] Processor 501 may be a single processing unit or multiple processing units, and all processing units may include single or multiple computing units or multiple cores. Processor 501 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Processor 501 may be configured to acquire and execute computer-readable instructions stored in working memory 502, storage unit 507, or other computer-readable media, such as program code of operating system 502a, program code of application program 502b, etc.

[0073] Working memory 502 and storage unit 507 are examples of computer-readable storage media for storing instructions that are executed by processor 501 to perform the various functions described above. Working memory 502 may include both volatile and non-volatile memory (e.g., RAM, ROM, etc.). Furthermore, storage unit 507 may include hard disk drives, solid-state drives, removable media including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network-attached storage, storage area networks, etc. Working memory 502 and storage unit 507 may be collectively referred to herein as memory or computer-readable storage media, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by processor 501 as a specific machine configured to perform the operations and functions described in the examples herein.

[0074] Input unit 506 can be any type of device capable of inputting information to electronic device 500. Input unit 506 can receive input digital or character information and generate key signal input related to user settings and / or function control of electronic device, and can include, but is not limited to, a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone and / or remote control. Output unit can be any type of device capable of presenting information, and can include, but is not limited to, display unit 505, speaker 506 and other output units 509. Other output units 509 can include, but are not limited to, video / audio output terminals, vibrators and / or printers. Communication unit 508 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and can include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and / or chipsets, such as Bluetooth™ devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices and / or the like.

[0075] The application program 502b in working register 502 can be loaded to perform the various methods and processes described above. For example, in some embodiments, the image processing methods can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 507. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 500 via storage unit 507 and / or communication unit 508. When the computer program is loaded and executed by processor 501, one or more steps of the image processing methods described above can be performed. Alternatively, in other embodiments, processor 501 can be configured to perform the image processing methods by any other suitable means (e.g., by means of firmware).

[0076] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0078] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0079] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0080] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0081] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0082] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0083] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A method for managing unmanned aerial vehicle (UAV) swarms based on emerging swarm intelligence elements, wherein the emerging swarm intelligence elements include autonomous individuals, shared behavioral rules, desired environmental entropy input, and feedback mechanisms between the swarm and individuals, the method comprising: The degree of aggregation of the drone swarm is detected to assess the autonomy of individual drones within the swarm. This detection includes calculating the current time. The degree of aggregation of the drone swarm mentioned at that time ; The accuracy of the drone swarm's movement direction is analyzed to assess the drone swarm's behavioral rules; the analysis includes calculating the current moment. The deviation angle of the drone swarm at that time ; The system senses the external environment in which the drone swarm is located in order to determine the interference signal to be applied to the drone swarm as environmental entropy input. Construct feedback inputs that will be perceived by the drone swarm to change the feedback interaction between the drone swarm and individual drones; as well as Based on the degree of aggregation of the aforementioned drone swarm The deviation angle of the drone swarm Based on the interference signal to be applied and the constructed feedback input, a control strategy for the drone swarm is determined. The control strategy includes a first control sub-strategy for controlling and capturing individual drones within the drone swarm, a second control sub-strategy for attacking selected individual drones within the drone swarm, a third control sub-strategy for interfering with the drone swarm, and a fourth control sub-strategy for constructing false feedback inputs for the drone swarm. The control strategy for the drone swarm includes: The degree of aggregation of the drone swarm If the weight exceeds the first threshold, increase the weight of the first control sub-policy; and Deviation angle of the drone swarm If the value is less than the second threshold, increase the weight of the second control sub-strategy. The control strategy is designed to disrupt the swarm intelligence of the drone swarm, thereby counteracting the swarm intelligence of the drone swarm.

2. The method according to claim 1, wherein, Detecting the aggregation level of the drone swarm to assess the autonomy of individual drones within the swarm includes: Calculate the current time The degree of aggregation of the drone swarm: , in, For individual drones, The total number of individual drones within a drone swarm. For the first The number of other individual drones within the communication radius of an individual drone. Among them, the degree of aggregation of the drone swarm The larger the value, the higher the autonomy of the individual drones within the drone swarm.

3. The method according to claim 2, wherein, Analyzing the accuracy of the drone swarm's movement direction to evaluate the drone swarm's behavioral rules includes: Calculate the current time The deviation angle of the drone swarm at that time: , in, The leader drone is responsible for communicating with neighboring drones, indicating its preferred direction. For the first The position vector of an individual drone at the current time t. For the first Individual drones in the next long-term Position vector at time, Among them, the deviation angle of the drone swarm The smaller the value, the higher the accuracy of the movement direction of the drone swarm, and the more similar the behavior rules of individual drones.

4. The method according to claim 3, wherein, Sensing the external environment of the drone swarm to determine the interference signals to be applied to the drone swarm as environmental entropy input includes: Identify the interference factors affecting the drone swarm in the external environment where the drone swarm is located; and Interference signals corresponding to the interference factors are determined from a pre-built regional reconnaissance and early warning system and applied to the UAV swarm.

5. The method according to claim 4, wherein, Constructing feedback inputs that will be perceived by the drone swarm to alter the feedback interaction between the drone swarm and individual drones includes: Based on the evaluated behavioral rules of the drone swarm, a feedback input to be perceived by the drone swarm is constructed. The feedback inputs include: creating false targets and / or decoy targets to attract the drone swarm to take action against the false targets and / or decoy targets.

6. An apparatus for managing unmanned aerial vehicle (UAV) swarms based on swarm intelligence emergent elements, wherein the swarm intelligence emergent elements include autonomous individuals, shared behavioral rules, desired environmental entropy input, and feedback between the swarm and individuals, the apparatus comprising: A detection module is configured to detect the aggregation level of the drone swarm to assess the autonomy of individual drones within the swarm. The detection includes calculating the current time... The degree of aggregation of the drone swarm mentioned at that time ; An analysis module is configured to analyze the accuracy of the movement direction of the drone swarm to evaluate the behavior rules of the drone swarm, the analysis including calculating the current time. The deviation angle of the drone swarm at that time ; The perception module is configured to perceive the external environment in which the drone swarm is located in order to determine the interference signal to be applied to the drone swarm as environmental entropy input; The construction module is configured to construct feedback inputs that will be perceived by the drone swarm, so as to change the feedback interaction between the drone swarm and individual drones; as well as The determination module is configured to base its determination on the degree of aggregation of the drone swarm. The deviation angle of the drone swarm Based on the interference signal to be applied and the constructed feedback input, a control strategy for the drone swarm is determined. The control strategy includes a first control sub-strategy for controlling and capturing individual drones within the drone swarm, a second control sub-strategy for attacking selected individual drones within the drone swarm, a third control sub-strategy for interfering with the drone swarm, and a fourth control sub-strategy for constructing false feedback inputs for the drone swarm. The control strategy for the drone swarm includes: The degree of aggregation of the drone swarm If the weight exceeds the first threshold, increase the weight of the first control sub-policy; and Deviation angle of the drone swarm If the value is less than the second threshold, increase the weight of the second control sub-strategy. The control strategy is designed to disrupt the swarm intelligence of the drone swarm, thereby counteracting the swarm intelligence of the drone swarm.

7. An electronic device, comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores a computer program that, when executed by the at least one processor, implements the method according to any one of claims 1-5.

8. A non-transitory computer-readable storage medium storing a computer program, wherein, The computer program, when executed by a processor, implements the method according to any one of claims 1-5.

9. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method according to any one of claims 1-5.

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