CN113110603A - Unmanned aerial vehicle cluster task reliability analysis method based on cluster fault - Google Patents
Unmanned aerial vehicle cluster task reliability analysis method based on cluster fault Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle cluster task reliability analysis method based on cluster faults, which comprises the following steps: s1, finding cluster faults from the aspects of functional entities, information interaction, use modes and processes, and determining fault types to be considered in all layers of the unmanned aerial vehicle cluster; s2, obtaining the fault cause of the cluster fault through a fault criterion; s3, measuring the reliability of the unmanned aerial vehicle cluster according to the cluster fault, decomposing the cluster task into stage tasks, dividing a certain stage task of the cluster into a plurality of sub-stage tasks which are continuous in time and do not overlap with each other, building a reliability model for each sub-stage task, calculating the reliability of the cluster task, and finding out reasons influencing the reliability of the task. The method provides feedback for unmanned aerial vehicle equipment system design and task planning by finding out main reasons influencing task reliability in the stage task, and is used for guiding selection of equipment entities, correction of performance parameters and adjustment of hierarchical structure relation.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle cluster task reliability analysis method based on cluster faults.
Background
An Unmanned Aerial Vehicle (UAV) is also called an Unmanned Aerial Vehicle (UAV), and is an UAV system integrating an UAV, an airborne sensor, airborne task equipment, a command control system, a communication system, a measurement and control system, a comprehensive guarantee system and the like. The unmanned aerial vehicle cluster is composed of a plurality of unmanned aerial vehicles with limited autonomous capability, and the unmanned aerial vehicles generate an integral effect through mutual communication under the condition of no centralized command control through an ad hoc mechanism, so that autonomous cooperation with a higher degree is realized, and an expected task target can be completed under the intervention of personnel as few as possible. The unmanned aerial vehicle cluster not only has wide application in the military aspect, but also has a brand-new head corner in civil fields such as logistics, agriculture, emergency rescue, remote sensing and earth observation, pipeline inspection and the like.
With the change of task environment, the task target and priority of the cluster unmanned aerial vehicle are changed dynamically, and members in the cluster continuously join, break down or quit in the task. Under the actual use background that the task and the structure are changeable, the reliability of the unmanned aerial vehicle cluster is more important, and the reliability problem is more complex. However, currently, the reliability of the unmanned aerial vehicle clustering task is not studied in the prior art. The concept of cluster reliability is the basis of quantitative reliability research, and has important significance for reasonable configuration of unmanned aerial vehicle clusters and effective improvement of task completion capability.
Disclosure of Invention
The invention provides an unmanned aerial vehicle cluster task reliability analysis method based on cluster faults, aims to solve the problem that the reliability of an unmanned aerial vehicle cluster task is not researched in the prior art, realizes the research on the reliability of the unmanned aerial vehicle cluster task, finds out main reasons influencing the task reliability, and improves the task completion capability by reasonably configuring the unmanned aerial vehicle cluster.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
The unmanned aerial vehicle cluster task reliability analysis method based on cluster faults comprises the following steps:
s1, finding cluster faults from the aspects of functional entities, information interaction, use modes and processes, and determining fault types to be considered in all layers of the unmanned aerial vehicle cluster;
s2, obtaining a fault cause of cluster faults through fault criteria, and determining the specified functions of all layers of the unmanned aerial vehicle cluster in the tasks;
s3, measuring the reliability of the unmanned aerial vehicle cluster according to the cluster fault, decomposing the cluster task into stage tasks, dividing a certain stage task of the cluster into a plurality of sub-stage tasks which are continuous in time and do not overlap with each other, building a reliability model for each sub-stage task, calculating the reliability of the cluster task, and finding out reasons influencing the reliability of the task.
Further optimizing the technical solution, in step S1, the cluster failure includes:
and (3) entity failure: mainly refers to the faults of cluster hardware and software, including the faults of communication components;
and (3) information transmission failure: the problems of information transmission delay, content error or loss occur in the task of the cluster, and the state of the cluster cannot meet the specified requirements; and
process failure: the reaction process of the clusters is slow enough to affect the task completion.
Further optimize technical scheme, unmanned aerial vehicle cluster includes:
unit layer: single piece of equipment capable of independently completing simple task activities;
platform layer: carrying various unmanned aerial vehicle systems with different equipment and different capabilities for a common task;
cluster layer: according to the capability and the characteristics of the unmanned aerial vehicle, the unmanned aerial vehicle platforms completing different tasks complete different tasks according to a task cluster formed by certain establishment relations; and
system layer: the unmanned aerial vehicle system is composed of various unmanned aerial vehicle clusters.
In the technical scheme, the unit layer and the platform layer only consider entity faults, and the cluster layer and the system layer need to consider entity faults, information transmission faults and process faults.
Further optimizing the technical scheme, the failure of the entity failure is caused by: the reliability level of the software and hardware, natural environment, battlefield environment, use strength, human factors and collision;
the failure of the information transmission failure is due to: the communication component has insufficient performance or unreasonable configuration and improper protocol;
the failure of a process failure results from: the use strategy, the rule and the service flow are not properly set, the deployment position is not reasonable, the task planning scheme is not reasonable, the flight path design is not proper, the formation is not proper and the operation flow arrangement is not proper.
Further optimizing the technical solution, the step S3 specifically includes the following steps:
s31, stage task decomposition;
s32, determining a fault criterion;
s33, determining a task time model;
s34, determining a task environment;
s35, establishing a task reliability block diagram;
s36, establishing a mathematical model of task reliability in the cluster stage, respectively establishing calculation models of entity faults, information transmission faults and process faults, and calculating and correcting.
Further optimizing the technical solution, in the step S36, the reliability of the cluster stage task is:
the cluster task can be decomposed into K stage tasks, and the reliability R of the stage task KQk is calculated using the following formula:
Further optimizing the technical scheme and the information transmission reliability
Calculated using the following formula:
wherein the content of the first and second substances,
representing the information transmission delay of the phase task k,
indicating the allowed information transfer delay of the phase task k,
indicating the information accuracy of the phase task k,
indicating the information accuracy required by the phase task k,
represents the rate of information loss for the phase task k,
indicating the allowed information loss rate, N, of the phase task kANumber of information N representing simultaneous satisfaction of 3 requirementsSMRepresents the total number of messages transmitted;
stage task k information average transmission delay
Is calculated as follows:
wherein TSiFor the I-th information transmission delay, I is 1, 2, …, I; and I is the total category number of the information.
Further optimizing the technical scheme and the process reliability
Calculated using the following formula:
wherein the content of the first and second substances,
represents the average task on-line response time of the phase task k,
indicating the task online response time allowed by the phase task k. N is a radical ofXYRepresenting the number of times that the on-line response time meets the task requirements when the task change changes in the phase task k, NXYSMRepresenting the total number of task changes in the phase task k;
wherein, TxyiFor the response time of the phase task k at the ith task change, i is 1, 2, …,m; and m is the total number of task changes.
Further optimizing the technical scheme, the task reliability R of the groupQThe calculation is as follows:
ωkthe weight of the phase task k is represented,
due to the adoption of the technical scheme, the technical progress of the invention is as follows.
According to the method, the cluster faults are divided into three types, namely entity faults, information transmission faults and process faults, so that the specified functions of all layers of the unmanned aerial vehicle cluster in the tasks are determined, the definition and measurement model of the reliability of the unmanned aerial vehicle cluster is obtained, main reasons influencing the reliability of the tasks in the stages are found out through calculation and correction of the model, reference is provided for reliability research of the unmanned aerial vehicle cluster, feedback is provided for design and task planning of an unmanned aerial vehicle equipment system, and the method is used for guiding selection of equipment entities, correction of performance parameters and adjustment of hierarchical structure relations and has important significance for effectively improving reasonable configuration and task completion capability of the unmanned aerial vehicle cluster.
Drawings
FIG. 1 is a schematic diagram of the unmanned aerial vehicle cluster configuration of the present invention;
FIG. 2 is a task decomposition diagram of the cluster defense penetration stage of the present invention;
FIG. 3 is a diagram of a model of the unmanned aerial vehicle cluster task timing sequence of the present invention;
FIG. 4 is a block diagram of cluster sub-phase task reliability in accordance with the present invention;
FIG. 5 is a block diagram of the reliability of the sub-phase task 1 of the unmanned aerial vehicle system for reconnaissance according to the present invention;
FIG. 6 is a block diagram of the reliability of the sub-phase task 1 of the unmanned aerial vehicle attacking system 1 of the present invention;
FIG. 7 is a block diagram of the reliability of the sub-phase task 1 of the unmanned aerial vehicle system 2 attack according to the present invention;
fig. 8 is a block diagram of the reliability of the sub-phase task 2 of the reconnaissance drone system of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the figures and specific examples.
1. Unmanned aerial vehicle cluster constitution
The unmanned aerial vehicle cluster can be divided into four levels, namely a system layer, a cluster layer, a platform layer and a unit layer.
Unit layer: a single piece of equipment capable of independently performing simple task activities. Such as unmanned aerial vehicles, airborne projectiles, added-hanging reconnaissance devices, ground control stations, and the like.
Platform layer: for a common task, various drone systems with different capabilities are piggybacked on different equipment.
Cluster layer: according to the capability and characteristics of the unmanned aerial vehicle, the unmanned aerial vehicle platform for completing different tasks forms a larger system according to a certain establishment relation. If the unmanned aerial vehicle clusters are detected according to the function, the unmanned aerial vehicle clusters are deceived to interfere, and the east unmanned aerial vehicle cluster and the middle unmanned aerial vehicle cluster are distinguished according to the regions. The unmanned aerial vehicles can take off from a uniform place, can take off from different places, and even adopt different delivery modes to belong to different building units.
System layer: the unmanned aerial vehicle system is composed of various unmanned aerial vehicle clusters. The system layer can be regarded as a cluster of the cluster and is an advanced expression form of the unmanned plane cluster. The system layer and the cluster layer have similar network properties and show the time and space dynamic characteristics of random association of large-scale nodes, and the system layer characteristics are not considered independently in the invention.
The boundaries and composition of the drone cluster, and the relationship between the compositions are schematically shown in fig. 1. For example, a set of XX drone systems includes: 4 unmanned aerial vehicles, 1 ground control station and 1 set of ground-air data distribution system, 1 AGM-114 antitank missile, airborne reconnaissance loads such as airborne photoelectricity/infrared, laser ranging and irradiating devices and synthetic aperture radar, and an airborne data link system including GPS/strapdown combined inertial navigation such as LN-100, C-band visual data link and satellite data link. This constitutes a platform level combat unit, and sets of XX drone systems may form a drone cluster, or be combined with other types of drone systems. The unmanned aerial vehicles can take off from a uniform place or different places, and even adopt different delivery modes and belong to different building units. The drone cluster may also be part of a larger weaponry hierarchy.
In an unmanned aerial vehicle system, a ground control command subsystem generally consists of one or more operation control substations and mainly realizes control, task equipment and load operation, data receiving and distributing analysis and partial system maintenance of an aircraft. The ground terminal, which is also a data link, is responsible for uplink and downlink information transmission and communication with other unmanned aerial vehicle systems during task execution. The launch and recovery subsystem is responsible for launching (takeoff) and recovering (landing) of the aircraft. The mission equipment is additionally hung on the aircraft, and the latter is the support of the former. The support subsystem plays a supporting role in the use, maintenance and repair of the unmanned aerial vehicle system.
The unmanned aerial vehicle system is clustered according to functions, executed tasks and a task airspace, new common tasks can be merged, formed and expanded again along with the change of the tasks, the environment and the like in the tasks, and old members quit and new members join. Thus, the drone cluster is not a solid building unit, but rather a loose, dynamic organization formed around the mission.
An unmanned aerial vehicle cluster task reliability analysis method based on cluster faults comprises the following steps:
s1, finding cluster faults from the aspects of functional entities, information interaction, use modes and processes, and determining fault types to be considered by all layers of the unmanned aerial vehicle cluster.
S2, obtaining the fault cause of the cluster fault through the fault criterion, and determining the specified functions of each layer of the unmanned aerial vehicle cluster in the task.
S3, measuring the reliability of the unmanned aerial vehicle cluster according to the cluster fault, decomposing the cluster task into stage tasks, dividing a certain stage task of the cluster into a plurality of sub-stage tasks which are continuous in time and do not overlap with each other, building a reliability model for each sub-stage task, calculating the reliability of the cluster task, and finding out reasons influencing the reliability of the task.
2. Cluster failure
Reliability analysis, evaluation, is developed around the fault. GJB451A defines for failure: a state in which the product cannot perform a prescribed function. In particular, it means that the hardware or software of the component, device, etc. of the product loses its intended function or that one/several performance parameters cannot be kept between the required upper and lower limits. Conventional reliability analysis therefore primarily considers failures caused by hardware/software functional failures.
From the task requirement, in the process of integrating various devices and platforms into a cluster, three aspects of basic work are performed: firstly, information interaction and sharing are realized among different platforms and clusters; integrating the functional entities of all the members to realize function complementation and superposition; and thirdly, flexibly combining the activities and the control behaviors of different platforms and systems to form a use flow meeting the requirements of specific tasks. For a complex object such as a cluster, all hardware/software of the complex object is reliable, and the cluster cannot be guaranteed to be reliable, for example, a specified function cannot be executed by the cluster due to information transmission errors under the condition that a communication link has no fault; the underlying local hardware/software failure may not necessarily result in the cluster failing to perform the intended task, because other functional entities may be replaced, or the local hardware failure may not be enough to cause the cluster to be disabled. Therefore, the failure of the cluster needs to be considered from three aspects of functional entities, information interaction, use modes and flows.
In step S1, the cluster failure includes: entity failure, information transmission failure, process failure.
And (3) entity failure: the connotation of functional entity failure is consistent with traditional failure, mainly refers to the failure of cluster hardware and software, including communication component failure, which is called entity failure.
And (3) information transmission failure: the cluster has the problems of information transmission delay, content error or loss in the task and cannot reach the state required by the regulation. Unmanned aerial vehicles form an unmanned aerial vehicle cluster capable of completing complex tasks, and node member networking and network communication are the basis for realizing the cluster, so that the unmanned aerial vehicle cluster is a dynamic mobile communication network firstly. Task processes, various instructions, status data of equipment units, various task environment data, etc. are transmitted and shared in this communication network. In the same task, the data transmission paths selected in different task scenes are dynamically changed, and the dynamic combination of multiple subtasks is embodied as the change of transmission data and the change of the transmission paths. The failure associated therewith is referred to as an "information transmission failure".
Process failure: the cluster use flow refers to the time sequence and logic relation of execution of various activities in different task implementation processes, and formed calling flow and control flow of various resources (software, hardware and personnel). The performance of the cluster process determines the task implementation efficiency to a great extent, an important index of the process performance is response time, and the shorter the response time is, the smoother the cluster task is executed.
This response time mainly includes: task issuing time, preparation time of each member, processing time of each task node, information transmission time, delay time of resources and the like. The response process is related to the use strategy, the operation rule, the deployment, the use mode, the task planning, the flight path design and the like of the cluster, and is also related to the number of members participating in the task, the number of activities participated by the members, the number of nodes of the task and the like. A "process fault" is considered to have occurred when the reactive process of the cluster is so slow as to affect the completion of the task.
And (3) defining cluster faults:
applying the GJB451A definition, a cluster failure refers to a state where the cluster is unable to perform a specified function. However, the hierarchy of the cluster dictates that the functions discussed at each level are different.
The unit layer function is mainly the software and hardware use function generated by each equipment unit according to the use of the equipment unit, and the use function can be upwards aggregated step by the unit layer-platform layer-cluster layer. The functional entities are mainly concentrated on a unit layer, and all layers above the unit layer only contain the equipment. Thus, when software or hardware fails, the unit is affected to perform the specified function. Therefore, only entity failure occurs in the unit layer, and the failure can be transmitted to the upper layers, thereby affecting the overall reliability level of the cluster.
The platform layer provides internal information interaction for each subordinate functional entity, and the function loss can be regarded as communication equipment failure and belongs to software and hardware failures when seen from the inside of an unmanned aerial vehicle system. In addition, the use and maintenance operation activities of the subordinate functional entities generally have fixed programs which are regarded as input in the invention, so that platform layer information transmission faults and process faults are not considered any more.
The cluster layer/system layer integrates the functions of each equipment unit entity according to a specific open protocol, standard or specification under a unified framework of an overall function target to form a functional body capable of completing a specific task. The specific requirements are as follows:
1) providing interoperability among functional entities, carrying out information interaction on a peer-to-peer layer, and realizing functional interoperability by adopting common application software, common interfaces and components, common application services and the like among different platforms as much as possible;
2) providing reusability and portability of functional entities in a distributed environment;
3) the distribution transparency of subordinate functional entities is provided, the complexity brought by the system distribution is reduced, and the coupling among the functional entities is reduced;
4) providing cluster flow control, and realizing process control through system security service and user authority judgment;
5) and the system participates in the functional recombination of the cluster, and can dynamically arrange and recombine each functional entity in the face of different tasks.
The types of failures that may occur at the various levels of the cluster are not the same. The unit layer mainly shows the software and hardware faults of the equipment, so that only the entity faults are considered. Each type of equipment in the platform layer has respective operation flow, and information interaction also exists among various types of equipment. However, these processes and interactions are built into the system during the development and production process of the drone system, and therefore the quality of these processes and information interactions will not be discussed and evaluated in the present invention, and will be regarded as input. Thus, the platform layer also only considers physical failures.
The cluster layer and the system layer are required to face different tasks and task environments, suitable unmanned aerial vehicle platforms and various task equipment can be selected from equipment in a full-centralized mode, and an unmanned aerial vehicle cluster is quickly constructed through integrated configuration. In the process of implementing the task, the platform, the weapon and the like can have war losses, faults or be repaired and supplemented, and members can continuously join and leave the cluster, so that the updating system is required to continuously execute the task through resource reallocation, function reconfiguration and flow reconstruction. This is a dynamic process that changes constantly, and information transmission between platforms and between systems is required in this process. Therefore, three types of faults of the cluster layer and the system layer need to be considered. It should be noted that: the equipment entities are positioned in the unit layer, so that the entity faults actually occur in the unit layer, the higher level on the unit layer only contains different equipment entity ranges, and the higher level fault level is obtained by aggregation starting from the entity fault level of the unit and combining equipment and task relationships of all levels.
In step S2, 1) entity failure:
and (3) fault expression: hardware and software failures of the cluster, including open circuit failures of communication network hardware devices, connections and software.
The cause of the fault: the reliability level of the software and the hardware, the natural environment, the battlefield environment, the use strength, human factors, collision and the like.
The fault influence is as follows: the basic connectivity function of the cluster communication network is affected, resulting in information transmission failure, or directly affecting the execution of unmanned aerial vehicle combat/use tasks.
And (3) fault criterion: the method is the same as the traditional reliability fault criterion.
The failure eliminating mode comprises the following steps: repairing or replacing the equipment and adjusting the communication architecture.
2) And (3) information transmission failure:
and (3) fault expression: slow transmission of information, content errors or losses.
The cause of the fault: insufficient performance or improper configuration of the communication means itself, improper protocol, etc., e.g., insufficient bandwidth.
The fault influence is as follows: clustering process failures or task failures.
And (3) fault criterion: and selecting parameters such as transmission time, transmission rate, bit error rate, information accuracy, information loss rate and the like from three aspects of information transmission integrity, accuracy and timeliness, and regarding the fault when the performance parameters cannot be kept between the required upper limit and the required lower limit. These parameters are used to evaluate the integrity, correctness, and timeliness of the cluster information transfer across platforms/clusters, which also reflects that the platform layer does not consider information transmission failure.
The failure eliminating mode comprises the following steps: repairing or replacing equipment, changing or perfecting a corresponding protocol, and optimizing corresponding equipment deployment.
3) Process failure:
and (3) fault expression: the command control is not smooth, the coordination among all members is not smooth, the cluster response is slow, and the resources conflict.
The cause of the fault: the use strategy, the rule and the service flow are not properly set, the deployment position is not reasonable, the task planning scheme is not reasonable, the track design is not proper, the formation is not proper, the battle flow arrangement is not proper, and the like.
The fault influence is as follows: the influence cycle is long and profound.
And (3) fault criterion: performance parameters such as task online response time, flow reconstruction time and the like cannot be kept within a required time limit.
The failure eliminating mode comprises the following steps: and adjusting and optimizing the processes, rules, strategies, configurations and the like.
The physical faults in the three types of faults are taken as the basis and can be transmitted layer by layer. When communication software and communication hardware have faults, the connectivity of the cluster is influenced, and further information transmission faults are caused. The information transmission inevitably affects the smooth implementation of the battle process. When the equipment has an entity failure, a new member joins or an old member exits, which may cause task re-planning and formation and process re-arrangement, possibly resulting in process failure.
The main performance parameters are:
time delay of information transmission: the indicator reflects the time of information transmission in the transmission channel. Depending on the speed of information transfer, bandwidth, network architecture, routing, information capacity, communication protocol compatibility, etc. of the data link.
Transmission rate: the total amount of data transferred by the data path per unit time.
Error rate: the measure of the accuracy of data transmission in a specified time is expressed by the percentage of bit errors in transmission to the total number of transmitted codes.
The information accuracy is as follows: the measure of the data transmission accuracy index in a specified time is expressed by the percentage of the received correct information quantity to the total received information quantity.
Information loss rate: the ratio of the number of messages lost to the total number of messages sent.
Flow reconstruction time: design and platform/system configuration, deployment time required for the task flow to re-run.
Task online response time: the response time refers to the time for the task to start executing. The task online response time is the time when the unmanned aerial vehicle cluster responds to the task change in the process of executing the task.
In step S3, reliability of the cluster:
applying traditional reliability definition to give a cluster reliability definition: the cluster is capable of performing the specified functions under the specified conditions and within the specified time. The product is unreliable or has a fault. When the fault of the cluster is clarified, the reliability of the whole cluster can be measured. There are other parameters to measure cluster reliability.
The reliability of the drone cluster is constantly changing over the course of the task time. This evolution process includes the evolution of cluster constituents and the evolution of cluster structure. The reasons for element evolution are mainly upgrading and updating of the platforms forming the cluster and replacement of old platforms by new platforms with new technologies. In the task, only dynamic evolution is considered in cluster structure evolution, and cluster reliability evolution mainly refers to reliability evolution occurring along with the dynamic evolution of the cluster structure. Mainly comprises the following steps:
1) the task change causes the task intensity, the task amount to change, or the environment (including natural environment, battlefield environment, electromagnetic environment) to change drastically, which causes the reliability of the equipment and the communication link of the cluster to change;
2) equipment in a task exits due to damage or failure, and new equipment is added, so that the membership relationship, namely the cluster structure, is changed, and the cluster reliability is changed;
3) the process failure caused by the flow rearrangement in the task causes the cluster reliability change.
A reliability relation model among members is established layer by layer when cluster reliability is analyzed, a platform layer is analyzed from bottom to top, and the relationship among the members is considered more in the system layer and the system layer from the transverse direction. Drone cluster reliability may analyze evolutionary trends from the quantitative variation of its parameters over time.
3. Cluster task reliability modeling
3.1 modeling analysis
The unmanned aerial vehicle cluster is composed of a plurality of unmanned aerial vehicle systems and cluster communication links. In the process of executing tasks by a cluster, even if the working/stopping states of all components can be clearly judged, the influence of the working/stopping states on the whole cluster cannot be determined, and the process is a 'black box' and can only judge the success or failure of the tasks at the task execution end point.
In fact, the "loose coupling" nature of the clusters in performing a certain stage of a task directly results in a constant change in the configuration of the participating equipment or units in performing the task. Then, if a task at a certain stage of the cluster can be divided into a plurality of sub-task stages according to different time nodes and different configurations, the invariance of the configuration is met on the sub-task stage, and the reliability can be calculated by applying a traditional modeling method. For a certain stage task of the cluster, the task is cut off according to the starting time and the ending time of the task activity according to different task activities of components (all unmanned aerial vehicle systems), and the task can be divided into a plurality of sub-stage tasks which are continuous in time and do not overlap with each other, and the sub-stage tasks meet the principle that the configuration during the task is not changed. And (3) constructing a reliability model for each sub-stage task, so that the change condition of the cluster for the relevant reliability of each sub-stage task can be obtained. And further mastering the reliability change rule of the task at a certain stage of the whole cluster. For example, a task in the penetration stage of a certain unmanned aerial vehicle cluster is executed by three unmanned aerial vehicle systems, a reconnaissance unmanned aerial vehicle system has two task activities of target positioning and penetration guidance, and an attack unmanned aerial vehicle has two activities of battle treatment and penetration. According to the starting time and the ending time of the task activities of the three, the break-through stage task is divided into 3 sub-stage tasks, as shown in fig. 2.
3.2 model construction
The model construction specifically comprises the following steps:
s31, stage task decomposition;
s32, determining a fault criterion;
s33, determining a task time model;
s34, determining a task environment;
s35, establishing a task reliability block diagram;
s36, establishing a mathematical model of task reliability in the cluster stage, respectively establishing calculation models of entity faults, information transmission faults and process faults, and calculating and correcting.
In step S36, the cluster stage task reliability:
the cluster task can be decomposed into K stage tasks, and the reliability R of the stage task KQk is calculated using the following formula:
Reliability of information transmission
Calculated using the following formula:
wherein the content of the first and second substances,
representing the information transmission delay of the phase task k,
indicating the allowed information transfer delay of the phase task k,
indicating the information accuracy of the phase task k,
indicating the information accuracy required by the phase task k,
represents the rate of information loss for the phase task k,
indicating the allowed information loss rate, N, of the phase task kANumber of information N representing simultaneous satisfaction of 3 requirementsSMRepresents the total number of messages transmitted;
stage task k information average transmission delay
Is calculated as follows:
wherein TSiFor the I-th information transmission delay, I is 1, 2, …, I; and I is the total category number of the information.
The information accuracy is used for evaluating the accuracy of the cross-platform/cluster information transmission of the system. Information for different purposes in a task has different requirements on the correctness of the information, such as that a combat order must be retransmitted once the transmission is wrong. Let ZP be the accuracy of certain i-class informationi
ZPi＝(1-Ne)/Nr (4)
Wherein N iseCounting the number of i-type information errors received within a time period; n is a radical ofrThe total amount of the i-type information received in the statistical time. Stage task k average information accuracy
Is calculated as follows:
the information loss rate is used to measure whether the information sent and received between the various drone systems can be completely transferred. Because each unmanned aerial vehicle system in the system requires differently to the information quality of different grade type, consequently to the information loss rate requirement also not the same. For example, the continuity of the target indication information is more demanding than the continuity of the battlefield situation information. Let the i-type information loss rate be DPi
DPi＝N1/Ns (6)
Wherein N is1Counting the number of i-type information loss in time; n is a radical ofsThe number of i-type information transmission in the time is counted. Stage task k average information loss rate
Is calculated as follows:
degree of process reliability
Calculated using the following formula:
wherein the content of the first and second substances,
represents the average task on-line response time of the phase task k,
indicating the task online response time allowed by the phase task k. N is a radical ofXYRepresenting the number of times that the on-line response time meets the task requirements when the task change changes in the phase task k, NXYSMRepresenting the total number of task changes in the phase task k;
wherein, TxyiThe response time of the ith task of the phase task k is 1, 2, …, m; and m is the total number of task changes.
Task reliability R of a groupQThe calculation is as follows:
ωkthe weight of the phase task k is represented,
if the mission gap of the group phase mission allows maintenance, such as between the pre-mission preparation phase and long haul phase, the mission reliability is calculated using a weighted summation model. If the phase task is continuous and not modifiable, product model calculations are employed. If the two conditions are mixed, the product model calculation is adopted for the tasks in the continuous stages, and then the tasks are weighted and summed with other parts as a whole.
4. Cluster task reliability calculation embodiment
4.1 task description
The embodiment is an unmanned aerial vehicle cluster multi-target cooperative investigation and task i. The unmanned aerial vehicle cluster comprises 1 reconnaissance unmanned aerial vehicle system (including 3 homotype reconnaissance aircraft), 2 attack unmanned aerial vehicle systems (4 homotype attack aircraft of each system). After the unmanned aerial vehicle cluster takes off from the take-off and landing area, the unmanned aerial vehicle cluster flies along a preset air route to reach a task area in a dense formation mode, then the targets in the task area are searched, identified and positioned, and finally the identified targets are attacked. The autonomous scouting and printing task timing model of the unmanned aerial vehicle cluster is shown in figure 3. The task execution is mainly based on a sequential structure, and in the target search stage, a selection structure exists: if the reconnaissance aircraft does not search the target in the task process, all unmanned aerial vehicles return to the air; if the target is identified, the reconnaissance aircraft carries out target positioning and performs penetration guidance and striking guidance, and then the attack aircraft carries out penetration and striking. Because multiple targets need to be attacked, "blast and strike" constitutes a loop structure until the target list is empty.
4.2 task reliability calculation
4.2.1 "Surge" stage task entity reliability calculation
1) Phase task decomposition
In the cluster defense burst stage, the reconnaissance unmanned aerial vehicle system needs to complete 2 subtasks of target positioning and defense burst guiding, and the attack unmanned aerial vehicle system needs to complete 2 subtasks of fighting and defense burst. In combination with the start-stop time of the sub-tasks, the cluster defense break-through phase is divided into 3 sub-phase tasks that are executed sequentially, as shown in table 1.
2) Determining fault criteria
The sub-system that participates in the sub-phase task fails to affect the task execution.
3) Determining a task time model
TABLE 1 UAV Cluster defense penetration stage task time model description
4) Determining task environments
And (3) natural environment: the altitude is 20-60 meters, the general altitude of the mountainous region is 1000-1500 meters, the mountainous region belongs to warm-temperate zone semi-humid continental monsoon climate, and rainstorm often occurs in 7 and 8 months; social and civil conditions: the civil barracks in the battle field are expanded and compiled by the actors, are full of the actors and are compiled into different teams, and have certain battle capacity. Threatened by attack of enemy helicopters and artillery.
5) Establishing task reliability block diagram
Clustering:
the reliability block diagram of the cluster in the two sub-phase tasks is the same, as shown in fig. 4.
Sub-phase task 1:
the sub-stage task 1 requires at least one target of three unmanned aerial vehicles in the reconnaissance unmanned aerial vehicle system to be successfully positioned, and a reliability block diagram is shown in fig. 5.
The sub-phase task 1 requires that all four unmanned aerial vehicles must successfully complete the task for attacking the unmanned aerial vehicle systems 1 and 2, and the reliability block diagram is shown in fig. 6 and 7.
Sub-phase task 2:
the sub-stage task 2 requires that at least one of three unmanned aerial vehicles of the reconnaissance unmanned aerial vehicle system succeeds in emergency guidance, and a reliability block diagram is shown in fig. 8.
The sub-stage task 2 requires that four unmanned aerial vehicles must complete the task successfully for attacking the unmanned aerial vehicle systems 1 and 2, and the reliability block diagram is the same as that of the sub-stage task 1.
6) Establishing a mathematical model and correcting
If the penetration stage is the kth stage of the cluster, the reliability of the task entity in the stage is
Let the entity reliabilities of cluster sub-stage tasks 1 and 2 be
And
then there is
Wherein the content of the first and second substances,
and
entity reliability of the reconnaissance unmanned aerial vehicle system in sub-phase tasks 1 and 2 respectively;
and
the entity reliability of the attack unmanned aerial vehicle system 1 in the sub-stage tasks 1 and 2 is respectively determined;
and
the entity reliabilities of the attacking drone system 2 at sub-phase tasks 1 and 2, respectively.
Wherein the content of the first and second substances,
and
the entity reliability of the ith photoelectric radar and the laser guidance equipment of the reconnaissance unmanned aerial vehicle system respectively,
is the ith aircraft of reconnaissance unmanned aerial vehicle system, i ═ 1, 2, 3. RZKIs the reliability of the command control subsystem entity of the reconnaissance unmanned aerial vehicle system
wherein the content of the first and second substances,
the entity reliability of the ith aircraft attacking the unmanned aerial vehicle system is 1, 2, 3, 4, 5, 6, 7 and 8, 4 the first aircraft before numbering belongs to the attacking unmanned aerial vehicle system 1, and the rest belongs to the attacking unmanned aerial vehicle system 2. RGK1And RGK2The reliability of the control subsystem entities attacking the unmanned aerial vehicle systems 1 and 2 respectively.
And (3) assuming that the reliability of each subsystem of the unmanned aerial vehicle participating in the task is subject to exponential distribution, and the failure rate is known from empirical data, calculating the reliability to be 0.97 by adopting a mathematical model.
And the entity reliability calculation processes of tasks in other stages are the same.
4.2 task reliability of groups
And a simulation method is adopted to collect data such as information sending quantity, information transmission delay, information error quantity, information loss quantity, total times of task change, task online response time and the like, and the data is substituted into a formula to obtain the information transmission reliability and the process reliability of the penetration stage which are respectively 0.98 and 0.93. The task reliability calculation result in the defense period is 0.884.
The stage tasks of the clusters in this embodiment are continuous, non-modifiable, and are calculated using a product model. Task reliability R of a groupQThe calculation result was 0.804. Through the analysis of the duplicate disks, the process fault of the defense outburst stage, namely the response delay, is the main reason for influencing the reliability of the task.
According to the method, the cluster faults are divided into three types, namely entity faults, information transmission faults and process faults, by combining the use condition of the unmanned aerial vehicle cluster, so that the specified functions of each layer of the cluster in the task are determined, the definition and measurement model of the reliability of the unmanned aerial vehicle cluster is obtained, and reference can be provided for the research of the reliability of the unmanned aerial vehicle cluster.
Claims (10)
1. The unmanned aerial vehicle cluster task reliability analysis method based on cluster faults is characterized by comprising the following steps:
s1, finding cluster faults from the aspects of functional entities, information interaction, use modes and processes, and determining fault types to be considered in all layers of the unmanned aerial vehicle cluster;
s2, obtaining a fault cause of cluster faults through fault criteria, and determining the specified functions of all layers of the unmanned aerial vehicle cluster in the tasks;
s3, measuring the reliability of the unmanned aerial vehicle cluster according to the cluster fault, decomposing the cluster task into stage tasks, dividing a certain stage task of the cluster into a plurality of sub-stage tasks which are continuous in time and do not overlap with each other, building a reliability model for each sub-stage task, calculating the reliability of the cluster task, and finding out reasons influencing the reliability of the task.
2. The method for analyzing reliability of cluster tasks of unmanned aerial vehicle based on cluster failures as claimed in claim 1, wherein in step S1, the cluster failures include:
and (3) entity failure: mainly refers to the faults of cluster hardware and software, including the faults of communication components;
and (3) information transmission failure: the problems of information transmission delay, content error or loss occur in the task of the cluster, and the state of the cluster cannot meet the specified requirements; and
process failure: the reaction process of the clusters is slow enough to affect the task completion.
3. The cluster fault based unmanned aerial vehicle cluster task reliability analysis method of claim 2, wherein the unmanned aerial vehicle cluster comprises:
unit layer: single piece of equipment capable of independently completing simple task activities;
platform layer: carrying various unmanned aerial vehicle systems with different equipment and different capabilities for a common task;
cluster layer: according to the capability and the characteristics of the unmanned aerial vehicle, the unmanned aerial vehicle platforms completing different tasks complete different tasks according to a task cluster formed by certain establishment relations; and
system layer: the unmanned aerial vehicle system is composed of various unmanned aerial vehicle clusters.
4. The cluster fault based unmanned aerial vehicle cluster task reliability analysis method of claim 3, wherein the unit layer and the platform layer only consider entity faults, and the cluster layer and the system layer need to consider entity faults, information transmission faults and process faults.
5. The cluster fault-based unmanned aerial vehicle cluster task reliability analysis method of claim 2, wherein the fault of the entity fault is caused by: the reliability level of the software and hardware, natural environment, battlefield environment, use strength, human factors and collision;
the failure of the information transmission failure is due to: the communication component has insufficient performance or unreasonable configuration and improper protocol;
the failure of a process failure results from: the use strategy, the rule and the service flow are not properly set, the deployment position is not reasonable, the task planning scheme is not reasonable, the flight path design is not proper, the formation is not proper and the operation flow arrangement is not proper.
6. The cluster fault-based unmanned aerial vehicle cluster task reliability analysis method of claim 2, wherein the step S3 specifically comprises the following steps:
s31, stage task decomposition;
s32, determining a fault criterion;
s33, determining a task time model;
s34, determining a task environment;
s35, establishing a task reliability block diagram;
s36, establishing a mathematical model of task reliability in the cluster stage, respectively establishing calculation models of entity faults, information transmission faults and process faults, and calculating and correcting.
7. The method of claim 6, wherein in step S36, the cluster stage task reliability is:
the cluster task can be decomposed into K stage tasks and stage tasksReliability of transaction k
Calculated using the following formula:
8. The cluster fault-based unmanned aerial vehicle cluster task reliability analysis method of claim 7, wherein information transmission reliability
Calculated using the following formula:
wherein the content of the first and second substances,
representing the information transmission delay of the phase task k,
indicating the allowed information transfer delay of the phase task k,
indicating the information accuracy of the phase task k,
indicating the information accuracy required by the phase task k,
represents the rate of information loss for the phase task k,
indicating the allowed information loss rate, N, of the phase task kANumber of information N representing simultaneous satisfaction of 3 requirementsSMRepresents the total number of messages transmitted;
stage task k information average transmission delay
Is calculated as follows:
wherein TSiFor the I-th information transmission delay, I is 1, 2, …, I; and I is the total category number of the information.
9. The cluster fault-based unmanned aerial vehicle cluster task reliability analysis method of claim 7, wherein process reliability
Calculated using the following formula:
wherein the content of the first and second substances,
represents the average task on-line response time of the phase task k,
indicating the task online response time allowed by the phase task k. N is a radical ofXYRepresenting the number of times that the on-line response time meets the task requirements when the task change changes in the phase task k, NXYSMRepresenting the total number of task changes in the phase task k;
wherein, TxyiThe response time of the ith task of the phase task k is 1, 2, …, m; and m is the total number of task changes.
10. The cluster fault-based unmanned aerial vehicle cluster task reliability analysis method of claim 7, wherein task reliability R of a clusterQThe calculation is as follows:
ωkThe weight of the phase task k is represented,
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