CN113068151B - Unmanned aerial vehicle swarm task reliability analysis method and system considering system failure - Google Patents
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Abstract
The invention relates to an unmanned aerial vehicle swarm task reliability analysis method considering system failure, which comprises the following steps: adopting a complex network method to construct a network model of the unmanned aerial vehicle swarm; the network model comprises a communication layer, a structural layer and a task layer, wherein each node in the structural layer corresponds to one unmanned aerial vehicle, the node in the communication layer is determined according to communication equipment in each unmanned aerial vehicle, and the node in the task layer is determined according to the task load quantity of each unmanned aerial vehicle; obtaining failure distribution of each node in the network model along with time variation; updating the network model according to the failure distribution of each node along with the change of time; and carrying out reliability analysis on the updated network model according to a preset task reliability index. The invention improves the comprehensiveness and accuracy of reliability analysis.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle swarm task reliability analysis method and system considering system failure.
Background
With the development of intelligent control technology, networked communication technology and group intelligent theory, the unmanned plane swarm is the main development direction of the aviation technology and is an important realization form of each task in each field of civil and military use in the future. The unmanned plane swarm carries out capacity sharing through communication between the unmanned plane swarm and cooperatively completes tasks in a system mode. Although the task capability of the unmanned aerial vehicle has higher robustness and flexibility, the unmanned aerial vehicle faces more complex task environments and threats, quantitative decision-making basis is needed in all links of design, use, maintenance guarantee and the like of unmanned aerial vehicle swarm, and task reliability is an important index for measuring the task capability under given conditions. The accurate and effective unmanned aerial vehicle swarm task reliability quantification and evaluation method has important significance for the technical development of the method. At present, unmanned aerial vehicle swarm task reliability assessment is not comprehensive enough.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle swarm task reliability analysis method and system considering system failure, and comprehensiveness and accuracy of reliability analysis are improved.
In order to achieve the purpose, the invention provides the following scheme:
an unmanned aerial vehicle swarm task reliability analysis method considering system failure, the method comprising:
adopting a complex network method to construct a network model of the unmanned aerial vehicle swarm; the network model comprises a communication layer, a structural layer and a task layer, wherein each node in the structural layer corresponds to one unmanned aerial vehicle, the node in the communication layer is determined according to communication equipment in each unmanned aerial vehicle, and the node in the task layer is determined according to the task load quantity of each unmanned aerial vehicle;
obtaining failure distribution of each node in the network model along with time variation;
updating the network model according to the failure distribution of each node along with the change of time;
and carrying out reliability analysis on the updated network model according to a preset task reliability index.
Optionally, the obtaining the failure distribution of each node in the network model over time includes:
obtaining failure time corresponding to each node of the communication layer, and obtaining failure distribution of the communication layer;
obtaining failure time corresponding to each node of the structural layer, and obtaining failure distribution of the structural layer;
and acquiring failure time corresponding to each node of the task layer, and acquiring failure distribution of the task layer.
Optionally, the task reliability indicators include vulnerability and connectivity.
Optionally, the updating the network model according to the time-varying failure distribution of each node specifically includes:
initializing task time and determining task interval time;
obtaining failure distribution of each node in the network model along with time change in a set time period;
and traversing the network model once every the task interval time, inquiring and deleting nodes corresponding to the task interval time.
Optionally, the method further comprises:
and traversing the network model once every the task interval time, inquiring and deleting the attacked nodes corresponding to the task interval time.
The invention also discloses an unmanned aerial vehicle swarm task reliability analysis system considering system failure, which comprises the following components:
the network model building module is used for building a network model of the unmanned aerial vehicle swarm by adopting a complex network method; the network model comprises a communication layer, a structural layer and a task layer, wherein each node in the structural layer corresponds to one unmanned aerial vehicle, the node in the communication layer is determined according to communication equipment in each unmanned aerial vehicle, and the node in the task layer is determined according to the task load quantity of each unmanned aerial vehicle;
the failure distribution obtaining module is used for obtaining the failure distribution of each node in the network model along with the change of time;
the network model updating module is used for updating the network model according to the failure distribution of each node along with the change of time;
and the reliability analysis module is used for carrying out reliability analysis on the updated network model according to a preset task reliability index.
Optionally, the failure distribution obtaining module includes:
a communication layer failure distribution obtaining unit, configured to obtain failure time corresponding to each node of the communication layer, and obtain communication layer failure distribution;
the structural layer failure distribution acquisition unit is used for acquiring failure time corresponding to each node of the structural layer and acquiring structural layer failure distribution;
and the task layer failure distribution acquisition unit is used for acquiring failure time corresponding to each node of the task layer and acquiring the task layer failure distribution.
Optionally, the task reliability indicators include vulnerability and connectivity.
Optionally, the network model updating module specifically includes:
the initialization unit is used for initializing task time and determining task interval time;
the failure distribution acquisition unit is used for acquiring failure distribution of each node in the network model along with time change in a set time period;
and the first network model updating unit is used for traversing the network model once every task interval time, inquiring and deleting nodes corresponding to the task interval time.
Optionally, the system further comprises:
and the second network model updating unit is used for traversing the network model once every task interval time, inquiring and deleting the attacked nodes corresponding to the task interval time.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses an unmanned aerial vehicle swarm task reliability analysis method and system considering system failure, wherein a network model of an unmanned aerial vehicle swarm is constructed, and failure distribution of each node in the network model along with time change is obtained; updating the network model according to the failure distribution of each node along with the change of time; and performing reliability analysis on the updated network model according to a preset task reliability index, so that the accuracy and comprehensiveness of the unmanned aerial vehicle swarm task reliability analysis are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle swarm task reliability analysis method considering system failure according to the present invention;
FIG. 2 is a schematic structural diagram of an unmanned aerial vehicle swarm task reliability analysis system considering system failure according to the invention;
FIG. 3 is a detailed flow diagram of the unmanned aerial vehicle swarm task reliability analysis method considering system failure according to the present invention;
FIG. 4 is a schematic diagram of the distribution of failed nodes and attacked nodes in the network model of the present invention;
FIG. 5 is a schematic diagram illustrating the discrete distribution of failure nodes and attacked nodes in the network model of the present invention;
FIG. 6 is a schematic diagram of a network model structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an unmanned aerial vehicle swarm task reliability analysis method and system considering system failure, and comprehensiveness and accuracy of reliability analysis are improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow diagram of an analysis method for reliability of an unmanned aerial vehicle swarm task considering system failure, and as shown in fig. 1, the analysis method for reliability of the unmanned aerial vehicle swarm task considering system failure includes:
step 101: adopting a complex network method to construct a network model of the unmanned aerial vehicle swarm; the network model comprises a communication layer, a structural layer and a task layer, wherein each node in the structural layer corresponds to one unmanned aerial vehicle, the node in the communication layer is determined according to communication equipment in each unmanned aerial vehicle, and the node in the task layer is determined according to the task load quantity or the type of each unmanned aerial vehicle.
Step 102: and obtaining the failure distribution of each node in the network model along with the change of time.
The obtaining of the time-varying failure distribution of each node in the network model includes:
and acquiring failure time corresponding to each node of the communication layer, and acquiring failure distribution of the communication layer.
And obtaining failure time corresponding to each node of the structural layer, and obtaining failure distribution of the structural layer.
And acquiring failure time corresponding to each node of the task layer, and acquiring failure distribution of the task layer.
Step 103: and updating the network model according to the failure distribution of each node along with the change of time.
The updating the network model according to the time-varying failure distribution of each node specifically includes:
the task time is initialized and the task interval time is determined.
And acquiring the failure distribution of each node in the network model along with the change of time in a set time period.
And traversing the network model once every the task interval time, inquiring and deleting the nodes corresponding to the task interval time, and inquiring and deleting the attacked nodes corresponding to the task interval time.
Step 104: and carrying out reliability analysis on the updated network model according to a preset task reliability index.
The task reliability indicators include vulnerability and connectivity.
The network model is constructed aiming at an unmanned aerial vehicle swarm system, the network model is shown in fig. 6, a communication network Ga in fig. 6 is a communication layer, a communication node Va represents a node in the communication layer, a structure network Gb is a structural layer, a structure node Vb represents a node in the structural layer, a task network Gc is a task layer, a task node Va represents a node in the task layer, a system failure can be mapped to a node failure of each layer in the network model, the node failures of a machine layer (structural layer), the communication layer and the task layer correspond to specific physical meanings in an actual airplane system, the failure distribution type (F) of a three-layer network node can be given according to the specific physical meanings, and the explanation is as follows:
the physical meaning of the network node of the organism layer represents the single entity of the unmanned aerial vehicle, the organism of the unmanned aerial vehicle belongs to mechanical equipment, various parts or components in the equipment have certain functions, when the three states that the parts can not work completely, can not complete the specified function according to the determined standard and can not be used reliably and safely are appeared, the organism is considered to be invalid, and the failure distribution F of the node of the organism layer is given1。
The physical meaning of the communication layer network nodes represents communication equipment carried by a single unmanned aerial vehicle, the single unmanned aerial vehicle in the unmanned aerial vehicle cluster network carries out information interaction through the communication equipment to cooperatively complete a task, when the communication equipment cannot carry out normal information transmission, the communication equipment is considered to be invalid, and the invalid distribution F of the communication layer nodes is given2。
The physical meaning of the task layer network nodes represents task loads, and tasks are transmitted among the task loads to form a complete task chain so as to complete specified tasks. If the task load can not complete the specified function of the task load, the task load is considered to be invalid, and the failure distribution F of the nodes of the task layer is given3。
According to the characteristics of a three-layer network of the unmanned plane swarm system, given specific failure distribution of a machine layer, a communication layer and a task layer, specific failure time of each node in the unmanned plane swarm system network can be obtained, and a node V of the communication layer is assumedaiHas a failure time of taiNode V of the structural layerbiHas a failure time of tbiNode V of task layerciHas a failure time of tciWhere i ∈ {1, 2,.., n }, n is the number of nodes. From TStarting at 0 moment, traversing the failure time of the three layers of nodes at each interval delta t, and finding out the corresponding failure node in the delta t.
When the simulation failure simulation is carried out on the three-layer network nodes of the unmanned aerial vehicle swarm system, the specific failure distribution of the machine body layer, the communication layer and the task layer is given according to the task characteristics of the unmanned aerial vehicle swarm system and by combining the scenes of failure distribution application. Selecting a degradation model (failure model) which follows exponential distribution to carry out prediction (structural layer) on a single machine corresponding to each node of the unmanned aerial vehicle cluster network; selecting a degradation model which obeys inverse Gaussian distribution to carry out prediction (task layer) on task loads corresponding to all nodes of the unmanned aerial vehicle cluster network; and selecting a degradation model which obeys Weibull distribution to carry out prediction (communication layer) on communication equipment corresponding to each node of the unmanned aerial vehicle cluster network.
The following describes in detail a reliability analysis method for a drone swarm task considering system failure according to the present invention, and a detailed flow is shown in fig. 3.
Step1, constructing a network model: selecting a control structure of a certain unmanned aerial vehicle swarm, respectively generating n nodes for a communication layer, a structural layer and a task layer, specifying the number m of types of task loads, and building an unmanned aerial vehicle swarm model by using a complex network method.
Step2, failure distribution: respectively selecting proper failure distribution F according to the degradation failure characteristics of the communication layer, the structural layer and the task layeriI is 1,2,3, and then the communication layer node V is obtainedaiHas a failure time of taiNode V of the structural layerbiHas a failure time of tbiNode V of task layerciHas a failure time of tciWhere i ∈ {1, 2,.., n }, n is the number of nodes.
Step3, updating the network model:
a. time initialization, and task time T is 0.
b. The task interval time at is determined.
c. Discretizing: event of node failure occurring within i.Δ t, Ei＝{ei1(t)，ei2(t)，...eil(t) }, t ∈ i.Δ t, discretized into the i.Δ t timeNode failure event E occurredi＝{ei1，ei2，...eil}。
d. And traversing the failure nodes of the three-layer network every delta t time, searching the failure nodes in the corresponding delta t time, and deleting the failure nodes in the cluster network (network model).
The distribution of the failed nodes and the attacked nodes in the network model is shown in fig. 4, and the discrete distribution of the failed nodes and the attacked nodes in the network model is shown in fig. 5.
Under a given task profile (a time sequence description of events and environments experienced during the period of completing a specified task), the distribution of network model nodes which are disabled due to system failure, external "attacks" and the like in time is shown in fig. 4: over time, drone swarm systems can be subject to various types of attacks or failures with uncertainty. In order to represent the reliability function, the frequency of attack, and the like of the nodes in a complex network-based swarm model 1, the events continuously occurring in the time dimension are discretized, and the events E occurring in i.delta t are discretizedi＝{ei1(t)，ei2(t), eil (t), t ∈ i · Δ t, discretized as event E occurring at time i · Δ ti＝{ei1，ei2，...eilAs shown in fig. 5.
Step4, evaluating task reliability: and selecting proper task reliability indexes, such as vulnerability (P), connectivity (G) and the like, to evaluate the task reliability of the unmanned aerial vehicle swarm through a network model.
And if the task reliability index is selected as connectivity, the calculation formula of the connectivity is the ratio of the number of the current task chains to the number of the initial task chains. The mission reliability of the drone swarm is represented by connectivity.
Fig. 2 is a schematic structural diagram of an unmanned aerial vehicle swarm task reliability analysis system considering system failure, as shown in fig. 2, the system includes:
a network model building module 201, configured to build a network model of the drone swarm by using a complex network method; the network model comprises a communication layer, a structural layer and a task layer, wherein each node in the structural layer corresponds to one unmanned aerial vehicle, the node in the communication layer is determined according to communication equipment in each unmanned aerial vehicle, and the node in the task layer is determined according to the task load quantity of each unmanned aerial vehicle.
A failure distribution obtaining module 202, configured to obtain a failure distribution of each node in the network model over time.
And the network model updating module 203 is configured to update the network model according to the time-varying failure distribution of each node.
And the reliability analysis module 204 is configured to perform reliability analysis on the updated network model according to a preset task reliability index.
The failure distribution obtaining module 202 includes:
and the communication layer failure distribution acquisition unit is used for acquiring failure time corresponding to each node of the communication layer and acquiring the communication layer failure distribution.
And the structural layer failure distribution acquisition unit is used for acquiring failure time corresponding to each node of the structural layer and acquiring structural layer failure distribution.
And the task layer failure distribution acquisition unit is used for acquiring failure time corresponding to each node of the task layer and acquiring the task layer failure distribution.
Optionally, the task reliability indicators include vulnerability and connectivity.
The network model updating module 203 specifically includes:
and the initialization unit is used for initializing the task time and determining the task interval time.
And the failure distribution acquisition unit is used for acquiring the failure distribution of each node in the network model along with the change of time in a set time period.
And the first network model updating unit is used for traversing the network model once every task interval time, inquiring and deleting nodes corresponding to the task interval time.
Optionally, the system further comprises:
and the second network model updating unit is used for traversing the network model once every task interval time, inquiring and deleting the attacked nodes corresponding to the task interval time.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. An unmanned aerial vehicle swarm task reliability analysis method considering system failure is characterized by comprising the following steps:
adopting a complex network method to construct a network model of the unmanned aerial vehicle swarm; the network model comprises a communication layer, a structural layer and a task layer, wherein each node in the structural layer corresponds to one unmanned aerial vehicle, the node in the communication layer is determined according to communication equipment in each unmanned aerial vehicle, and the node in the task layer is determined according to the task load quantity of each unmanned aerial vehicle;
obtaining failure distribution of each node in the network model along with time variation;
updating the network model according to the failure distribution of each node along with the change of time;
performing reliability analysis on the updated network model according to a preset task reliability index;
the updating the network model according to the time-varying failure distribution of each node specifically includes:
initializing task time and determining task interval time;
obtaining failure distribution of each node in the network model along with time change in a set time period;
and traversing the network model once every the task interval time, inquiring and deleting the failure nodes corresponding to the task interval time.
2. The method for analyzing reliability of unmanned aerial vehicle swarm tasks with system failure according to claim 1, wherein the obtaining of the failure distribution of each node in the network model over time comprises:
obtaining failure time corresponding to each node of the communication layer, and obtaining failure distribution of the communication layer;
obtaining failure time corresponding to each node of the structural layer, and obtaining failure distribution of the structural layer;
and acquiring failure time corresponding to each node of the task layer, and acquiring failure distribution of the task layer.
3. The method of analyzing mission reliability of a swarm of unmanned aerial vehicles with system failure as claimed in claim 1, wherein the mission reliability indicators include vulnerability and connectivity.
4. The method for analyzing reliability of drone swarm mission with system failure according to claim 1, further comprising:
and traversing the network model once every the task interval time, inquiring and deleting the attacked nodes corresponding to the task interval time.
5. An unmanned aerial vehicle swarm task reliability analysis system considering system failure, the system comprising:
the network model building module is used for building a network model of the unmanned aerial vehicle swarm by adopting a complex network method; the network model comprises a communication layer, a structural layer and a task layer, wherein each node in the structural layer corresponds to one unmanned aerial vehicle, the node in the communication layer is determined according to communication equipment in each unmanned aerial vehicle, and the node in the task layer is determined according to the task load quantity of each unmanned aerial vehicle;
the failure distribution obtaining module is used for obtaining the failure distribution of each node in the network model along with the change of time;
the network model updating module is used for updating the network model according to the failure distribution of each node along with the change of time;
the reliability analysis module is used for carrying out reliability analysis on the updated network model according to a preset task reliability index;
the network model updating module specifically includes:
the initialization unit is used for initializing task time and determining task interval time;
the failure distribution acquisition unit is used for acquiring failure distribution of each node in the network model along with time change in a set time period;
and the first network model updating unit is used for traversing the network model once every task interval time, inquiring and deleting the failure nodes corresponding to the task interval time.
6. The unmanned aerial vehicle swarm task reliability analysis system of system failure of claim 5, wherein the failure distribution obtaining module comprises:
a communication layer failure distribution obtaining unit, configured to obtain failure time corresponding to each node of the communication layer, and obtain communication layer failure distribution;
the structural layer failure distribution acquisition unit is used for acquiring failure time corresponding to each node of the structural layer and acquiring structural layer failure distribution;
and the task layer failure distribution acquisition unit is used for acquiring failure time corresponding to each node of the task layer and acquiring the task layer failure distribution.
7. The system-failure drone swarm task reliability analysis system of claim 5, wherein the task reliability indicators comprise vulnerability and connectivity.
8. The system of claim 5, wherein the system further comprises:
and the second network model updating unit is used for traversing the network model once every task interval time, inquiring and deleting the attacked nodes corresponding to the task interval time.
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