A method, system, and device for unmanned aerial vehicle (UAV) network trust management based on gray systems.

Abstract

This invention discloses a method, system, and device for trust management in unmanned aerial vehicle (UAV) networks based on a gray system. First, energy trust is obtained based on the energy consumption of the target UAV due to its communication behavior. Then, based on historical interaction information and the target UAV's energy status, the number of interactions is adjusted to obtain interaction trust. The direct trust value of the target UAV i is calculated based on the interaction trust and energy trust. Next, based on the gray system, a third-party observation node is selected from the target UAV node's neighboring nodes, and its indirect trust value is obtained by weighting the direct trust value and recommended value of the third-party observation node. Finally, the direct and indirect trust values ​​are integrated to obtain the final trust value of the target UAV node, and malicious nodes are isolated based on this trust value. This invention utilizes an effective interaction count mechanism to effectively distinguish between intentional and unintentional malicious behavior, reducing the error rate of malicious node identification.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) network security, and relates to a UAV network trust management method, system and device, and particularly to a UAV network trust management method, system and device using a gray system design. Background Technology

[0002] With the continuous development of technology, unmanned aerial vehicle (UAV) technology has matured. Their small size, flexible flight, ease of operation, and strong adaptability have enabled them to occupy a place in applications such as logistics transportation, agricultural planting, and disaster relief. However, as the application of UAVs becomes increasingly widespread, the safety issues that have gradually emerged in recent years have also become a focus of public attention.

[0003] Due to the high mobility and dynamic topology of drone networks, drones cannot fully trust each other. If malicious drones are present in the network, attackers can not only eavesdrop on drone communications but also attack the entire network by injecting or modifying messages. For example, during drone logistics transportation, malicious nodes might intentionally spread false information, guiding transport drones to incorrect addresses. Therefore, to ensure drone network security, defense solutions for drones should be able to promptly detect malicious nodes that forge identities.

[0004] To address these issues, the most effective solution is to employ a trust management mechanism in drone networks. Compared to traditional drone network defense solutions, a trust management mechanism can reduce the consumption of drones' limited resources while identifying and isolating malicious nodes based on trust values, thus protecting drone network security.

[0005] Currently, trust management is mainly divided into three types of models: entity trust model, which mainly considers factors such as node service quality, social attributes, and hardware conditions to evaluate network nodes, but its evaluation effect is poor when there is little network interaction; data trust model, which evaluates data content by considering factors such as data similarity, authenticity, and integrity, but it cannot establish reliable connections between drones; and hybrid model, which combines entity trust and data trust to protect network security more comprehensively, but its disadvantage is that it has a large overhead.

[0006] In existing research, most trust management mechanisms lack the ability to detect unintentional malicious behavior of normal drones, as well as reasonable schemes for selecting third-party observation nodes, resulting in a low accuracy rate in identifying malicious drones. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a method, system, and device for drone network trust management that can distinguish whether malicious behavior by drones is intentional and has the ability to select appropriate third-party observation nodes.

[0008] The technical solution adopted by the method of the present invention is: a trust management method for unmanned aerial vehicle networks based on a gray system, comprising the following steps:

[0009] Step 1: Based on the energy consumption E of the target UAV i due to its communication behavior c Gain energy trust T E (i); and based on historical interaction information and the target UAV's energy status, adjust the number of interactions to obtain interaction trust T. F (i); based on energy trust T E (i) and interactive trust T F (i) Calculate the direct trust value T of the target UAV i. direct (i);

[0010] Step 2: Based on the grey system, select third-party observation nodes among the neighboring nodes of the target drone node, and obtain the indirect trust value T of the target drone node by weighting the direct trust value and recommendation value of the third-party observation nodes. indirect (i);

[0011] Step 3: For the direct trust value T direct (i) and indirect trust value T indirect (i) Perform trust integration to obtain the final trust value of the target drone node, and isolate malicious nodes based on the trust value.

[0012] Preferably, step 1 includes the following sub-steps:

[0013] Step 1.1: Calculate the energy consumption E of the target UAV i due to its communication activities. c =2*ERP+ETP, where ERP is the energy consumption for receiving data packets and ETP is the energy consumption for transmitting data packets;

[0014] Step 1.2: Based on the energy status of target UAV i, calculate the effective value f of the k-th interaction of target UAV i. i k ;

[0015]

[0016] Where E res_i E represents the remaining energy of the target drone i. th The energy threshold required to complete the task, α∈(0,1) is a correction coefficient used to eliminate the impact of unintentional packet loss due to insufficient energy;

[0017] Step 1.3: Calculate the number of effective interactions between target UAV i and time t. Where K is the total number of interactions within time t;

[0018] Step 1.4: Calculate the interaction trust of target UAV i in Let be the total number of interactions between target drone i and time t. Let be the number of times target drone i successfully forwards a message within time t;

[0019] Step 1.5: Based on the energy consumption Ei of the target UAV i due to movement and task execution, and the maximum energy consumption E in the UAV network. max Calculate energy trust

[0020] Step 1.6: Based on the calculated interaction trust and energy trust, obtain the direct trust T of the target UAV i using a weighted average. direct (i)=ω F T F (i)+ω E T E (i), where ω F and ω E It is a weighting factor, and satisfies ω F +ω E =1.

[0021] As a preferred embodiment, step 2 includes the following sub-steps:

[0022] Step 2.1: Based on the neighboring nodes of the target UAV i, construct a candidate node set S = {S1, S2, ..., S...} m}, the number of candidate nodes is m, and a set of candidate node attributes Q = {Q1, Q2, ..., Q} is constructed. n}, with n attributes, and obtain the interaction trust and energy trust of candidate nodes;

[0023] Step 2.2: Calculate the attribute weights of candidate nodes and attribute level

[0024] Step 2.3: Based on the attribute levels of each candidate node Constructing a grey decision matrix

[0025] Step 2.4: Normalize the elements in the grey decision matrix D to obtain the normalized matrix. in in, G ij , Representing gray numbers respectively The lower and upper limits,

[0026] Step 2.5: Obtain the weighted matrix based on the normalized attribute levels and attribute weights. in in, Represents attribute Q j The weights;

[0027] Step 2.6: Based on the weighting matrix D * To obtain the best reference solution S max ;

[0028]

[0029] in, V ij , Representing gray numbers respectively The lower and upper limits, where 1≤j≤n;

[0030] Step 2.7: Calculate the grey probability of all candidate nodes. And sorted in ascending order, considering both the evaluation effect and the impact on network performance, the top L candidate nodes were selected as third-party recommended nodes; among them, L is a preset value;

[0031] Step 2.8: Based on the direct trust value and recommendation value of the third-party recommendation node, obtain the indirect trust value for the target drone i. Where T direct_j T is the direct trust value of the j-th third-party recommendation node itself. rec_j It is the recommendation value given by the j-th third-party recommendation node for the target drone i.

[0032] As a preferred embodiment, step 3 includes the following sub-steps:

[0033] Step 3.1: For the direct trust value T direct (i) and indirect trust value T indirect (i) Perform trust integration to obtain the trust value T(i) of the final target UAV i in the UAV network = ωT direct (i)+(1-ω)T indirect (i), where ω is the weighting factor for direct trust;

[0034] Step 3.2: Compare the trust value T(i) of the target drone i with the set trust threshold T. S If T(i) < T S This indicates that drone i is likely a malicious node. Therefore, it should be isolated from the drone network, and its communication and evaluation functions should be disabled.

[0035] The technical solution adopted by the system of the present invention is: a drone network trust management system based on a gray system, comprising the following modules:

[0036] Module 1 is used to determine the energy consumption E of the target UAV i due to its communication behavior. c Gain energy trust T E (i); and based on historical interaction information and the target UAV's energy status, adjust the number of interactions to obtain interaction trust T. F (i); based on energy trust T E (i) and interactive trust T F (i) Calculate the direct trust value T of the target UAV i. direct (i);

[0037] Module 2 is used to select third-party observation nodes among the neighboring nodes of the target UAV node based on a gray system, and to obtain the indirect trust value T of the target UAV node by weighting the direct trust value and recommendation value of the third-party observation nodes. indirect (i);

[0038] Module 3 is used for direct trust value T direct (i) and indirect trust value T indirect (i) Perform trust integration to obtain the final trust value of the target drone node, and isolate malicious nodes based on the trust value.

[0039] As a preferred embodiment, the specific implementation of module 1 includes the following sub-modules:

[0040] Module 1.1 is used to calculate the energy consumption E of the target UAV i due to its communication activities. c =2*ERP+ETP, where ERP is the energy consumption for receiving data packets and ETP is the energy consumption for transmitting data packets;

[0041] Module 1.2 is used to calculate the effective value f of the k-th interaction of target UAV i based on the energy status of target UAV i. i k ;

[0042]

[0043] Where E res_i E represents the remaining energy of the target drone i. th The energy threshold required to complete the task, α∈(0,1) is a correction coefficient used to eliminate the impact of unintentional packet loss due to insufficient energy;

[0044] Module 1.3 is used to calculate the number of effective interactions between target UAV i and time t. Where K is the total number of interactions within time t;

[0045] Module 1.4 is used to calculate the interaction trust of target UAV i. in Let be the total number of interactions between target drone i and time t. Let be the number of times target drone i successfully forwards a message within time t;

[0046] Module 1.5 is used to determine the energy consumption E of the target UAV i due to movement and task execution. i And the maximum energy consumption E in drone networks max Calculate energy trust

[0047] Module 1.6 is used to obtain the direct trust T of the target UAV i by weighting the calculated interaction trust and energy trust. direct (i)=ω F T F (i)+ω E T E (i), where ω F and ω E It is a weighting factor, and satisfies ω F +ω E =1.

[0048] As a preferred embodiment, the specific implementation of module 2 includes the following sub-modules:

[0049] Module 2.1 is used to construct a candidate node set S = {S1, S2, ..., S...} based on the neighboring nodes of the target UAV i. m The number of candidate nodes is m, and a set of candidate node attributes Q = {Q1, Q2, ..., Q} is constructed. n}, with n attributes, and obtain the interaction trust and energy trust of candidate nodes;

[0050] Module 2.2 is used to calculate the attribute weights of candidate nodes. and attribute level

[0051] Module 2.3 is used to classify the attribute levels of each candidate node. Constructing a grey decision matrix

[0052] Module 2.4 is used to normalize the elements in the grey decision matrix D to obtain the normalized matrix. in in, G ij , Representing gray numbers respectively The lower and upper limits,

[0053] Module 2.5 is used to obtain a weighted matrix based on normalized attribute levels and attribute weights. in in, Represents attribute Q j The weights;

[0054] Module 2.6, used to calculate based on the weighted matrix D * To obtain the best reference solution S max ;

[0055]

[0056] in, V ij , Representing gray numbers respectively The lower and upper limits, where 1≤j≤n;

[0057] Module 2.7 is used to calculate the gray probability of all candidate nodes. And sorted in ascending order, considering both the evaluation effect and the impact on network performance, the top L candidate nodes were selected as third-party recommended nodes; among them, L is a preset value;

[0058] Module 2.8 is used to obtain indirect trust for target drone i based on the direct trust value and recommendation value of the third-party recommendation node itself. Where T direct_j T is the direct trust value of the j-th third-party recommendation node itself. rec_j It is the recommendation value given by the j-th third-party recommendation node for the target drone i.

[0059] As a preferred embodiment, the specific implementation of module 3 includes the following sub-modules:

[0060] Module 3.1 is used for direct trust values ​​T. direct (i) and indirect trust value T indirect (i) Perform trust integration to obtain the trust value T(i) of the final target UAV i in the UAV network = ωT direct (i)+(1-ω)T indirect (i), where ω is the weighting factor for direct trust;

[0061] Module 3.2 is used to compare the trust value T(i) of the target drone i with the set trust threshold T. S If T(i) < T S This indicates that drone i is likely a malicious node. Therefore, it should be isolated from the drone network, and its communication and evaluation functions should be disabled.

[0062] The technical solution adopted by the device of the present invention is: a drone network trust management device based on a gray system, comprising:

[0063] One or more processors;

[0064] A storage device for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the gray system-based UAV network trust management method.

[0065] The present invention has the following advantages and effects over the prior art:

[0066] 1. Some drones may unintentionally lose packets due to energy issues. Therefore, the effective interaction count mechanism can effectively identify whether the malicious behavior of drones is intentional and improve the accuracy of detection.

[0067] 2. Utilize a gray system to select suitable third-party observation nodes to avoid malicious drones providing false recommendation values ​​that could affect trust value calculation. Attached Figure Description

[0068] The technical solutions described herein are further illustrated below using examples and specific implementation methods. Additionally, accompanying drawings are used in the description of the technical solutions. Those skilled in the art can, without any creative effort, obtain other drawings and the intent of the present invention based on these drawings.

[0069] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0070] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0071] To implement a trust management scheme for unmanned aerial vehicle (UAV) networks based on gray systems, this invention requires the involvement of the following network entities:

[0072] Drones: Each drone is responsible for executing tasks and forwarding information, and has certain data storage (responsible for storing interaction data and neighbor recommendation values) and computing power (responsible for calculating direct and indirect trust values).

[0073] During drone communication, drones can collect information on the communication behavior and energy consumption of neighboring drones. Specifically, communication behavior refers to effective interactions (such as successful message forwarding or unintentional packet loss due to objective reasons) and invalid interactions (such as tampering with or dropping data packets); energy consumption refers to the average energy consumption of drones over a period of time. Drones can use this information to perform trust assessments. Whenever drones interact, the sender assesses the trust of the message receiver based on the interaction and energy consumption. Simultaneously, a gray system is used to select a certain number of third-party observation nodes around the receiver for indirect trust calculations. Finally, the two are weighted and integrated, and malicious nodes are isolated based on a trust threshold.

[0074] Please see Figure 1 The present invention provides a method for trust management of unmanned aerial vehicle (UAV) networks based on a gray system, comprising the following steps:

[0075] Step 1: Based on the energy consumption E of the target UAV i due to its communication behavior c Gain energy trust T E (i); and based on historical interaction information and the target UAV's energy status, adjust the number of interactions to obtain interaction trust T. F (i); based on energy trust T E (i) and interactive trust T F (i) Calculate the direct trust value T of the target UAV i. direct ();

[0076] In one implementation, step 1 specifically includes the following sub-steps:

[0077] Step 1.1: Calculate the energy consumption E of the target UAV i due to its communication activities. c = 2 * ERP + ETP, where ERP is the energy consumption for receiving data packets, calculated using the following formula: ETP is the energy consumption incurred during data packet transmission, calculated using the following formula: PacketSize is the size of the transmitted data packet;

[0078] Step 1.2: Based on the energy status of target UAV i, calculate the effective value f of the k-th interaction of target UAV i. i k ;

[0079]

[0080] Where E res_i E represents the remaining energy of the target drone i. th The energy threshold required to complete the task, α∈(0,1) is a correction coefficient used to eliminate the impact of unintentional packet loss due to insufficient energy;

[0081] Step 1.3: Calculate the number of effective interactions between target UAV i and time t. Where K is the total number of interactions within time t;

[0082] Step 1.4: Calculate the interaction trust of target UAV i in Let be the total number of interactions between target drone i and time t. Let be the number of times target drone i successfully forwards a message within time t;

[0083] Step 1.5: Based on the energy consumption E generated by the target UAV i due to movement and task execution. i And the maximum energy consumption E in drone networks max Calculate energy trust

[0084] Step 1.6: Based on the calculated interaction trust and energy trust, obtain the direct trust T of the target UAV i using a weighted average. direct (i)=ω F T F (i)+ω E T E (i), where ω F and ω E It is a weighting factor, and satisfies ω F + E =1.

[0085] Step 2: Based on the grey system, select third-party observation nodes among the neighboring nodes of the target drone node, and obtain the indirect trust value T of the target drone node by weighting the direct trust value and recommendation value of the third-party observation nodes. indirect (i);

[0086] In one implementation, step 2 specifically includes the following sub-steps:

[0087] Step 2.1: Based on the neighboring nodes of the target UAV i, construct a candidate node set S = {S1, S2, ..., S...} m The number of candidate nodes is m, and a set of candidate node attributes Q = {Q1, Q2, ..., Q} is constructed. n}, with n attributes, and obtain the interaction trust and energy trust of candidate nodes;

[0088] Step 2.2: Calculate the attribute weights of candidate nodes and attribute level

[0089] In one implementation, the attribute weights and attribute levels correspond to the evaluation and intervals as shown in Tables 1 and 2 below.

[0090] Table 1

[0091]

[0092] Table 1

[0093]

[0094] Step 2.3: Based on the attribute levels of each candidate node Constructing a grey decision matrix

[0095] Step 2.4: Normalize the elements in the grey decision matrix D to obtain the normalized matrix. in in, G ij , Representing gray numbers respectively The lower and upper limits,

[0096] Step 2.5: Obtain the weighted matrix based on the normalized attribute levels and attribute weights. in in, Represents attribute Q j The weights;

[0097] Step 2.6: Based on the weighting matrix D * To obtain the best reference solution S max ;

[0098]

[0099] in, V ij , Representing gray numbers respectively The lower and upper limits, where 1≤j≤n;

[0100] Step 2.7: Calculate the grey probability of all candidate nodes. And sorted in ascending order, taking into account both the evaluation effect and the impact on network performance, the top ten candidate nodes were selected as third-party recommended nodes; among them,

[0101] Step 2.8: Based on the direct trust value and recommendation value of the third-party recommendation node, obtain the indirect trust value for the target drone i. Where T direct_ T is the direct trust value of the j-th third-party recommendation node itself. rec_j It is the recommendation value given by the j-th third-party recommendation node for the target drone i.

[0102] Step 3: For the direct trust value Tdirect (i) and indirect trust value T indirect (i) Perform trust integration to obtain the final trust value of the target drone node, and isolate malicious nodes based on the trust value;

[0103] In one implementation, step 3 specifically includes the following sub-steps:

[0104] Step 3.1: For the direct trust value T direc t(i) and indirect trust value T indirec t(i) performs trust integration to obtain the final trust value T(i) = ωT for the target drone i in the drone network. direct (i)+(1-ω)T indirect (i), where ω is the weighting factor for direct trust;

[0105] Step 3.2: Compare the trust value T(i) of the target drone i with the set trust threshold T. S If T(i) < T S This indicates that drone i is likely a malicious node. Therefore, it should be isolated from the drone network, and its communication and evaluation functions should be disabled.

[0106] This invention also provides a drone network trust management system based on a gray system, comprising the following modules:

[0107] Module 1 is used to determine the energy consumption E of the target UAV i due to its communication behavior. c Gain energy trust T E (i); and based on historical interaction information and the target UAV's energy status, adjust the number of interactions to obtain interaction trust T. F (i); based on energy trust T E (i) and interactive trust T F (i) Calculate the direct trust value T of the target UAV i. direct (i);

[0108] Module 2 is used to select third-party observation nodes among the neighboring nodes of the target UAV node based on a gray system, and to obtain the indirect trust value T of the target UAV node by weighting the direct trust value and recommendation value of the third-party observation nodes. indirect (i);

[0109] Module 3 is used for direct trust value T direct (i) and indirect trust value T indirect (i) Perform trust integration to obtain the final trust value of the target drone node, and isolate malicious nodes based on the trust value.

[0110] In one implementation, module 1 specifically includes the following sub-modules:

[0111] Module 1.1 is used to calculate the energy consumption E of the target UAV i due to its communication activities. c =2*ERP+ETP, where ERP is the energy consumption for receiving data packets and ETP is the energy consumption for transmitting data packets;

[0112] Module 1.2 is used to calculate the effective value f of the k-th interaction of target UAV i based on the energy status of target UAV i. i k ;

[0113]

[0114] Where E res_i E represents the remaining energy of the target drone i. th The energy threshold required to complete the task, α∈(0,1) is a correction coefficient used to eliminate the impact of unintentional packet loss due to insufficient energy;

[0115] Module 1.3 is used to calculate the number of effective interactions between target UAV i and time t. Where K is the total number of interactions within time t;

[0116] Module 1.4 is used to calculate the interaction trust of target UAV i. in Let be the total number of interactions between target drone i and time t. Let be the number of times target drone i successfully forwards a message within time t;

[0117] Module 1.5 is used to determine the energy consumption E of the target UAV i due to movement and task execution. i And the maximum energy consumption E in drone networks max Calculate energy trust

[0118] Module 1.6 is used to obtain the direct trust T of the target UAV i by weighting the calculated interaction trust and energy trust. direct (i)=ω F T F (i)+ω E T E (i), where ω F and ω E It is a weighting factor, and satisfies ω F +ω E =1.

[0119] In one implementation, module 2 specifically includes the following sub-modules:

[0120] Module 2.1 is used to construct a candidate node set S = {S1, S2, ..., S...} based on the neighboring nodes of the target UAV i. m The number of candidate nodes is m, and a set of candidate node attributes Q = {Q1, Q2, ..., Q} is constructed. n}, with n attributes, and obtain the interaction trust and energy trust of candidate nodes;

[0121] Module 2.2 is used to calculate the attribute weights of candidate nodes. and attribute level

[0122] Module 2.3 is used to classify the attribute levels of each candidate node. Constructing a grey decision matrix

[0123] Module 2.4 is used to normalize the elements in the grey decision matrix D to obtain the normalized matrix. in in, G ij , Representing gray numbers respectively The lower and upper limits,

[0124] Module 2.5 is used to obtain a weighted matrix based on normalized attribute levels and attribute weights. in in, Represents attribute Q j The weights;

[0125] Module 2.6, used to calculate based on the weighted matrix D * To obtain the best reference solution S max ;

[0126]

[0127] in, V ij , Representing gray numbers respectively The lower and upper limits, where 1≤j≤n;

[0128] Module 2.7 is used to calculate the gray probability of all candidate nodes. And sorted in ascending order, considering both the evaluation effect and the impact on network performance, the top L candidate nodes were selected as third-party recommended nodes; among them, L is a preset value;

[0129] Module 2.8 is used to obtain indirect trust for target drone i based on the direct trust value and recommendation value of the third-party recommendation node itself. Where Tdirect_ T is the direct trust value of the j-th third-party recommendation node itself. rec_j It is the recommendation value given by the j-th third-party recommendation node for the target drone i.

[0130] In one implementation, module 3 specifically includes the following sub-modules:

[0131] Module 3.1 is used for direct trust values ​​T. direct (i) and indirect trust value T indirect (i) Perform trust integration to obtain the trust value T(i) of the final target UAV i in the UAV network = ωT direct (i)+(1-ω)T indirect (i), where ω is the weighting factor for direct trust;

[0132] Module 3.2 is used to compare the trust value T(i) of the target drone i with the set trust threshold T. S If T(i) < T S This indicates that drone i is likely a malicious node. Therefore, it should be isolated from the drone network, and its communication and evaluation functions should be disabled.

[0133] This invention also provides a drone network trust management device based on a gray system, comprising:

[0134] One or more processors;

[0135] A storage device for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the gray system-based UAV network trust management method.

[0136] This invention designs a trust management scheme for unmanned aerial vehicle (UAV) networks based on a gray system. Nodes in the network are primarily assessed for trust based on interaction and energy, supplemented by recommended trust, using a comprehensive multi-indicator approach. In the interaction and energy trust phases, the message-sending node calculates interaction trust based on the number of valid interactions with the receiving node, effectively identifying unintentional malicious behavior by normal UAVs due to objective reasons; simultaneously, it calculates energy trust using the energy consumption of the receiving node, and weights these two factors to obtain direct trust. In the recommended trust phase, the scheme uses a gray system to calculate the gray probability of candidate nodes adjacent to the target node, sorts them in ascending order, selects the top-ranked nodes as third-party observation nodes, and weights and sums the recommended values ​​to obtain indirect trust. This scheme identifies and isolates malicious nodes by integrating direct and indirect trust and comparing them with a trust threshold.

[0137] It should be understood that the above description of the preferred embodiments is quite detailed, but it should not be considered as a limitation on the scope of protection of this invention. Those skilled in the art, under the guidance of this invention, can make substitutions or modifications without departing from the scope of protection of the claims of this invention, and all such substitutions or modifications fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for trust management in unmanned aerial vehicle (UAV) networks based on a gray system, comprising the following steps: Step 1: Based on the target drone Energy consumption due to communication activities Gain the trust of energy ; Based on historical interaction information and the target drone's energy status, the number of interactions is adjusted to gain interaction trust. Based on energy trust and interactive trust Calculate target drone Direct trust value ; Step 1 includes the following sub-steps: Step 1.1: Calculate the target UAV Energy consumption due to communication activities ,in Energy consumption for receiving data packets, Energy consumption due to data packet transmission; Step 1.2: Based on the target drone Energy status, calculate target drone No. Valid value of this interaction ; in For target drones Remaining energy The energy threshold required to complete the task. This is a correction factor used to eliminate the impact of unintentional packet loss due to insufficient energy. Step 1.3: Calculation Target drone within a certain time frame Number of valid interactions ,in for Total number of interactions within a given time period; Step 1.4: Calculate the target UAV Interactive trust ,in For target drones exist Total number of interactions within a given time period For target drones exist The number of times the message was successfully forwarded within a given time period; Step 2: Based on the gray system, select third-party observation nodes among the neighboring nodes of the target drone node, and obtain the indirect trust value of the target drone node by weighting the direct trust value and recommendation value of the third-party observation nodes. ; Step 3: For direct trust values and indirect trust value Trust integration is performed to obtain the final trust value of the target drone node, and malicious nodes are isolated based on the trust value.

2. The UAV network trust management method based on a gray system according to claim 1, characterized in that, Step 1 also includes the following sub-steps: Step 1.5: Based on the target drone Energy consumption due to movement and task execution Maximum energy consumption in drone networks Calculate energy trust ; Step 1.6: Based on the calculated interaction trust and energy trust, obtain the target drone using a weighted average. direct trust ,in and It is a weighting factor and satisfies .

3. The UAV network trust management method based on a gray system according to claim 1, characterized in that, Step 2 includes the following sub-steps: Step 2.1: Based on the target drone Connect the adjacent nodes to construct a candidate node set. The number of candidate nodes is And construct a set of candidate node attributes The number of attributes is And obtain the interaction trust and energy trust of the candidate nodes; Step 2.2: Calculate the attribute weights of candidate nodes and attribute level ; Step 2.3: Based on the attribute levels of each candidate node Constructing a grey decision matrix ; Step 2.4: Convert the grey decision matrix The elements in the matrix are normalized to obtain the normalized matrix. ,in ;in, Representing gray numbers respectively The lower and upper limits, ; Step 2.5: Obtain the weighted matrix based on the normalized attribute levels and attribute weights. ,in ;in, Representing attributes The weights; Step 2.6: Based on the weighting matrix To obtain the best reference solution ; in, , Representing gray numbers respectively The lower and upper limits, of which ; Step 2.7: Calculate the grey probability of all candidate nodes. The results were sorted in ascending order, and the top results were selected after comprehensively considering the evaluation effect and the impact on network performance. L Several candidate nodes serve as third-party recommendation nodes; among them... , L This is the default value; Step 2.8: Based on the direct trust value and recommendation value of the third-party recommendation node, obtain the target drone's... Indirect trust ,in It is the first The direct trust value of each third-party recommendation node itself. It is the first A third-party recommendation node for the target drone The recommended values ​​are given.

4. The UAV network trust management method based on a gray system according to claim 1, characterized in that, Step 3 includes the following sub-steps: Step 3.1: For direct trust values and indirect trust value Trust integration is conducted to obtain the final target drone. Trust values ​​in drone networks ,in It is a weighting factor for direct trust; Step 3.2: Compare the target drones Trust value With the set trust threshold ,like This indicates that drones It is highly likely to be a malicious node. Isolate it from the drone network and disable its communication and evaluation functions.

5. A trust management system for unmanned aerial vehicle (UAV) networks based on a gray system, characterized in that, Includes the following modules: Module 1, for use with target-based UAVs Energy consumption due to communication activities Gain the trust of energy ; Based on historical interaction information and the target drone's energy status, the number of interactions is adjusted to gain interaction trust. Based on energy trust and interactive trust Calculate target drone Direct trust value ; Module 1 includes the following sub-modules: Module 1.1 is used to calculate the target UAV. Energy consumption due to communication activities ,in Energy consumption for receiving data packets, Energy consumption due to data packet transmission; Module 1.2 is used to determine the target UAV. Energy status, calculate target drone No. Valid value of this interaction ; in For target drones Remaining energy The energy threshold required to complete the task. This is a correction factor used to eliminate the impact of unintentional packet loss due to insufficient energy. Module 1.3, used for calculation Target drone within a certain time frame Number of valid interactions ,in for Total number of interactions within a given time period; Module 1.4 is used to calculate the target UAV. Interactive trust ,in For target drones exist Total number of interactions within a given time period For target drones exist The number of times the message was successfully forwarded within a given time period; Module 2 is used to select third-party observation nodes among the neighboring nodes of the target UAV node based on a gray system, and to obtain the indirect trust value of the target UAV node by weighting the direct trust value and recommendation value of the third-party observation nodes. ; Module 3 is used for direct trust values. and indirect trust value Trust integration is performed to obtain the final trust value of the target drone node, and malicious nodes are isolated based on the trust value.

6. The UAV network trust management system based on a gray system according to claim 5, characterized in that, Module 1 also Includes the following sub-modules: Module 1.5 is used to determine the target drone. Energy consumption due to movement and task execution Maximum energy consumption in drone networks Calculate energy trust ; Module 1.6 is used to obtain the target drone based on the calculated interaction trust and energy trust using a weighted average. direct trust ,in and It is a weighting factor and satisfies .

7. The UAV network trust management system based on a gray system according to claim 5, characterized in that, Module 2 includes the following sub-modules: Module 2.1 is used to determine the target UAV. Connect the adjacent nodes to construct a candidate node set. The number of candidate nodes is And construct a set of candidate node attributes The number of attributes is And obtain the interaction trust and energy trust of the candidate nodes; Module 2.2 is used to calculate the attribute weights of candidate nodes. and attribute level ; Module 2.3 is used to classify the attribute levels of each candidate node. Constructing a grey decision matrix ; Module 2.4 is used to process the grey decision matrix. The elements in the matrix are normalized to obtain the normalized matrix. ,in ;in, Representing gray numbers respectively The lower and upper limits, ; Module 2.5 is used to obtain a weighted matrix based on normalized attribute levels and attribute weights. ,in ;in, Representing attributes The weights; Module 2.6, used for weighting matrix To obtain the best reference solution ; in, , Representing gray numbers respectively The lower and upper limits, of which ; Module 2.7 is used to calculate the gray probability of all candidate nodes. The results were sorted in ascending order, and the top results were selected after comprehensively considering the evaluation effect and the impact on network performance. L Several candidate nodes serve as third-party recommendation nodes; among them... , L This is the default value; Module 2.8 is used to obtain the target drone based on the direct trust value and recommendation value of the third-party recommendation node itself. Indirect trust ,in It is the first The direct trust value of each third-party recommendation node itself. It is the first A third-party recommendation node for the target drone The recommended values ​​are given.

8. The UAV network trust management system based on a gray system according to claim 5, characterized in that, Module 3 includes the following sub-modules: Module 3.1 is used for direct trust values. and indirect trust value Trust integration is conducted to obtain the final target drone. Trust values ​​in drone networks ,in It is a weighting factor for direct trust; Module 3.2 is used to compare target drones. Trust value With the set trust threshold ,like This indicates that drones It is highly likely to be a malicious node. Isolate it from the drone network and disable its communication and evaluation functions.

9. A trust management device for unmanned aerial vehicle (UAV) networks based on a gray system, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the unmanned aerial vehicle network trust management method based on a gray system as described in any one of claims 1 to 4.

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