CN113283124A - Multi-agent-based autonomous USoS participation model construction method and system - Google Patents

Multi-agent-based autonomous USoS participation model construction method and system Download PDF

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CN113283124A
CN113283124A CN202110822426.5A CN202110822426A CN113283124A CN 113283124 A CN113283124 A CN 113283124A CN 202110822426 A CN202110822426 A CN 202110822426A CN 113283124 A CN113283124 A CN 113283124A
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CN113283124B (en
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葛冰峰
陈刚
魏河川
杨志伟
李际超
杨克巍
张�浩
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National University of Defense Technology
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Abstract

The invention discloses an autonomous USoS participation model construction method based on multiple agents, which comprises the following steps: constructing a main body model; establishing an exchange relationship between a main body model of the system and the system; and introducing behavior rules to constrain the interaction relation to form an autonomous USoS participation model based on multiple agents. The participation model can autonomously control each independent system in the model, and the validity of the proposed audit participation model is checked by displaying the dynamic process of audit participation. The invention adopts a modeling method based on logic drive to express the judgment and inference rules of system evolution, constructs a cooperative mechanism of reconnaissance-communication-attack rules, focuses more on the behavior modeling of the system than the prior art, and leads the unmanned system to better accord with the actual combat requirements.

Description

Multi-agent-based autonomous USoS participation model construction method and system
Technical Field
The invention relates to the field of autonomous control, in particular to the crossing field of multi-agent cooperative control, system confrontation and combat simulation, and particularly relates to a multi-agent-based autonomous USoS participation model construction method and a multi-agent-based autonomous USoS participation model construction system.
Background
The OODA (Objective observed decision Attack) battle loop is a closed loop formed by reconnaissance equipment, decision equipment, striking equipment and other objects of an enemy and is used for completing a specific battle task. The process is not completed once, but the sharing and updating of information and the continuous execution of equipment actions are completed in a continuous loop until the goal of fighting (generally referred to as the goal of elimination) is reached. It essentially conforms to the basic behavioral specification of an agent in a complex world, namely the cyclic reciprocating process of environment-perception-decision-action-environment.
Most of the agents in the traditional USoS are homogeneous, i.e., similar in function and behavior. However, in actual combat, different types of agents have different capabilities, and the coordination mechanism among the agents is more complicated. The invention constructs a cooperative mechanism of reconnaissance-communication-attack rules based on an OODA (object oriented data acquisition) operational cycle theory, so that an unmanned system can better meet actual operational requirements.
Disclosure of Invention
The invention aims to provide a multi-agent-based autonomous USoS participation model construction method and a multi-agent-based autonomous USoS participation model construction system, so as to solve the technical defects in the prior art.
Referring to FIG. 13, the nodes are first equipped
Figure 990261DEST_PATH_IMAGE001
And (4) solid modeling.
By using the theory of operational rings, three classes are constructed for the operational system
Figure 870492DEST_PATH_IMAGE001
Models, respectively investigation
Figure 292115DEST_PATH_IMAGE002
And make a decision
Figure 181574DEST_PATH_IMAGE002
And striking
Figure 326247DEST_PATH_IMAGE001
. In addition, the other system is owned
Figure 647155DEST_PATH_IMAGE001
Will be all by my party
Figure 41227DEST_PATH_IMAGE002
Are regarded as targets and therefore, the targets are not designed separately
Figure 999956DEST_PATH_IMAGE001
. All kinds of
Figure 779562DEST_PATH_IMAGE001
The models have different capability attributes and thus can perform different functions. For different purposes
Figure 532755DEST_PATH_IMAGE001
The model is constructed by two methods: for each type of
Figure 414123DEST_PATH_IMAGE001
Specially designing a model; designing a general model, and filtering condition identification by setting attribute values
Figure 910963DEST_PATH_IMAGE002
Type (if it has investigation capability, it can be identified as investigation
Figure 810655DEST_PATH_IMAGE001
). According to practical experience, it is recommended to use the second method, on the one hand avoidingDifferent types of
Figure 469169DEST_PATH_IMAGE001
The repeated setting of the common attribute, on the other hand, the identification can be realized only by simple judgment conditions
Figure 41096DEST_PATH_IMAGE001
And thus implements the functionality of the first method. Thus, in
Figure 872786DEST_PATH_IMAGE002
In solid modeling, the invention firstly designs the general
Figure 892564DEST_PATH_IMAGE001
Models, then identified by design screening conditions
Figure 721979DEST_PATH_IMAGE001
Type (b).
Figure 577940DEST_PATH_IMAGE002
The design key of (1) is the setting of attributes, on one hand, the requirements of a scene to be simulated can be met, and on the other hand, the design is refined enough to avoid redundant and redundant design. Designing the general purpose of equipment nodes according to the characteristics of a combat system
Figure 416583DEST_PATH_IMAGE002
The model is shown in table 1:
TABLE 1
Figure 28217DEST_PATH_IMAGE003
Figure 28534DEST_PATH_IMAGE004
Lines 14-21 of Table 1 are
Figure 106212DEST_PATH_IMAGE005
Is the most important attribute in the subsequent modeling process. Further, the above
Figure 748546DEST_PATH_IMAGE005
Rather than being completely independent between attributes, the following lists the dependencies that exist between attributes:
Figure 477336DEST_PATH_IMAGE006
and
Figure 710871DEST_PATH_IMAGE007
at the same time, 0, or not 0 at the same time, i.e. satisfies:
Figure 275845DEST_PATH_IMAGE008
or
Figure 456290DEST_PATH_IMAGE009
。
Figure 790320DEST_PATH_IMAGE010
And
Figure 647286DEST_PATH_IMAGE011
none is 0 (communication is the basic capability of the equipment), i.e.:
Figure 433977DEST_PATH_IMAGE012
。
Figure 152534DEST_PATH_IMAGE013
and
Figure 606649DEST_PATH_IMAGE014
at the same time, 0, or at the same time, not 0,
Figure 634517DEST_PATH_IMAGE015
or
Figure 174083DEST_PATH_IMAGE016
。
Figure 696331DEST_PATH_IMAGE017
And
Figure 4952DEST_PATH_IMAGE018
at the same time, 0 or not at the same time, i.e., satisfy
Figure 751191DEST_PATH_IMAGE019
Or
Figure 962074DEST_PATH_IMAGE020
。
If it is
Figure 288013DEST_PATH_IMAGE021
Then, then
Figure 247879DEST_PATH_IMAGE022
。
If it is
Figure 368282DEST_PATH_IMAGE023
Then, then
Figure 131707DEST_PATH_IMAGE024
。
Figure 995758DEST_PATH_IMAGE025
And the number of the command channels is less than or equal to the number of the communication channels.
In satisfying the above
Figure 13393DEST_PATH_IMAGE026
On the basis of attribute constraint, the identification can be realized by setting rules
Figure 570276DEST_PATH_IMAGE026
Type (c) of the cell. Suppose an equipment
Figure 555419DEST_PATH_IMAGE026
Only corresponding to one equipment type, one equipment type is not considered at all
Figure 223160DEST_PATH_IMAGE026
There are cases where there are multiple equipment types. According to equipment
Figure 95301DEST_PATH_IMAGE026
The attribute judgment type identification rule is as follows:
for a certain
Figure 823086DEST_PATH_IMAGE027
：
It belongs to the investigation class
Figure 29945DEST_PATH_IMAGE028
And if and only if
Figure 235799DEST_PATH_IMAGE029
；
It belongs to the decision class
Figure 228025DEST_PATH_IMAGE028
And if and only if
Figure 126711DEST_PATH_IMAGE030
；
It belongs to the percussion category
Figure 823796DEST_PATH_IMAGE031
And if and only if
Figure 833341DEST_PATH_IMAGE032
。
The combat system accomplishes the strike to the target through the nimble cooperation between the equipment node, according to the combat ring theory, has 4 kinds of different even limit types in the combat system, and the difference is: the three types of nodes form the whole fighting capacity through four connected edges. Class 4 with edges
Figure 945653DEST_PATH_IMAGE031
The model is as follows:
(1) investigation edge
And (3) constraint: for any two subject models
Figure 749661DEST_PATH_IMAGE033
And
Figure 744162DEST_PATH_IMAGE034
exist of
Figure 541085DEST_PATH_IMAGE035
To
Figure 507904DEST_PATH_IMAGE027
If and only if:
Figure 686076DEST_PATH_IMAGE036
a type of investigation;
Figure 167873DEST_PATH_IMAGE037
(both parties do not belong to the same camp);
Figure 768487DEST_PATH_IMAGE038
(both sides distance is less than
Figure 589813DEST_PATH_IMAGE039
Investigation range of (1);
Figure 1203DEST_PATH_IMAGE040
（
Figure 907979DEST_PATH_IMAGE039
available).
The functions are as follows: the detection edge can store the detected target information into a target information list, and the two are used
Figure 46705DEST_PATH_IMAGE041
For example, if present, from
Figure 988116DEST_PATH_IMAGE042
To
Figure 304828DEST_PATH_IMAGE043
The detection edge of (1) is
Figure 433321DEST_PATH_IMAGE044
Figure 372808DEST_PATH_IMAGE045
Figure 168726DEST_PATH_IMAGE046
。
(2) Communication edge
And (3) constraint:
Figure 921919DEST_PATH_IMAGE047
（
Figure 803287DEST_PATH_IMAGE048
is a reconnaissance type rig);
Figure 283816DEST_PATH_IMAGE049
（
Figure 934240DEST_PATH_IMAGE042
is decision type equipment);
Figure 858333DEST_PATH_IMAGE050
(both parties belong to one formation);
Figure 226998DEST_PATH_IMAGE051
(both sides distance is less than
Figure 58688DEST_PATH_IMAGE043
Communication range of (2)；
Figure 812886DEST_PATH_IMAGE052
、
Figure 907881DEST_PATH_IMAGE053
。
The functions are as follows: the communication edge can share the target information to other
Figure 763841DEST_PATH_IMAGE054
Two of the above
Figure 336905DEST_PATH_IMAGE055
For example, if present, from
Figure 211189DEST_PATH_IMAGE056
To
Figure 477085DEST_PATH_IMAGE057
On the communication side of
Figure 554763DEST_PATH_IMAGE058
。
(3) Finger control edge
And (3) constraint: for any two agents
Figure 197097DEST_PATH_IMAGE033
And
Figure 928817DEST_PATH_IMAGE042
exist of
Figure 365615DEST_PATH_IMAGE043
To
Figure 665009DEST_PATH_IMAGE042
If and only if:
Figure 579875DEST_PATH_IMAGE059
（
Figure 428751DEST_PATH_IMAGE060
is decision type equipment);
Figure 36450DEST_PATH_IMAGE061
（
Figure 88720DEST_PATH_IMAGE034
is a percussion type equipment);
Figure 807277DEST_PATH_IMAGE062
(both parties belong to one formation);
Figure 510660DEST_PATH_IMAGE063
(since the command issuing relies on communication, the distance between the parties is less than
Figure 289260DEST_PATH_IMAGE043
Communication range of);
Figure 625563DEST_PATH_IMAGE064
、
Figure 351074DEST_PATH_IMAGE065
。
the functions are as follows: the control edge can process the target information and then distribute different target information to different attack nodes according to a certain rule, namely
Figure 456433DEST_PATH_IMAGE066
。
(4) Striking edge
And (3) constraint: for any two agents
Figure 655202DEST_PATH_IMAGE033
And
Figure 416485DEST_PATH_IMAGE034
existence ofBy
Figure 742424DEST_PATH_IMAGE043
To
Figure 905552DEST_PATH_IMAGE067
If and only if:
Figure 272293DEST_PATH_IMAGE068
（
Figure 786451DEST_PATH_IMAGE048
is a percussion type equipment);
Figure 650501DEST_PATH_IMAGE069
(both parties do not belong to the same camp);
Figure 668136DEST_PATH_IMAGE070
(both sides are at a distance less than
Figure 474287DEST_PATH_IMAGE043
Striking range of (d);
Figure 210162DEST_PATH_IMAGE071
。
the functions are as follows: the strike edge can strike the target according to the target information distributed by the command control node, whether the strike is successful or not is processed according to a certain probability rule, and if the strike is successful, the strike edge is deleted
Figure 612324DEST_PATH_IMAGE043
And all the connecting edges connected with the connecting edge.
Equipment behavior rules
Figure 484465DEST_PATH_IMAGE054
Model primary description equipment node
Figure 727097DEST_PATH_IMAGE054
How to take corresponding action in the face of environmental changes. Multiple corresponding in the battle system
Figure 419109DEST_PATH_IMAGE054
In the model, equipment
Figure 890542DEST_PATH_IMAGE054
The behavior of (1) is mainly reflected in two aspects, one is
Figure 882769DEST_PATH_IMAGE054
Another is a plurality of
Figure 765143DEST_PATH_IMAGE054
Change of network structure (new node introduction, edge reconnection).
The main characteristic of the battle system is that the equipment can be dynamically recombined to realize the overall optimal battle effect. One important embodiment of dynamic recombination is to reconnect the edges of the battle network according to the dynamic environment change, so that more high-quality battle rings are formed, and the whole battle network keeps the optimal performance. Ideally, each equipment could theoretically form a particular type of link with other equipment, but since the number of links forming any type is not infinite in practice, a decision needs to be made as to which equipment to close the loop when supply and demand is not met (i.e., the limit on the number of links is not sufficient to meet all the link requirements). The following closed-loop rule design is respectively carried out for the detection edge, the communication edge, the decision edge and the attack edge:
threat-first reconnaissance rule of continuous border:
and sequencing the threat degrees of the targets which can be detected by the detection equipment, and sequentially detecting the targets according to the sequencing. The threat can be defined as the distance of the target from the reconnaissance equipment, the distance is just inversely related to the reconnaissance side quality, the closer the distance, the higher the threat degree, and the higher the reconnaissance side quality at the moment. Of course, other calculation methods may be used for the threat level. Designing the edge connecting rule of the reconnaissance edge according to the principle of threat priority, as follows:
Figure 475610DEST_PATH_IMAGE072
for investigation
Figure 281892DEST_PATH_IMAGE027
According to its scope of investigation
Figure 128625DEST_PATH_IMAGE073
The inner target threat levels rank the targets. If the number of objects in the investigation range is less than
Figure 198212DEST_PATH_IMAGE074
Detecting all the targets, namely forming detection edges with all the targets; if the number of targets in the investigation range is larger than
Figure 382593DEST_PATH_IMAGE075
Then get the front of the rank of the target threat
Figure 195829DEST_PATH_IMAGE075
Individual target and investigation
Figure 162648DEST_PATH_IMAGE027
Constituting a scout edge.
Rule of communication side connection nearby:
the quality of the communication edge is in negative correlation with the distance between the communication equipment, so that the communication can be sequentially carried out with other equipment according to the sequencing of the distance, and the quality of the communication edge can be ensured to be larger. Accordingly, the rules for near communication connection are designed as follows:
Figure 137557DEST_PATH_IMAGE076
all of
Figure 71884DEST_PATH_IMAGE054
All have certain communication capabilityTo aim at
Figure 423231DEST_PATH_IMAGE043
To its communication range
Figure 244556DEST_PATH_IMAGE077
Inter-my equipment node distance
Figure 390367DEST_PATH_IMAGE043
The distances of (a) are sorted. If the number of the equipment nodes of my party in the communication range is less than
Figure 546410DEST_PATH_IMAGE078
Forming a communication edge with all the equipment nodes of our party; if the number of the equipment nodes of my party in the communication range is larger than
Figure 701448DEST_PATH_IMAGE079
Then take the distance
Figure 580543DEST_PATH_IMAGE043
Minimum front
Figure 959571DEST_PATH_IMAGE079
Individual my equipment nodes form a communication edge.
Weakest preferred strike alignment rule:
as long as the target is within the striking range, the probability of striking an edge is not related to the distance, but to the average radius of the target. In this context, it is considered that
Figure 602911DEST_PATH_IMAGE054
The attribute includes information related to a life value, and in order to destroy the target of the other party as soon as possible, the target with the lowest life value should be selected for the prior attack. Therefore, the weakest preferred strike alignment rule is designed as follows:
Figure 296061DEST_PATH_IMAGE080
the rule bagThe continuous edge rule design of the directing edge and the striking edge is included because the directing node potentially tells the striking node how the striking edge should be formed when the directing node commands the striking node to strike a certain target. To is directed at
Figure 91978DEST_PATH_IMAGE027
The information it grasps is as follows: 1) strike from my party
Figure 845171DEST_PATH_IMAGE054
All attribute information (mainly depending on the position, attack power, defense power, cost of one attack, etc.); 2) enemies in a target list
Figure 543235DEST_PATH_IMAGE055
Location, offensive power, defensive power, and value information. Based on the information, hitting our party by using a stable matching algorithm
Figure 40076DEST_PATH_IMAGE054
And enemies in the target list
Figure 690500DEST_PATH_IMAGE054
One-to-one or many-to-one matching (multiple hits by my party can be made)
Figure 349014DEST_PATH_IMAGE054
Attack an enemy target).
Equipment node
Figure 966946DEST_PATH_IMAGE081
Is the basis for the dynamic countermeasure implemented. In the battle system countermeasure, the equipment set is a system, and can also be regarded as a cluster, and the moving direction and speed of the equipment at each step in the system countermeasure can be determined by taking reference to a cluster motion model.
In nature, individual animals reach a certain number of groups, can be well self-organized to form forward, turning, avoiding … … and other matrix types, and can be seamlessly switched among different matrix types, as shown in fig. 1, a cluster motion model is used for simulating various impressive animal cluster motions in nature, wherein fig. 1 (a) is a schematic diagram of bird cluster motion, and fig. 1 (b) is a schematic diagram of fish cluster motion.
The bird swarm model is one of the cluster motions of a complex system theory study. By observation, the behavior mechanism of a bird group mainly comprises three types, as shown in fig. 2: FIG. 2 (a) shows the aggregation propensity: individuals tend to move closer to surrounding individuals to avoid isolation; FIG. 2 (b) shows velocity alignment, with the velocities of the individual and surrounding neighbors remaining synchronized; fig. 2 (c) shows density repulsion, and when the distance between individuals is too close, the individuals move toward each other to avoid collision.
Above-mentioned action mechanism can guarantee to avoid the collision between the individuality in the bird crowd on the one hand, and on the other hand can guarantee certain aggregative nature between the bird crowd to can reach the sharing of information between the individuality under the prerequisite of avoiding the collision (like the bird crowd is looking for the in-process of food, and an individual seeks food, can attract other individuality to look for food in the future).
Equipment for fighting against combat systems by using moving rules of animal clusters
Figure 1898DEST_PATH_IMAGE054
For reference purposes, e.g. for scouting
Figure 506829DEST_PATH_IMAGE054
The principle of finding the reconnaissance target is similar to that of foraging of the bird flock, and on one hand, the target of the other party is found more quickly by the bird flock, and on the other hand, individual reconnaissance is avoided
Figure 601824DEST_PATH_IMAGE055
Too much aggregation. By using bird group behavior mechanism for reference and combining the characteristics of fighting system, 3 equipment types are constructed
Figure 441473DEST_PATH_IMAGE054
The movement rules of (2) are as follows:
the detection Agent's "spring type" movement rule:
for equipment systems, the aim of reconnaissance is to find enemy targets as quickly, as early and as complete as possible, which requires that sufficient areas be reconnaissance. Thus, for investigation
Figure 280116DEST_PATH_IMAGE054
In other words, the moving principle is as follows: 1) need to be dispersed to reduce overlap of investigation ranges, and 2) need to be aggregated to some extent to avoid missing investigation regions. Thus, when investigating
Figure 905132DEST_PATH_IMAGE054
From other investigation
Figure 171028DEST_PATH_IMAGE054
When the distance is too small, the two mutually repel and attract each other, the process is similar to the force in different directions and different magnitudes generated by different stretching of the spring, and accordingly, the spring type investigation is defined
Figure 497974DEST_PATH_IMAGE054
And (4) moving rules. First, defining a reconnaissance based on bird group clustering and collision avoidance rejection mechanism
Figure 874728DEST_PATH_IMAGE054
The elastic model of
Figure 150989DEST_PATH_IMAGE082
Fangzhi investigation
Figure 587786DEST_PATH_IMAGE054
The elasticity calculation is illustrated for the example shown in fig. 3.
Wherein the content of the first and second substances,
Figure 77061DEST_PATH_IMAGE083
is that
Figure 257507DEST_PATH_IMAGE048
All others in the scope of the scout
Figure 857115DEST_PATH_IMAGE082
Square scout
Figure 714082DEST_PATH_IMAGE054
The center of gravity of the vehicle,
Figure 500772DEST_PATH_IMAGE084
is that
Figure 484909DEST_PATH_IMAGE027
The position of (a).
Firstly, according to fig. 3, the magnitude of the elastic force is calculated in three steps:
(1) finding objects
Figure 939024DEST_PATH_IMAGE085
All others within the scope of investigation of
Figure 514362DEST_PATH_IMAGE082
Square block
Figure 37616DEST_PATH_IMAGE086
Is recorded as a set
Figure 559864DEST_PATH_IMAGE087
。
(2) Computing collections
Figure 868485DEST_PATH_IMAGE082
All of
Figure 817987DEST_PATH_IMAGE086
Center of gravity of
Figure 94116DEST_PATH_IMAGE088
：
Figure 154476DEST_PATH_IMAGE089
Wherein the content of the first and second substances,
Figure 583183DEST_PATH_IMAGE090
is composed of
Figure 438007DEST_PATH_IMAGE085
The coordinates of (a);
(3) calculating the elasticity
Figure 198503DEST_PATH_IMAGE091
，
Figure 62554DEST_PATH_IMAGE092
Wherein the content of the first and second substances,
Figure 345767DEST_PATH_IMAGE084
is composed of
Figure 371492DEST_PATH_IMAGE093
Is determined by the coordinate of (a) in the space,
Figure 356635DEST_PATH_IMAGE094
is composed of
Figure 24376DEST_PATH_IMAGE084
To
Figure 162097DEST_PATH_IMAGE083
The vector of (a) is determined,
Figure 624302DEST_PATH_IMAGE095
as vectors
Figure 96741DEST_PATH_IMAGE094
The 2-norm of (a), the length,
Figure 568173DEST_PATH_IMAGE096
is composed of
Figure 357138DEST_PATH_IMAGE084
To
Figure 990244DEST_PATH_IMAGE083
The vector of (a) is normalized,
Figure 700711DEST_PATH_IMAGE097
define the length and forward and reverse directions of the spring: greater than 0 means that
Figure 959523DEST_PATH_IMAGE084
And
Figure 806256DEST_PATH_IMAGE083
is less than
Figure 875844DEST_PATH_IMAGE093
In the investigation range of
Figure 808028DEST_PATH_IMAGE098
Is a repulsive force, oriented
Figure 873460DEST_PATH_IMAGE083
Point of direction
Figure 574700DEST_PATH_IMAGE084
And is and
Figure 815188DEST_PATH_IMAGE097
the larger the value, the larger the repulsive force; less than 0 means that
Figure 969089DEST_PATH_IMAGE084
And
Figure 569704DEST_PATH_IMAGE083
is greater than
Figure 391029DEST_PATH_IMAGE099
The range of investigation of (a) is,
Figure 802419DEST_PATH_IMAGE098
represents an attractive force, the direction is
Figure 709195DEST_PATH_IMAGE084
Point of direction
Figure 113500DEST_PATH_IMAGE083
And is and
Figure 789332DEST_PATH_IMAGE097
the larger the value, the larger the attraction force.
Secondly, based on the magnitude of the elastic force, it can be calculated
Figure 371623DEST_PATH_IMAGE093
The direction and distance of movement of. Order to
Figure 765696DEST_PATH_IMAGE100
Is composed of
Figure 708113DEST_PATH_IMAGE099
Is obtained by obtaining
Figure 504030DEST_PATH_IMAGE101
The relationship between them is as follows:
Figure 257223DEST_PATH_IMAGE102
Figure 138591DEST_PATH_IMAGE103
thus, there are approximated:
Figure 85032DEST_PATH_IMAGE104
and
Figure 735456DEST_PATH_IMAGE105
i.e. by
Figure 659550DEST_PATH_IMAGE106
The position at the next time is the vector sum of the current position and the current velocity, and the velocity at the next time is the vector sum of the current velocity and the current elastic force.
Finger control
Figure 11902DEST_PATH_IMAGE086
And (3) closing communication uniform movement rules:
for the battle system, in order to guarantee investigation
Figure 46854DEST_PATH_IMAGE086
And control by finger
Figure 82944DEST_PATH_IMAGE086
The communication between the two devices needs to make each command control as possible
Figure 912359DEST_PATH_IMAGE086
With a certain number of investigations
Figure 752008DEST_PATH_IMAGE086
Remain within communication range. The communication range is equivalent to the visual range of the birds in the bird swarm model, and for birds outside the communication range
Figure 590651DEST_PATH_IMAGE086
And information such as the position and the speed cannot be acquired. In addition, for multiple commanders
Figure 215668DEST_PATH_IMAGE086
Communication redundancy may be caused if the aggregation level is too high, i.e. multiple fingers
Figure 481564DEST_PATH_IMAGE086
And the same investigation
Figure 808509DEST_PATH_IMAGE086
Communication is performed, thereby causing a waste of communication resources. Therefore, by using the aggregation tendency and density exclusion mechanism in the bird group behavior model for reference, the finger control is constructed
Figure 185264DEST_PATH_IMAGE086
The close communication uniform movement rule of (2), as shown in fig. 4.
Wherein the study object in the figure is
Figure 461524DEST_PATH_IMAGE085
，
Figure 898322DEST_PATH_IMAGE107
Is that
Figure 463295DEST_PATH_IMAGE085
All others within communication range
Figure 895938DEST_PATH_IMAGE108
Square scout
Figure 229968DEST_PATH_IMAGE086
The center of gravity of the vehicle,
Figure 837666DEST_PATH_IMAGE109
is that
Figure 624357DEST_PATH_IMAGE085
All other commanders within communication range
Figure 857761DEST_PATH_IMAGE086
The center of gravity of the vehicle,
Figure 46297DEST_PATH_IMAGE084
is that
Figure 824897DEST_PATH_IMAGE085
The position of (a).
Respectively calculate
Figure 364463DEST_PATH_IMAGE085
Attractive force of
Figure 135978DEST_PATH_IMAGE110
And repulsive force
Figure 444600DEST_PATH_IMAGE111
From which the resultant of the two forces is calculated as
Figure 190839DEST_PATH_IMAGE112
. Then, can be based on
Figure 217701DEST_PATH_IMAGE085
The current position, velocity and resultant calculate the velocity and position at its next time.
Striking
Figure 278061DEST_PATH_IMAGE086
Task point-oriented movement rules:
for the fighting system fighting task, the order is required
Figure 690457DEST_PATH_IMAGE113
According to investigation
Figure 76439DEST_PATH_IMAGE113
Derived information, and command
Figure 325017DEST_PATH_IMAGE113
And the issued command moves to the target and executes the striking task. At the same time, it is necessary to ensure that the signals do not exceed and control the control as much as possible
Figure 923489DEST_PATH_IMAGE113
The communication distance therebetween. Thus, striking
Figure 453041DEST_PATH_IMAGE113
The movement rules of (1) need to be both target-oriented movement and finger-oriented control
Figure 744345DEST_PATH_IMAGE113
Move as shown in fig. 5. Wherein the study object in the figure is
Figure 480219DEST_PATH_IMAGE099
，
Figure 413540DEST_PATH_IMAGE114
Is the position of the object or objects,
Figure 534949DEST_PATH_IMAGE115
is all covered in the communication range
Figure 997154DEST_PATH_IMAGE093
Direction A of
Figure 220325DEST_PATH_IMAGE086
The center of gravity of the vehicle,
Figure 426179DEST_PATH_IMAGE084
is that
Figure 667673DEST_PATH_IMAGE093
The position of (a).
Respectively calculate
Figure 566359DEST_PATH_IMAGE093
Respectively of two attractive forces
Figure 276826DEST_PATH_IMAGE116
Force of mixing
Figure 20791DEST_PATH_IMAGE117
From this, the resultant of the two forces is calculated as:
Figure 382371DEST_PATH_IMAGE118
then can be based on
Figure 186379DEST_PATH_IMAGE099
The current position, speed and resultant force are calculated to calculate the speed and position at the next moment,
Figure 180880DEST_PATH_IMAGE114
is the location of the target.
The combat system is a typical open system, and both sides can add new equipment nodes to the system according to actual conditions so as to achieve the purpose of winning. If the introduction of a new equipment node is not arranged, the whole combat system is closed, and finally an equilibrium state is reached. Suppose there are two parties
Figure 728536DEST_PATH_IMAGE082
And
Figure 695355DEST_PATH_IMAGE119
through the mutual influence between the two parties, the final equilibrium state has three types: only remain in the system
Figure 188041DEST_PATH_IMAGE082
Left in the system
Figure 607521DEST_PATH_IMAGE119
A small amount of the catalyst remains in the system
Figure 958868DEST_PATH_IMAGE120
And
Figure 780193DEST_PATH_IMAGE119
and the two do not interfere with each other, as shown in fig. 6.
However, in actual combat, node introduction is not unlimited due to resource limitations (otherwise, one party introduces an unlimited number of own nodes instantaneously), and therefore, a certain node introduction mechanism needs to be set.
Firstly, a base is set
Figure 440850DEST_PATH_IMAGE121
And equipment
Figure 82047DEST_PATH_IMAGE122
In contrast to this, the present invention is,
Figure 971506DEST_PATH_IMAGE123
having an individual set of attributes, respectively
Figure 912917DEST_PATH_IMAGE124
Respectively representing the existing resource amount, the unit time resource recovery amount and adding a reconnaissance
Figure 213317DEST_PATH_IMAGE122
Resource consumption, adding a command
Figure 607390DEST_PATH_IMAGE125
Resource consumption and a new hit
Figure 566118DEST_PATH_IMAGE122
The resource consumption of. Then, 3 node introduction mechanisms were designed as follows.
Introducing a new equipment node rule in a saturation mode:
according to the new increase
Figure 345724DEST_PATH_IMAGE122
And make a decision
Figure 98917DEST_PATH_IMAGE122
Striking
Figure 980285DEST_PATH_IMAGE122
Once the current amount of resources reaches the order of (2) generating the corresponding
Figure 477125DEST_PATH_IMAGE122
The amount of resources required, i.e. to generate one such
Figure 397325DEST_PATH_IMAGE122
The specific rule is as follows:
Figure 55839DEST_PATH_IMAGE126
introducing a new equipment node rule as required:
in-system investigation
Figure 424504DEST_PATH_IMAGE122
And make a decision
Figure 459456DEST_PATH_IMAGE122
And striking
Figure 479233DEST_PATH_IMAGE122
When the existing quantity does not meet the demand quantity, the corresponding quantity is generated
Figure 43070DEST_PATH_IMAGE122
The specific rule is as follows:
Figure 164610DEST_PATH_IMAGE127
regular introduction of new equipment node rules
Introducing 3 types of nodes at intervals of fixed time, or sequentially introducing investigation at intervals of fixed time according to sequence
Figure 737674DEST_PATH_IMAGE122
And make a decision
Figure 346378DEST_PATH_IMAGE122
And striking
Figure 612275DEST_PATH_IMAGE122
In this loop, in the following manner of introducing 3 kinds of nodes simultaneously, the rule for introducing new equipment nodes at regular time is designed as follows:
Figure 689952DEST_PATH_IMAGE128
based on the method, the invention also provides a multi-agent-based autonomous USoS participation model construction system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of any method when executing the computer program.
The invention has the following beneficial effects:
1. the multi-Agent-based autonomous USoS participation model construction method provided by the invention does not require a modeler to establish a detailed mathematical model for a collaborative process, and can describe interaction behaviors such as cooperation, countermeasure and the like among the agents only by paying attention to behavior modeling of a system.
2. According to the multi-Agent-based autonomous USoS participation model construction method, the constructed entity Agent model and the behavior rules can support and construct a dynamic simulation model of an equipment system.
3. The method for constructing the autonomous USoS participation model based on the multi-Agent realizes the USOS engagement model through the multi-Agent programmable modeling simulation environment NetLoco. The dynamic course of the engagement can be visually demonstrated and the validity of the proposed model can be verified.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a cluster motion phenomenon provided by the present invention;
FIG. 2 is a schematic diagram of a bird group behavior mechanism provided by the present invention;
FIG. 3 is a diagram of Agent movement rules provided by the present invention;
FIG. 4 is a schematic diagram of a movement rule of an Agent according to the present invention;
FIG. 5 is a schematic diagram of the movement rules of the striking Agent according to the present invention;
FIG. 6 is a schematic diagram of 3 equilibrium states of the closed combat system provided by the present invention;
FIG. 7 is a framework diagram of a combat system collaborative countermeasure simulation system according to a preferred embodiment of the present invention;
FIG. 8 is a visualization interface of a combat system collaborative confrontation simulation model according to a preferred embodiment of the present invention;
FIG. 9 is a schematic diagram of the countermeasure process of the unmanned combat system according to the different steps provided in the preferred embodiment of the present invention;
FIG. 10 is a schematic diagram of the 1-time combat fight process of the unmanned combat system according to the preferred embodiment of the present invention;
FIG. 11 is a schematic diagram of the 10 combat systems of the preferred embodiment of the present invention;
FIG. 12 is a schematic diagram comparing Agent capability provided by the preferred embodiment of the present invention;
FIG. 13 is a flow chart of a multi-agent based autonomous USoS participation model construction method provided by the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
The embodiment exemplifies the equipment node and the equipment edge connecting entity constructed according to the research
Figure 332286DEST_PATH_IMAGE129
Models and associated behavioral rules, relying on
Figure 61076DEST_PATH_IMAGE130
The simulation platform constructs
Figure 497874DEST_PATH_IMAGE129
The battle system of (2) confronts the simulation model, and the design idea and the whole framework of the model are shown in FIG. 7.
The example of the present embodiment can be fully reproduced, and the operation method is as follows: mounting of
Figure 62847DEST_PATH_IMAGE130
Tools (open source software); downloads the program and executes the code. Because the simulation has a certain randomness, each run does not necessarily produce the same dynamic process and result.
The framework shown in FIG. 7 has 4 layers from the lowest logical layer to the highest visual layer, wherein the rule layer and the data layer are used
Figure 243293DEST_PATH_IMAGE130
The language is realized by code programming, and the visual layer is
Figure 95099DEST_PATH_IMAGE130
The logic layer is not editable and invisible. According to the framework, corresponding equipment is constructed for each type
Figure 702798DEST_PATH_IMAGE131
Models, behavioral rules models, and visualization models.
The embodiment example mainly aims at research on autonomous confrontation of unmanned equipment system, and equipment is arranged in the process of autonomous confrontation
Figure 489488DEST_PATH_IMAGE131
And forming the battle ring connecting edges between the equipment according to the constructed behavior rule model so as to form the battle ring. And (4) taking the introduction mechanism of the new equipment node into consideration, carrying out sensitivity analysis of different capabilities on the influence of the countermeasure result.
Firstly, according to the constructed simulation model, comparing the process and result data of the cooperative countermeasure of the combat system under different conditions.
In the visualization module, the visualization module is used,
Figure 208045DEST_PATH_IMAGE132
the color of (c) indicates its formation (the red party is my party). The pentagons of the lower left corner and the upper right corner are bases
Figure 177007DEST_PATH_IMAGE132
，“
Figure 955608DEST_PATH_IMAGE133
”、“
Figure 495173DEST_PATH_IMAGE134
"and"
Figure 469951DEST_PATH_IMAGE135
"represents the types of percussion, command and reconnaissance bases, tank, plane and pentagon respectively representing percussion, reconnaissance and command
Figure 575311DEST_PATH_IMAGE132
Type, red, brown, blue and black bordered represent the scout, communication, command and strike types, respectively.
Suppose that the unmanned combat system is divided into two parts of red and blue, each of which has 3 bases
Figure 524812DEST_PATH_IMAGE129
Respectively, is a detection base
Figure 489357DEST_PATH_IMAGE132
Finger control base
Figure 64564DEST_PATH_IMAGE132
And striking base
Figure 227692DEST_PATH_IMAGE129
Can consume certain resources to generate corresponding types
Figure 348095DEST_PATH_IMAGE132
. Each base station
Figure 862253DEST_PATH_IMAGE132
Certain variables need to be set as shown in table 2. Generated by bases
Figure 972641DEST_PATH_IMAGE132
Inherit the attributes of the base, but generated
Figure 990276DEST_PATH_IMAGE129
The mobility attribute needs to be changed from 0 to 1. The new node generation rule of the present embodiment introduces a new device node rule with reference to a saturation formula.
TABLE 2 base information
Figure 547159DEST_PATH_IMAGE136
First, a visual interface of the combat system versus the simulation model is designed, as shown in fig. 8.
FIG. 8 (a) is a visual interface for visually displaying the dynamic changes of the tactical system; fig. 8 (b) and 8 (c) are partial data records of the history and current state of the operation of the combat system, respectively.
The end condition of the confrontation simulation model of the unmanned combat system is set as: when the combat system has only the equipment of the same battle
Figure 283034DEST_PATH_IMAGE137
Or when the simulation step reaches the maximum step of the constraint, the simulation is stopped, and the maximum step is set to 2000 in this embodiment.
Of both parties
Figure 200043DEST_PATH_IMAGE138
The initial capabilities were set according to table 2. The fighting process of the unmanned combat system is shown in the figure. The whole countermeasure process comprises 427 steps, and 1 time is taken for each 40 steps of the countermeasure process from step 0 to step 160. Due to space limitation, the final sub-graph gives the final state, and only the blue equipment is arranged in the battle system
Figure 72184DEST_PATH_IMAGE139
And the result shows that the blue matrix wins.
The unmanned system combat process embodies cooperation among the same pieces of equipment for formation and confrontation among different pieces of equipment for formation. The cooperation is embodied at the communication side and the instruction control side, the communication side transmits the detected target information among the equipments in the same formation, and the instruction control side sends the task to the striking side
Figure 65548DEST_PATH_IMAGE140
. Antibodies are now reconnaissance (acquiring target information) and hit (hitting target). The corresponding challenge results of the above challenge process are shown in fig. 10.
FIG. 10 (a) records the current remaining equipment of both parties at each step
Figure 288719DEST_PATH_IMAGE141
Quantity, (b) equipment recording loss of both red and blue until current step
Figure 478261DEST_PATH_IMAGE141
The number of the cells. The results show that the opponent's battle wins, consistent with the confrontation process of fig. 9. Wherein, FIG. 9 (a) -FIG. 9 (f) are step 0 and step 0, respectively40. The confrontation results in steps 80, 120, 160 and 427 are shown schematically. Since my party is investigating
Figure 470488DEST_PATH_IMAGE141
The detection range of the detector is only 10km, and the other party detects
Figure 369173DEST_PATH_IMAGE142
The reconnaissance range of 100km results in that the number of equipment of our party is always at a disadvantage and the equipment of our party
Figure 814061DEST_PATH_IMAGE142
Is always on the rising trend.
In addition, by collecting data of 10 times of simulation, equipment was plotted
Figure 72873DEST_PATH_IMAGE143
The trace heatmap of (a), is shown in fig. 11.
Equipment
Figure 185186DEST_PATH_IMAGE144
A trace of color is left when passing a location, and the colors can be superimposed. Thus, the more
Figure 989194DEST_PATH_IMAGE144
The heavier the color is at the position passed. Of both parties
Figure 186957DEST_PATH_IMAGE143
Meeting in the middle of the battlefield is also the most aggressive area of confrontation.
Through many experiments, under different capability settings, three equilibrium states are found in the embodiment. The first is a state where only the red system agent is alive. The second is a state where only the blue system agent is alive. Third, both red and blue system proxies exist, with no interaction between them. The first two equilibrium states are understandable and occur when all system agents of a single campaign are destroyed. For the third equalization, it may result from two cases: (1) the base of a camp is totally destroyed, resulting in no new system agents being added to the battlefield. (2) The number of the rest special workers and bases is less than three, and an effective hunting chain of observer- > judge- > executor cannot be formed.
Next, this example designs 6 capability schemes, as shown in table 3. Two-by-two of the 6 capacity schemes were challenged 20 times, and in order to eliminate random errors that may occur due to different camps, the challenge between any two schemes would be "red-to-blue" 10 times and "blue-to-red" 10 times, respectively.
TABLE 3 Capacity schemes
Figure 986810DEST_PATH_IMAGE145
Figure 953629DEST_PATH_IMAGE146
Make the red and blue left after the countermeasure simulation ends equip
Figure 928538DEST_PATH_IMAGE122
Number difference between
Figure 613597DEST_PATH_IMAGE147
Is an index of performance efficiency, wherein
Figure 214212DEST_PATH_IMAGE148
The winning of the direction of the red is shown,
Figure 35537DEST_PATH_IMAGE149
the indication of the win-win of the blue,
Figure 446927DEST_PATH_IMAGE150
indicating a flat hand. The statistical results are shown in table 4.
TABLE 4 statistics of results of systematic challenge under different capacity scenarios
Figure 353703DEST_PATH_IMAGE151
Then, the average of the system challenge results under different capacity schemes was calculated as shown in table 5.
TABLE 5
Figure 492429DEST_PATH_IMAGE152
Average value of (2)
Figure 433841DEST_PATH_IMAGE153
Then, 6 capacity scenarios were evaluated overall. Taking case of scenario 1, its evaluation score is the average of the results of the scenario compared to the other scenarios, i.e., [12.4, 0.4, 18.1, 18.1, 9.4, 8.4, -1.9, 16, 16.3, 9.9], thus the scenario 1 score is 5.355. From this, the total evaluation values of the 6 capability schemes are calculated as: 5.355, 0.815, 5, -4.965, -6.4 and 0.195. Thus, scheme 1> scheme 3> scheme 2> scheme 6> scheme 4> scheme 5.
When the fighting capabilities of the two parties in the fighting system are not very different, the result can be changed by only slightly improving some capabilities of the parties. Therefore, the critical point of the acquisition capability is important. If one breaks this critical point, the countermeasures can be reversed. This critical point is commonly referred to as a discontinuity point, according to the system theory, and is used to describe the discontinuity and abruptness of the change. Next, the scouting will be done in red
Figure 750553DEST_PATH_IMAGE154
For example, mutation points under the two-party current capability scheme shown in Table 6 were analyzed.
TABLE 6
Figure 144625DEST_PATH_IMAGE155
In table 6, the different capability indexes between the two capability schemes are mutually good and bad, and are relatively balanced, and the bold shows that the capability of the array where the column is located is higher than that of the other arrayThe capacity of the tank, the result of the fight depending on the amount of the fight remaining when the fight reaches the end condition
Figure 352621DEST_PATH_IMAGE154
The number of the cells. Make our party reconnaissance
Figure 882960DEST_PATH_IMAGE154
Has a scouting ability of
Figure 636152DEST_PATH_IMAGE156
，
Figure 517520DEST_PATH_IMAGE157
. To "remain
Figure 995119DEST_PATH_IMAGE154
Loss of quantity "and
Figure 645544DEST_PATH_IMAGE154
quantity in response
Figure 569637DEST_PATH_IMAGE156
Statistical indicators of changes. Each confrontation simulation will
Figure 938302DEST_PATH_IMAGE156
The value of (c) is increased by 1 and each simulation is repeated 10 times to eliminate the random effect, and the statistical result is shown in fig. 12.
The dark-colored curve and the dark-colored region of the graph (a) in FIG. 12 represent respectively the remaining equipment in our party's camp after the completion of the confrontation
Figure 222521DEST_PATH_IMAGE154
The mean and 95% confidence intervals of the quantities, respectively, the light-colored lines and areas represent the remaining equipment of the basket formation
Figure 727452DEST_PATH_IMAGE154
Mean value of the amount and 95% confidence interval. As can be inferred from FIG. 12 (a), when my party reconnaissance
Figure 822447DEST_PATH_IMAGE154
Ability to reconnaissance
Figure 678407DEST_PATH_IMAGE156
Less than 50, the equilibrium state of the confrontation is I, when
Figure 500739DEST_PATH_IMAGE156
Above 70, the equilibrium state of the confrontation is my winning. Therefore, the temperature of the molten metal is controlled,
Figure 125755DEST_PATH_IMAGE156
has a mutation point of [50,70 ]]Within the interval.
For more detailed analysis, interval [50,70 ] is used]The area of (c) is enlarged as shown in (b). (b) The figure is a close-up of the cross-over area of figure (a), showing only the remaining equipment
Figure 391652DEST_PATH_IMAGE154
Mean curve of quantities. As can be seen from the graph (b), the depth curve has two intersections, and the 2 nd intersection is regarded as an abnormal value. From the 1 st cross point
Figure 469329DEST_PATH_IMAGE156
In the interval [63,64 ]]In between, a median value of 63.5 in the interval can be assigned as a mutation point.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. The multi-agent-based autonomous USoS participation model construction method is characterized by comprising the following steps:
constructing a master model that is generic
Figure 820009DEST_PATH_IMAGE001
Models, including investigation
Figure 657384DEST_PATH_IMAGE001
And make a decision
Figure 315899DEST_PATH_IMAGE002
And striking
Figure 746880DEST_PATH_IMAGE001
(ii) a The method for constructing the main body model comprises the following steps:
identifying a subject model type by setting an attribute value filter condition, the attribute value including a reconnaissance range
Figure 968783DEST_PATH_IMAGE003
Scout channel
Figure 536030DEST_PATH_IMAGE004
Communication range
Figure 568709DEST_PATH_IMAGE005
Communication channel
Figure 486986DEST_PATH_IMAGE006
Capability of controlling by finger
Figure 981421DEST_PATH_IMAGE007
And a finger control channel
Figure 934334DEST_PATH_IMAGE008
Range of percussion
Figure 872334DEST_PATH_IMAGE009
Striking channel
Figure 12328DEST_PATH_IMAGE010
Maximum moving speed
Figure 838683DEST_PATH_IMAGE011
Mobility of the mobile terminal
Figure 380523DEST_PATH_IMAGE012
Residual oil amount
Figure 755004DEST_PATH_IMAGE013
Availability, availability
Figure 647873DEST_PATH_IMAGE014
；
If and only if
Figure 484111DEST_PATH_IMAGE015
、
Figure 146037DEST_PATH_IMAGE016
、
Figure 691419DEST_PATH_IMAGE017
The time subject model belongs to a detection class;
if and only if
Figure 806005DEST_PATH_IMAGE018
、
Figure 711513DEST_PATH_IMAGE019
、
Figure 227945DEST_PATH_IMAGE020
The temporal principal model belongs to a decision class;
if and only if
Figure 944228DEST_PATH_IMAGE018
、
Figure 546111DEST_PATH_IMAGE021
、
Figure 255310DEST_PATH_IMAGE022
The time main body model belongs to the strike class; establishing an exchange relationship between the main body model of the system and the system;
and introducing behavior rules to constrain the interaction relation to form an autonomous USoS participation model based on multiple agents.
2. The multi-agent based autonomous USoS participation model building method according to claim 1, wherein the following constraints are satisfied between the subject models:
Figure 626248DEST_PATH_IMAGE023
and
Figure 513433DEST_PATH_IMAGE024
at the same time, 0, or not 0 at the same time, i.e. satisfies:
Figure 337032DEST_PATH_IMAGE025
or
Figure 852852DEST_PATH_IMAGE026
；
Figure 78297DEST_PATH_IMAGE027
And
Figure 136383DEST_PATH_IMAGE028
all are not 0, namely:
Figure 712858DEST_PATH_IMAGE029
；
Figure 763859DEST_PATH_IMAGE030
and
Figure 843810DEST_PATH_IMAGE031
at the same time, 0, or not 0 at the same time, that is,:
Figure 338377DEST_PATH_IMAGE032
or
Figure 57940DEST_PATH_IMAGE033
；
Figure 725682DEST_PATH_IMAGE034
And
Figure 925719DEST_PATH_IMAGE035
at the same time, 0, or not 0 at the same time, that is,:
Figure 591187DEST_PATH_IMAGE036
or
Figure 611095DEST_PATH_IMAGE037
；
If it is
Figure 269479DEST_PATH_IMAGE038
Then, then
Figure 324022DEST_PATH_IMAGE039
；
If it is
Figure 894812DEST_PATH_IMAGE040
Then, then
Figure 667596DEST_PATH_IMAGE041
；
Figure 657899DEST_PATH_IMAGE042
And the number of the command channels is less than or equal to the number of the communication channels.
3. The multi-agent based autonomous USoS participation model building method as claimed in claim 1, wherein the exchange relationship between the subject model and the system comprises 4 types of connection edge: investigation, communication, command and strike sides:
detecting edges: for any two subject models
Figure 442315DEST_PATH_IMAGE043
And
Figure 574219DEST_PATH_IMAGE044
exist of
Figure 693354DEST_PATH_IMAGE044
To
Figure 568906DEST_PATH_IMAGE045
If and only if:
Figure 473408DEST_PATH_IMAGE046
a type of investigation;
Figure 510634DEST_PATH_IMAGE047
；
Figure 382644DEST_PATH_IMAGE048
；
Figure 796308DEST_PATH_IMAGE049
；
communication side: for any two agents
Figure 555317DEST_PATH_IMAGE043
And
Figure 497865DEST_PATH_IMAGE044
exist of
Figure 591592DEST_PATH_IMAGE044
To
Figure 74526DEST_PATH_IMAGE045
If and only if:
Figure 688041DEST_PATH_IMAGE050
；
Figure 332649DEST_PATH_IMAGE051
；
Figure 651022DEST_PATH_IMAGE052
；
Figure 406488DEST_PATH_IMAGE053
；
Figure 140089DEST_PATH_IMAGE054
、
Figure 955598DEST_PATH_IMAGE055
；
controlling edges: for any two agents
Figure 758338DEST_PATH_IMAGE043
And
Figure 317496DEST_PATH_IMAGE056
exist of
Figure 905603DEST_PATH_IMAGE056
To
Figure 892013DEST_PATH_IMAGE045
If and only if
Figure 447629DEST_PATH_IMAGE057
：
Figure 544898DEST_PATH_IMAGE058
；
Figure 987511DEST_PATH_IMAGE059
；
Figure 144823DEST_PATH_IMAGE060
；
Figure 922155DEST_PATH_IMAGE061
；
Figure 823115DEST_PATH_IMAGE055
、；
Beating edges: for any two agents
Figure 385815DEST_PATH_IMAGE043
And
Figure 714028DEST_PATH_IMAGE056
exist of
Figure 311664DEST_PATH_IMAGE056
To
Figure 750735DEST_PATH_IMAGE045
If and only if:
Figure 167941DEST_PATH_IMAGE062
；
Figure 932635DEST_PATH_IMAGE063
；
Figure 684559DEST_PATH_IMAGE064
；
Figure 927322DEST_PATH_IMAGE065
；
wherein the content of the first and second substances,
Figure 199034DEST_PATH_IMAGE066
is a type of the subject model, and is,
Figure 134629DEST_PATH_IMAGE067
is used for the formation of the main model,
Figure 108270DEST_PATH_IMAGE068
is the distance between the nodes and is,
Figure 154724DEST_PATH_IMAGE069
in order to determine the scope of investigation of the node,
Figure 546522DEST_PATH_IMAGE070
in order for a node to execute a state,
Figure 387439DEST_PATH_IMAGE071
is the communication range of the node or nodes,
Figure 848376DEST_PATH_IMAGE072
is the hit range of the node.
4. The multi-agent based autonomous USoS participation model building method of claim 1, wherein the behavior rules comprise battle closed-loop rules, movement rules and body model introduction rules.
5. The multi-agent based autonomous USoS participation model building method of claim 4, wherein the closed-loop rules comprise a threat-first reconnaissance edge rule, a near communication edge rule, and a weakest-first strike edge rule;
investigation edge connecting gaugeThen: for investigation class
Figure 432941DEST_PATH_IMAGE073
According to its scope of investigation
Figure 679246DEST_PATH_IMAGE074
The threat degree of the inner target orders the targets, if the number of the targets in the detection range is less than
Figure 691064DEST_PATH_IMAGE075
Detecting all the targets, namely forming detection edges with all the targets; if the number of targets in the investigation range is larger than
Figure 907806DEST_PATH_IMAGE076
Then get the front of the rank of the target threat
Figure 30483DEST_PATH_IMAGE075
Individual target and investigation
Figure 396874DEST_PATH_IMAGE077
Form a scout edge, wherein
Figure 579593DEST_PATH_IMAGE078
The number of target nodes can be simultaneously detected for the detection channels of the nodes;
rule of communication side connection nearby: all of
Figure 15123DEST_PATH_IMAGE079
All have certain communication capacity aiming at
Figure 941490DEST_PATH_IMAGE080
To its communication range
Figure 162387DEST_PATH_IMAGE081
Inner distance of my square body model
Figure 516008DEST_PATH_IMAGE080
If the number of the principal models of our party in the communication range is less than
Figure 438834DEST_PATH_IMAGE082
Then, forming a communication edge with all the principal models of our party; if the number of the principal models of our party in the communication range is larger than
Figure 168892DEST_PATH_IMAGE081
Then take the distance
Figure 244296DEST_PATH_IMAGE083
Minimum front
Figure 768818DEST_PATH_IMAGE082
The main body models of the individual parties form a communication edge;
weakest preferred strike alignment rule: class of current decision
Figure 178940DEST_PATH_IMAGE084
Command strikes
Figure 712689DEST_PATH_IMAGE084
Attacking a certain target and simultaneously attacking classes based on the own party
Figure 642599DEST_PATH_IMAGE085
All attribute information in the object list
Figure 338023DEST_PATH_IMAGE085
Position, attack power, defense power and value information, and striking the class of our party by using a stable matching algorithm
Figure 232511DEST_PATH_IMAGE085
And enemies in the target list
Figure 304372DEST_PATH_IMAGE086
Performing one-to-one or many-to-one matching to make the striking class
Figure 88788DEST_PATH_IMAGE086
A striking edge is formed between the two.
6. The method as claimed in claim 4, wherein the movement rules include "spring-loaded" movement rules, finger control
Figure 220692DEST_PATH_IMAGE079
Close communication uniform movement rules, attacks and the like
Figure 339827DEST_PATH_IMAGE079
A task point movement-oriented rule;
the "spring type" movement rule: defining investigation classes
Figure 215379DEST_PATH_IMAGE079
The elastic force model of (2): c is to
Figure 854302DEST_PATH_IMAGE087
All other scouts within the scout range
Figure 157107DEST_PATH_IMAGE079
The center of gravity of the vehicle,
Figure 763538DEST_PATH_IMAGE088
is that
Figure 177202DEST_PATH_IMAGE087
The position of (a); finding
Figure 936210DEST_PATH_IMAGE089
All others within the scope of investigation of
Figure 409917DEST_PATH_IMAGE079
Is recorded as a set
Figure 503644DEST_PATH_IMAGE090
(ii) a Computing collections
Figure 720999DEST_PATH_IMAGE091
All of
Figure 334514DEST_PATH_IMAGE079
Center of gravity of
Figure 979122DEST_PATH_IMAGE092
，
Figure 297495DEST_PATH_IMAGE093
Wherein the content of the first and second substances,
Figure 318541DEST_PATH_IMAGE094
is composed of
Figure 52141DEST_PATH_IMAGE073
The coordinates of (a);
calculating the elasticity
Figure 602071DEST_PATH_IMAGE095
，
Wherein the content of the first and second substances,
Figure 670390DEST_PATH_IMAGE096
is composed of
Figure 229548DEST_PATH_IMAGE089
Is determined by the coordinate of (a) in the space,
Figure 817655DEST_PATH_IMAGE097
is composed of
Figure 538486DEST_PATH_IMAGE096
The vector of the C is obtained by the method,
Figure 94101DEST_PATH_IMAGE098
as vectors
Figure 191370DEST_PATH_IMAGE097
The 2-norm of (a), the length,
Figure 899563DEST_PATH_IMAGE099
is composed of
Figure 56875DEST_PATH_IMAGE096
The vector to C is normalized,
Figure 834207DEST_PATH_IMAGE100
define the length and forward and reverse directions of the spring: greater than 0 means that
Figure 735167DEST_PATH_IMAGE096
And the distance between C is less than
Figure 297867DEST_PATH_IMAGE089
In the investigation range of
Figure 626080DEST_PATH_IMAGE101
Belonging to repulsive force, the direction is directed from C
Figure 887778DEST_PATH_IMAGE096
And is and
Figure 733375DEST_PATH_IMAGE100
the larger the value, the larger the repulsive force; less than 0 means that
Figure 275214DEST_PATH_IMAGE096
And the distance between C is larger than
Figure 898963DEST_PATH_IMAGE077
The range of investigation of (a) is,
Figure 526253DEST_PATH_IMAGE101
represents an attractive force, the direction is
Figure 644382DEST_PATH_IMAGE096
Is directed to C, an
Figure 40728DEST_PATH_IMAGE100
The larger the value, the larger the attraction force;
secondly, based on the magnitude of the elastic force
Figure 835378DEST_PATH_IMAGE083
The direction and distance of movement of; order to
Figure 949964DEST_PATH_IMAGE102
Is composed of
Figure 871784DEST_PATH_IMAGE080
Is obtained by obtaining
Figure 122636DEST_PATH_IMAGE103
The relationship between them is as follows:
Figure 88187DEST_PATH_IMAGE104
Figure 690070DEST_PATH_IMAGE105
wherein the content of the first and second substances,
Figure 150001DEST_PATH_IMAGE106
is a unit mass, and therefore, is approximated by:
Figure 724202DEST_PATH_IMAGE107
and
Figure 736020DEST_PATH_IMAGE108
i.e. by
Figure 952763DEST_PATH_IMAGE087
The position at the next moment is the vector sum of the current position and the current speed, and the speed at the next moment is the vector sum of the current speed and the current elasticity;
finger control
Figure 747543DEST_PATH_IMAGE085
And (3) closing communication uniform movement rules: respectively calculate
Figure 176251DEST_PATH_IMAGE087
Attractive force of
Figure 624550DEST_PATH_IMAGE109
And repulsive force
Figure 60079DEST_PATH_IMAGE110
From which the resultant of the two forces is calculated as
Figure 986447DEST_PATH_IMAGE111
(ii) a Then, according to
Figure 207344DEST_PATH_IMAGE087
Calculating the speed and the position of the current position, the speed and the resultant force at the next moment;
percussion type
Figure 560964DEST_PATH_IMAGE079
Task point-oriented movement rules: respectively calculate
Figure 483790DEST_PATH_IMAGE087
Respectively of two attractive forces
Figure 213849DEST_PATH_IMAGE112
Force of mixing
Figure 289252DEST_PATH_IMAGE113
From this, the resultant of the two forces is calculated as:
Figure 813774DEST_PATH_IMAGE114
then according to
Figure 223896DEST_PATH_IMAGE087
The current position, velocity and resultant force calculate the velocity and position at the next time,
Figure 898591DEST_PATH_IMAGE115
is the location of the target.
7. The multi-agent based autonomous USoS participation model building method of claim 4, wherein the subject model introduction rules comprise a saturation type new equipment node introduction rule, a new equipment node introduction rule according to requirements, and a new equipment node introduction rule at regular time;
introducing a new main body model rule by a saturation formula: according to newly added investigation classes
Figure 871576DEST_PATH_IMAGE079
Decision making class
Figure 832579DEST_PATH_IMAGE079
And strikes
Figure 480729DEST_PATH_IMAGE116
Once the current amount of resources reaches the order of (2) generating the corresponding
Figure 287011DEST_PATH_IMAGE079
The amount of resources required for the system,namely to generate one of the
Figure 586274DEST_PATH_IMAGE079
；
Introducing new subject model rules as required: class of investigation in the system
Figure 452599DEST_PATH_IMAGE079
Decision making class
Figure 588045DEST_PATH_IMAGE079
And strikes
Figure 198018DEST_PATH_IMAGE116
When the existing quantity does not meet the demand quantity, the corresponding quantity is generated
Figure 351788DEST_PATH_IMAGE079
；
And (3) regularly introducing a new main body model rule: introducing 3 types of nodes at intervals of a fixed time period; or sequentially introducing detection classes at regular time intervals according to the sequence
Figure 654593DEST_PATH_IMAGE079
Decision making class
Figure 11756DEST_PATH_IMAGE079
And strikes
Figure 425420DEST_PATH_IMAGE079
。
8. A multi-agent based autonomous USoS participation model building system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method of any of the preceding claims 1 to 7.
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