CN116384436A - Unmanned aerial vehicle 'bee colony' countermeasure method - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention discloses a method for fighting a 'bee colony' of an unmanned aerial vehicle, which comprises the following steps: step S1, acquiring three-dimensional space position distribution information of an enemy unmanned aerial vehicle bee colony according to a radar, acquiring information of the enemy unmanned aerial vehicle bee colony based on a clustering algorithm, and obtaining an augmented cluster ordering of the enemy unmanned aerial vehicle bee colony; s2, acquiring a clustering result of the enemy unmanned aerial vehicle bee colony through an OPTICS clustering algorithm according to the acquired information and the amplified cluster sequencing parameters; step S3, obtaining an optimized clustering result according to the relationship data between the enemy unmanned aerial vehicle bee colony and the target to be protected; and S4, optimizing the countermeasure allocation according to the optimized clustering result and the priority level. According to the method for the 'swarm' countermeasure of the unmanned aerial vehicle, provided by the invention, the acquisition of a target clustering result can be realized aiming at the swarm of the three-dimensional space density cluster of the unmanned aerial vehicle, the corresponding countermeasure priority is matched, and the reactive unmanned aerial vehicle which can move on the my side is reasonably allocated to tasks according to the number and the characteristics of each cluster.
Description
Technical Field
The invention relates to the field of man-machine countermeasures, in particular to a 'bee colony' countermeasure method for an unmanned aerial vehicle.
Background
Multiple unmanned security events have occurred worldwide. The public has become increasingly aware of the hazards that may be posed by unmanned aerial vehicles. At present, the countering means for the unmanned aerial vehicle mainly comprises: fire interception, helicopter air interception forced landing, net throwing capture, electronic interference, microwave damage, strong laser damage and the like. In many countermeasures, electronic interference has long acting distance, large angle and strong capability of coping with multiple targets, and becomes a development hot spot.
For an unmanned aerial vehicle 'bee colony' system consisting of a plurality of unmanned aerial vehicles, the unmanned aerial vehicle 'bee colony' system consists of a large-scale small unmanned aerial vehicle fight platform carrying different loads, a distributed intelligent colony technology is used as a basis, and the complex tasks such as warning, searching, target positioning, azimuth guiding and attack are completed under various environments through the perception interaction of single unmanned aerial vehicles and the information transmission of each other, so that the effects of low cost, low consumption and high efficiency are achieved. The unmanned aerial vehicle 'bee colony' system has the characteristics of large scale, small volume, low price, decentralization, autonomous decision and the like, has new characteristics superior to the traditional air threat, and is enough to cause huge pressure on any existing air defense system.
The method mainly comprises the steps of carrying out soft destruction and hard killing on the basis of detection and perception, wherein the method mainly comprises the steps of interference suppression and induction and the like of the unmanned aerial vehicle 'bee colony' of enemy, and the method mainly comprises the steps of attack by using artillery and laser directional energy weapon, bee colony attack resistance, physical capture and the like. Aiming at the 'saturation' attack of 'bee colony' of an unmanned aerial vehicle, the modern armed forces are applied to strike, the efficiency-cost ratio is low, and the exposure, exhaustion and even paralysis of the fight resources of an air defense system can be caused.
At this time, the adoption of "colony" to combat the enemy "colony" is an "equivalent" and "efficient" combat scheme. The electronic countering unmanned aerial vehicle with large-area soft killing capability can be used for carrying out interference development on the enemy unmanned aerial vehicle group, the electronic countering unmanned aerial vehicle with large-area soft killing capability can also be used for carrying out counterattack on the enemy unmanned aerial vehicle, or the obstacle is arranged in a large area to block the flight of the enemy unmanned aerial vehicle, and even the electronic countering unmanned aerial vehicle can be used for carrying out short-distance accurate striking by using an airborne weapon for fight.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a method for fighting against 'bee colony' of an unmanned aerial vehicle. The method can achieve acquisition of target clustering results and match corresponding countermeasure priorities for the clusters of the unmanned aerial vehicle clusters in the three-dimensional space density clusters, and reasonably distributes tasks of the reactive unmanned aerial vehicles capable of moving according to the number and the characteristics of each cluster.
In order to achieve the above purpose, the invention adopts the following technical scheme: an unmanned aerial vehicle 'swarm' countermeasure method comprising the steps of:
step S1, acquiring three-dimensional space position distribution information of an enemy unmanned aerial vehicle bee colony according to a radar, acquiring information of the enemy unmanned aerial vehicle bee colony based on a clustering algorithm, and obtaining an augmented cluster ordering of the enemy unmanned aerial vehicle bee colony;
S2, acquiring a clustering result of the enemy unmanned aerial vehicle bee colony through an OPTICS clustering algorithm according to the acquired information and the amplified cluster sequencing parameters;
step S3, obtaining an optimized clustering result according to the relationship data between the enemy unmanned aerial vehicle bee colony and the target to be protected;
and S4, optimizing the countermeasure allocation according to the optimized clustering result and the priority level.
In a preferred embodiment of the present invention, in step S2, an eps value is calculated according to the scale and distribution of the enemy unmanned aerial vehicle bee colony to be opposed, where the eps value is a neighborhood radius value; obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering diagram and the core distance matrix obtained by the clustering algorithm; or/and, in step S3, calculating the shortest distance index between the unmanned aerial vehicle in the enemy unmanned aerial vehicle bee colony and the target to be protected, optimizing the clustering result obtained based on the clustering algorithm, so that the clustering number is greater than or equal to minPts, wherein minPts is the minimum number of the Eps neighborhood, and meanwhile, the unmanned aerial vehicle which is closer to the target to be protected and has a closer countermeasure distance can be used as an expansion node to add into the clustering; calculating cluster type parameters related to the expansion node; or/and, in step S4, setting priority for each cluster according to the shortest distance between the cluster center of each cluster and the target to be protected; and performing countermeasure task allocation according to the combat effectiveness maximization principle and the high-value target defense principle of the target to be protected.
In a preferred embodiment of the present invention, the three-dimensional spatial location distribution information includes longitude, latitude, and altitude; the longitude and latitude data are converted into radians, and the altitude is expressed by taking meters as units; or/and, each cluster type parameter includes one or more of cluster center, number of members, average core density, and neighborhood radius of each cluster.
In a preferred embodiment of the present invention, in step S1, minPts is set to be the minimum number of neighbors within the radius epsilon of the field, the detected target position is three-dimensional data, and the value of minPts is not less than 4; density clustering is carried out on the enemy unmanned aerial vehicle bee colony based on a density clustering algorithm OPTICS, wherein the calculation method of the distance between the enemy unmanned aerial vehicles is as follows:
setting the spatial positions of a first node and a second node to be (lon_1, lat_1, alt_1) and (lon_12, lat_2, alt_2) respectively, wherein lon refers to longitude, lat refers to latitude and alt refers to altitude; the Haverine distance of the longitude and latitude of the node I and the node II is set as a formula haverine ((lon_1, lat_1)). M, the measurement unit is meter, and the distance calculation method of the longitude and latitude of the altitude of the node I and the node II is as follows:
dist=sqrt (haverine ((lon_1, lat_1)) · (lon_12, lat_2)) · m · 2, (alt_1-alt_2) · m · where "· m" represents measuring distance by meter, and after density clustering of the enemy unmanned aerial vehicle bee colony, a cluster structure of the enemy unmanned aerial vehicle bee colony density, i.e., an augmented cluster ordering, can be obtained.
In a preferred embodiment of the present invention, in step S2, the obtaining of the OPTICS clustering result is to calculate the eps value according to the scale of the enemy unmanned aerial vehicle bee colony to be subjected to the countermeasure according to the cluster of the valley portion formed by the eps value in the reachability distance map; and according to the computed eps value, in the reachability distance map obtained by OPTICS clustering, obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering map obtained by the OPTICS clustering algorithm and a core distance matrix thereof. Specifically, aiming at the detected enemy unmanned aerial vehicle swarm with the scale of n, n is the number of enemy unmanned aerial vehicles in the enemy unmanned aerial vehicle swarm; if the fight attack (n > x) is expected to be carried out on the x unmanned aerial vehicles, firstly, the accessibility distance matrix obtained by calculation of an OPTICS clustering algorithm is ordered according to ascending order, then the accessibility distance value of the x-th bit is taken as the eps value, and the number of the clustered unmanned aerial vehicles obtained at the moment is less than or equal to x; wherein some nodes are noise points, the value of x/n is between 0.4 and 0.7 according to the proper adjustment value of the noise points and the clustering quantity, namely the ratio of the unmanned aerial vehicle to the enemy bee colony is between 40 and 70 percent. Specifically, the value of x is too small, because the unmanned aerial vehicle bee colony is widely distributed, too small eps value can lead to that many unmanned aerial vehicles are noise points and can not be gathered, on the contrary, if the value of x is too large, most unmanned aerial vehicles are placed in clusters, and the adjacent distance of unmanned aerial vehicles in the clusters is too large to lose the practical meaning of countermeasure.
In a preferred embodiment of the present invention, in step S3, the shortest distance between each unmanned aerial vehicle in the enemy bee colony and the target to be protected is calculated, and the distance value is quantized to the [0.9,1.1] interval, which represents the distance index between the unmanned aerial vehicle and the target to be protected, and if the distance index value is less than 1, it represents that the unmanned aerial vehicle is relatively close to the important target in the my, otherwise, it is relatively far; according to the clustering result of the enemy unmanned aerial vehicle bee colony obtained by calculating the eps value, when the number of nodes in the clustering cluster is smaller than minPts, the clustering clusters are required to be subjected to node processing.
In a preferred embodiment of the present invention, the number of unmanned aerial vehicles in the enemy bee colony is set as n, which is counted as a set u= { U of unmanned aerial vehicles in the enemy bee colony 1 ,u 2 ,u n The number of the targets to be protected on the my side is q, and the targets to be protected on the my side are counted as a set O= { O 1 ,o 2 ,o n -a }; then for any one of the unmanned aerial vehicles u i With any target Q in the key target set Q j Distance d of (2) ij Then a distance matrix D can be obtained n×q Wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to q. Let d min =min (D) and D max =max (D) represents the closest and furthest distances of each drone in the enemy swarm from the object to be protected of my, respectively. For any unmanned aerial vehicle u i Definition u i The nearest key target distance min (d ij ) Is d i U is i Distance quantization value l from my target to be protected i Is that
In a preferred embodiment of the present invention, in step S3, the node processing includes: firstly, obtaining distance indexes of all nodes in a cluster, wherein the distance indexes are larger than 1, and if the distance indexes are larger than 1, the distance indexes indicate that all unmanned aerial vehicles in the cluster are far away from an important target of the user, and the nodes in the cluster are used as noise points;
otherwise, the nearest minPts non-clustered adjacent nodes of each node are taken to be added into a to-be-processed node list, and for each node in the list, the weighted core distance of the adjacent nodes is smaller than eps, namely the multiplication of the core distance and the distance index is smaller than eps, the adjacent nodes are used as the expansion nodes of the clusters to be added into the clusters, otherwise, the nodes are used as noise nodes;
and sequentially processing all the nodes to be processed, if the total number of the nodes of the cluster after expansion is more than or equal to minPts, reserving the cluster, otherwise, setting all the nodes in the cluster as noise points.
In a preferred embodiment of the present invention, in step S3, after the clustering result is optimized, the clustering result of the enemy unmanned aerial vehicle bee colony is further analyzed, the cluster center, the number of members, the shortest distance from the cluster center to the target to be protected, the average cluster core density and the maximum cluster radius of each cluster are calculated, then the priority of each cluster is set according to the shortest distance from the cluster center to the target to be protected, that is, the priority of the node closest to the target to be protected is the highest, the lower the priority is along with the increase of the distance, the cluster number is defined as p, the priority can be allocated according to 0-p-1, and 1 (p-1)/p, 1/p are respectively used to represent the priority weight.
In a preferred embodiment of the present invention, in step S4, a challenge task list of the unmanned aerial vehicle bee colony capable of challenge is obtained according to the clustering result of the enemy unmanned aerial vehicle bee colony, and the task allocation is performed on the unmanned aerial vehicle bee colony capable of challenge according to the battle efficiency maximization principle and the important high-value target optimal defense principle, aiming at the type and the number of the unmanned aerial vehicles of the unmanned aerial vehicle bee colony capable of challenge.
In a preferred embodiment of the present invention, in step S4, the method for distributing the combat efficacy values of each unmanned aerial vehicle in the unmanned aerial vehicle swarm against the enemy of different unmanned aerial vehicles in the unmanned aerial vehicle swarm against the enemy comprises: the distance between the unmanned aerial vehicle and the clustering center exceeds 1/2 of the maximum fight distance of the unmanned aerial vehicle, or the height of the clustering center exceeds the maximum flight height, the fight efficiency index value e is 0, namely the task cannot be completed; otherwise, calculating the task matching degree between the unmanned aerial vehicle and the task as k, and calculating the combat effectiveness index value according to 100- (4-k) x 25; the fight efficiency index values are classified according to grades, when the fight efficiency indexes of different unmanned aerial vehicles on the same task are the same, the priority order of the different unmanned aerial vehicles is defined, and the electronic fight unmanned aerial vehicle > attacks the unmanned aerial vehicle > to capture the unmanned aerial vehicle, namely, the electronic fight unmanned aerial vehicle is preferentially used under the same condition;
Or/and, in step S4, the combat task allocation method includes: setting the number of clusters of the enemy unmanned aerial vehicle cluster as t, and setting the number of unmanned aerial vehicles capable of moving as u, wherein u is a non-zero natural number, and two cases of t < u or t is more than or equal to u are included; the fight task allocation is to arrange all the unmanned aerial vehicles capable of being started to fight, and each unmanned aerial vehicle can only execute one task; firstly, meeting the defense principle of important targets, performing task allocation on clusters of enemy unmanned aerial vehicle bee colonies with highest priority and greatest threat distance, and then sequentially allocating unallocated tasks;
when t is more than or equal to u, the process is finished after all unmanned aerial vehicle tasks are distributed, and the far low-level remote clusters can be used as next combat targets for reprocessing; or when t is smaller than u, after all the tasks are distributed, calculating the fight efficiency value of the left u-t unmanned aerial vehicle and each task, and distributing the tasks to all the unmanned aerial vehicles according to the principle that the fight efficiency value is the largest.
The invention solves the defects existing in the background technology, and has the beneficial effects that:
an unmanned aerial vehicle 'bee colony' countermeasure method. The method can achieve acquisition of target clustering results and match corresponding countermeasure priorities for the clusters of the unmanned aerial vehicle clusters in the three-dimensional space density clusters, and reasonably distributes tasks of the reactive unmanned aerial vehicles capable of moving according to the number and the characteristics of each cluster. The fighting capacity of the enemy unmanned aerial vehicle bee colony is weakened to the greatest extent, the low-altitude defense difficulty is reduced, and the saturation attack of the enemy unmanned aerial vehicle bee colony is effectively counteracted.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a schematic diagram of a countermeasure system in accordance with a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a system for calculating a combat effectiveness index in accordance with a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of the system architecture of the unmanned aerial vehicle challenge task assignment of the preferred embodiment of the present invention;
fig. 4 is a schematic diagram of a reachability distance graph (segmented by threshold lines (eps), with three valleys representing 3 clusters, wherein the coordinate values of the reachability distances are only one segment representation, and the specific parameters of the actual reachability distances are defined according to the environment during actual operation) in the preferred embodiment of the present invention.
Detailed Description
The invention will now be described in further detail with reference to the drawings and examples, which are simplified schematic illustrations of the basic structure of the invention, which are presented only by way of illustration, and thus show only the structures that are relevant to the invention.
Example 1
As shown in fig. 1-4, a method for fighting a "bee colony" of an unmanned aerial vehicle includes the steps of:
step S1, acquiring three-dimensional space position distribution information of the enemy unmanned aerial vehicle bee colony according to a radar, acquiring information of the enemy unmanned aerial vehicle bee colony based on a clustering algorithm, and obtaining the augmented cluster sequencing of the enemy unmanned aerial vehicle bee colony. The three-dimensional spatial location distribution information includes longitude, latitude, and altitude; the longitude and latitude data are converted into radians, and the altitude is expressed by taking meters as units; the clustering algorithm adopts an OPTICS clustering algorithm, and each cluster clustering parameter of the OPTICS clustering algorithm comprises a cluster center, the number of members, average core density and maximum radius of each cluster.
Specifically, minPts are set to be the minimum neighbor number in the radius epsilon of the field, the detected target position is three-dimensional data, and the value of the minPts is more than or equal to 4; in this embodiment, the minPts value is 4. Density clustering is carried out on the enemy unmanned aerial vehicle bee colony based on a density clustering algorithm OPTICS, wherein the calculation method of the distance between the enemy unmanned aerial vehicles is as follows: setting the spatial positions of a first node and a second node to be (lon_1, lat_1, alt_1) and (lon_12, lat_2, alt_2) respectively, wherein lon refers to longitude, lat refers to latitude and alt refers to altitude; the distance between the longitude and latitude of the first node and the second node is set as a formula haverine (lon_1, lat_1) ((lon_12, lat_2)). M, the measurement unit is meter, the distance calculation method of the longitude and latitude of the first node and the second node is that dist=sqrt (lon_1, lat_1) ((lon_12, lat_2)). M.times.2, (alt_1-lat_2), ". Times.m' represents a method for measuring the distance by meter, and the cluster structure of the density of the enemy unmanned aerial vehicle bee colony can be obtained after density clustering is carried out on the enemy unmanned aerial vehicle bee colony, namely the cluster sequencing is enhanced.
And S2, obtaining a clustering result of the enemy unmanned aerial vehicle bee colony through a clustering algorithm according to the obtained information and the amplified cluster sequencing parameters. Calculating eps values according to the scale and distribution of the to-be-opposed enemy unmanned aerial vehicle bee colony, wherein the eps values are neighborhood radius values; and obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering diagram and the core distance matrix obtained by the clustering algorithm.
Specifically, the clustering algorithm adopts an OPTICS clustering algorithm; the acquisition of the OPTICS clustering result is to calculate the eps value according to the scale of the enemy unmanned aerial vehicle bee colony to be subjected to the countermeasure according to the cluster of the valley bottom part formed by the eps value in the reachability distance graph; aiming at a detected enemy unmanned aerial vehicle bee colony with the scale of n, if the enemy unmanned aerial vehicle is expected to resist attack (n > x), firstly, sequencing an accessibility distance matrix calculated by an OPTICS clustering algorithm according to ascending order, and then taking an accessibility distance value of an mth bit as an eps value of the accessibility distance matrix, wherein the number of clustered unmanned aerial vehicles obtained at the moment is less than or equal to x; wherein, few nodes are noise points, the value of x is moderately increased, and preferably, the value of x/n is 0.4-0.7. Specifically, when the value of x is too small, because the unmanned aerial vehicle bee colony is widely distributed, too small eps values can cause that many unmanned aerial vehicles are noise points which cannot be gathered, and conversely, when the value of m is too large, most unmanned aerial vehicles are placed in clusters, so that the adjacent distances of unmanned aerial vehicles in the clusters are too large to lose the practical significance of countermeasure.
And according to the computed eps value, in the reachability distance map obtained by OPTICS clustering, obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering map obtained by the OPTICS clustering algorithm and a core distance matrix thereof.
And S3, obtaining an optimized clustering result according to the relationship data between the enemy unmanned aerial vehicle bee colony and the target to be protected.
Specifically, calculating the shortest distance index of the unmanned aerial vehicle in the enemy unmanned aerial vehicle bee colony and the target to be protected, optimizing the clustering result obtained based on the clustering algorithm to ensure that the clustering quantity is more than or equal to minPts, wherein minPts is the minimum number of Eps neighborhood points, and meanwhile, the unmanned aerial vehicle which is closer to the target to be protected and has a closer countermeasure distance can be used as an expansion node to add into the clustering; and calculating cluster type parameters related to the extension node.
Further, calculating the shortest distance between each unmanned plane in the enemy bee colony and the target to be protected, and determining the distanceValue quantization to [0.9,1.1 ]]Interval representing the distance index between the target and the target to be protected, if the distance index value<1, representing that the target is relatively close to the important target on the my side, or else, relatively far; according to the clustering result of the enemy unmanned aerial vehicle bee colony obtained by calculating the eps value, when the number of nodes in the clustering cluster is smaller than minPts, the clustering clusters are required to be subjected to node processing. Specifically, let enemy bee colony unmanned aerial vehicle quantity be n, count as enemy bee colony unmanned aerial vehicle collection U= { U 1 ,u 2 ,u n The number of the targets to be protected on the my side is q, and the targets to be protected on the my side are counted as a set O= { O 1 ,o 2 ,o n -a }; then for any one of the unmanned aerial vehicles u i With any target Q in the key target set Q j Distance d of (2) ij Then a distance matrix D can be obtained n×q Wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to q. Let d min =min (D) and D max =max (D) represents the closest and furthest distances of each drone in the enemy swarm from the object to be protected of my, respectively. For any unmanned aerial vehicle u i Definition u i The nearest key target distance min (d ij ) Is d i U is i Distance quantization value l from my target to be protected i Is that
The node processing comprises the following steps: firstly, obtaining distance indexes of all nodes in a cluster, wherein the distance indexes are larger than 1, and if the distance indexes are larger than 1, the distance indexes indicate that all unmanned aerial vehicles in the cluster are far away from an important target of the user, and the nodes in the cluster are used as noise points; otherwise, the nearest minPts non-clustered adjacent nodes of each node are taken to be added into a to-be-processed node list, and for each node in the list, the weighted core distance of the adjacent nodes is smaller than eps, namely the multiplication of the core distance and the distance index is smaller than eps, the adjacent nodes are used as the expansion nodes of the clusters to be added into the clusters, otherwise, the nodes are used as noise nodes; and sequentially processing all the nodes to be processed, if the total number of the nodes of the cluster after expansion is more than or equal to minPts, reserving the cluster, otherwise, setting all the nodes in the cluster as noise points. After the clustering result is optimized, further analyzing the clustering result of the enemy unmanned aerial vehicle bee colony, calculating the cluster center, the number of members, the shortest distance from the cluster center to the target to be protected, the average core density and the maximum radius of the clusters, setting the priority of each cluster according to the shortest distance from the cluster center to the target to be protected, namely, the node closest to the target to be protected has the highest priority, along with the increase of the distance, the lower the priority is, the number of clusters is defined as p, the priority can be allocated according to 0-p-1, and the priority weights are respectively expressed by 1, (p-1)/p, and 1/p.
And S4, optimizing the countermeasure allocation according to the optimized clustering result and the priority level. Setting priority for each cluster according to the shortest distance between the cluster center of each cluster and the target to be protected; and performing countermeasure task allocation according to the combat effectiveness maximization principle and the high-value target defense principle of the target to be protected.
Specifically, a challenge task list of the unmanned aerial vehicle bee colony capable of challenge can be obtained according to a clustering result of the enemy unmanned aerial vehicle bee colony, and the challenge unmanned aerial vehicle bee colony is subjected to task allocation according to a fight efficiency maximization principle and an important high-value target optimal defense principle aiming at the type and the number of unmanned aerial vehicles of the unmanned aerial vehicle bee colony capable of challenge.
Further, the method for distributing the combat effectiveness values of the unmanned aerial vehicles of different categories in the combat unmanned aerial vehicle bee colony to each unmanned aerial vehicle in the enemy unmanned aerial vehicle bee colony comprises the following steps: as shown in fig. 2, if the distance between the unmanned aerial vehicle and the clustering center exceeds 1/2 of the maximum fight distance of the unmanned aerial vehicle, or if the height of the clustering center exceeds the maximum flight height, the fight efficiency index value e is 0, namely the unmanned aerial vehicle cannot be used for the task at all; otherwise, the task matching degree between the unmanned aerial vehicle and the task in the task matching degree calculation is k, and the matching values are 4,3,2 and 1 respectively from high to low; the task matching degree in this implementation is k. Calculating the combat effectiveness index value according to 100- (4-k) x 25; the fight efficiency index values are classified according to grades, when the fight efficiency indexes of different unmanned aerial vehicles on the same task are the same, the priority sequences of the different unmanned aerial vehicles are defined, and the electronic fighter unmanned aerial vehicle > attacks the unmanned aerial vehicle > to capture the unmanned aerial vehicle, namely, the electronic fighter unmanned aerial vehicle is preferentially used under the same condition.
In step S4, as shown in fig. 3, the combat task allocation method includes: setting the number of clusters of the enemy unmanned aerial vehicle cluster as t, and setting the number of unmanned aerial vehicles capable of moving as u, wherein u is a non-zero natural number, and two cases of t < u or t is more than or equal to u are included; the fight task allocation is to arrange all the unmanned aerial vehicles capable of being started to fight, and each unmanned aerial vehicle can only execute one task; firstly, meeting the defense principle of important targets, performing task allocation on clusters of enemy unmanned aerial vehicle bee colonies with highest priority and greatest threat distance, and then sequentially allocating unallocated tasks; when t is more than or equal to u, the process is finished after all unmanned aerial vehicle tasks are distributed, and the far low-level remote clusters can be used as next combat targets for reprocessing; or when t is smaller than u, after all the tasks are distributed, calculating the fight efficiency value of the left u-t unmanned aerial vehicle and each task, and distributing the tasks to all the unmanned aerial vehicles according to the principle that the fight efficiency value is the largest.
Furthermore, the small unmanned aerial vehicle 'bee colony' is usually released at the middle and low altitudes close to the target area group, and the method for countering the 'bee colony' by the 'bee colony' provided by the invention is mainly aimed at the unmanned aerial vehicle group at the middle and low altitudes and closer to the distance of the important target area, and the early warning value of low altitude early warning can be selected as that the flying height is between 1000 meters and 5000 meters, and the distance from the important target with high value is less than 200km. However, in other embodiments, the specific parameter setting may be adjusted according to the actual usage requirement.
Example two
As shown in fig. 1-3, a method for fighting a "bee colony" of an unmanned aerial vehicle includes the steps of:
step S1, acquiring three-dimensional space position distribution information of the enemy unmanned aerial vehicle bee colony according to a radar, acquiring information of the enemy unmanned aerial vehicle bee colony based on a clustering algorithm, and obtaining the augmented cluster sequencing of the enemy unmanned aerial vehicle bee colony. The three-dimensional spatial location distribution information includes longitude, latitude, and altitude; the longitude and latitude data are converted into radians, and the altitude is expressed by taking meters as units; the clustering algorithm adopts an OPTICS clustering algorithm or a DBCSAN clustering algorithm; the OPTICS clustering algorithm is preferred in this embodiment. Each cluster type parameter includes a cluster center, a number of members, an average core density, and a maximum radius of each cluster.
Specifically, minPts are set to be the minimum neighbor number in the radius epsilon of the field, the detected target position is three-dimensional data, and the value of the minPts is more than or equal to 4; in this embodiment, the minPts value is 4. Density clustering is carried out on the enemy unmanned aerial vehicle bee colony based on a density clustering algorithm OPTICS, wherein the calculation method of the distance between the enemy unmanned aerial vehicles is as follows: setting the spatial positions of a first node and a second node to be (lon_1, lat_1, alt_1) and (lon_12, lat_2, alt_2) respectively, wherein lon refers to longitude, lat refers to latitude and alt refers to altitude; the distance between the longitude and latitude of the first node and the second node is set as a formula haverine ((lon_1, lat_1) ((lon_12, lat_2)). M, the measurement unit is meter, the distance calculation method of the longitude and latitude of the first node and the second node is that dist=sqrt (hanverine ((lon_1, lat_1) ((lon_12, lat_2)). M.times.2 (alt_1-lat_2) (. Times.2)), wherein ". M" represents the distance measured by meter, and the clustering structure of the density of the enemy unmanned aerial vehicle bee colony can be obtained after density clustering is carried out on the enemy unmanned aerial vehicle bee colony, namely, the cluster is sorted in an enlarged manner.
And S2, obtaining a clustering result of the enemy unmanned aerial vehicle bee colony through a clustering algorithm according to the obtained information and the amplified cluster sequencing parameters. Calculating eps values according to the scale and distribution of the to-be-opposed enemy unmanned aerial vehicle bee colony, wherein the eps values are neighborhood radius values; and obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering diagram and the core distance matrix obtained by the clustering algorithm.
Specifically, the clustering algorithm adopts an OPTICS clustering algorithm; the acquisition of the OPTICS clustering result is to calculate the eps value according to the scale of the enemy unmanned aerial vehicle bee colony to be subjected to the countermeasure according to the cluster of the valley bottom part formed by the eps value in the reachability distance graph; aiming at a detected enemy unmanned aerial vehicle bee colony with the scale of n, if the fight attack (n > x) is expected to be carried out on x unmanned aerial vehicles in the enemy unmanned aerial vehicle bee colony, firstly, the reachability distance matrix calculated by an OPTICS clustering algorithm is ordered according to ascending order, then the reachability distance value of the x-th bit is taken as the eps value, and the number of the clustered unmanned aerial vehicles obtained at the moment is less than or equal to x; wherein, few nodes are noise points, the value of x is moderately increased, and preferably, the value of x/n is 0.4-0.7. Specifically, when the value of x is too small, because the unmanned aerial vehicle bee colony is widely distributed, too small eps values can cause that many unmanned aerial vehicles are noise points which cannot be gathered, and conversely, when the value of m is too large, most unmanned aerial vehicles are placed in clusters, so that the adjacent distances of unmanned aerial vehicles in the clusters are too large to lose the practical significance of countermeasure.
And according to the computed eps value, in the reachability distance map obtained by OPTICS clustering, obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering map obtained by the OPTICS clustering algorithm and a core distance matrix thereof.
And S3, obtaining an optimized clustering result according to the relationship data between the enemy unmanned aerial vehicle bee colony and the target to be protected.
Specifically, calculating the shortest distance index of the unmanned aerial vehicle in the enemy unmanned aerial vehicle bee colony and the target to be protected, optimizing the clustering result obtained based on the clustering algorithm to ensure that the clustering quantity is more than or equal to minPts, wherein minPts is the minimum number of Eps neighborhood points, and meanwhile, the unmanned aerial vehicle which is closer to the target to be protected and has a closer countermeasure distance can be used as an expansion node to add into the clustering; and calculating cluster type parameters related to the extension node.
Further, calculating the shortest distance between each unmanned plane in the enemy bee colony and the target to be protected, and quantifying the distance value to [0.9,1.1 ]]Interval representing the distance index between the target and the target to be protected, if the distance index value<1, representing that the target is relatively close to the important target on the my side, or else, relatively far; according to the clustering result of the enemy unmanned aerial vehicle bee colony obtained by calculating the eps value, when the number of nodes in the clustering cluster is smaller than minPts, the clustering clusters are required to be subjected to node processing. Let the number of unmanned aerial vehicles of enemy bee colony be n, count as enemy bee colony unmanned aerial vehicle set U= { U 1 ,u 2 ,u n The number of the targets to be protected on the my side is q, and the targets to be protected on the my side are counted as a set O= { O 1 ,o 2 ,o n -a }; then for any one of the unmanned aerial vehicles u i With any target Q in the key target set Q j Distance d of (2) ij Then a distance matrix D can be obtained n×q Wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to q. Let d min =min (D) and D max =max (D) represents the closest and furthest distances of each drone in the enemy swarm from the object to be protected of my, respectively. For any unmanned aerial vehicle u i Definition u i The nearest key target distance min (d ij ) Is d i U is i Distance quantization value l from my target to be protected i Is that
The node processing comprises the following steps: firstly, obtaining distance indexes of all nodes in a cluster, wherein the distance indexes are larger than 1, and if the distance indexes are larger than 1, the distance indexes indicate that all unmanned aerial vehicles in the cluster are far away from an important target of the user, and the nodes in the cluster are used as noise points; otherwise, the nearest minPts non-clustered adjacent nodes of each node are taken to be added into a to-be-processed node list, and for each node in the list, the weighted core distance of the adjacent nodes is smaller than eps, namely the multiplication of the core distance and the distance index is smaller than eps, the adjacent nodes are used as the expansion nodes of the clusters to be added into the clusters, otherwise, the nodes are used as noise nodes; and sequentially processing all the nodes to be processed, if the total number of the nodes of the cluster after expansion is more than or equal to minPts, reserving the cluster, otherwise, setting all the nodes in the cluster as noise points. After the clustering result is optimized, further analyzing the clustering result of the enemy unmanned aerial vehicle bee colony, calculating the cluster center, the number of members, the shortest distance from the cluster center to the target to be protected, the average core density and the maximum radius of the clusters, setting the priority of each cluster according to the shortest distance from the cluster center to the target to be protected, namely, the node closest to the target to be protected has the highest priority, along with the increase of the distance, the lower the priority is, the number of clusters is defined as p, the priority can be allocated according to 0-p-1, and the priority weights are respectively expressed by 1, (p-1)/p, and 1/p.
Table-examples of analysis data for clusters
And S4, optimizing the countermeasure allocation according to the optimized clustering result and the priority level. Setting priority for each cluster according to the shortest distance between the cluster center of each cluster and the target to be protected; and performing countermeasure task allocation according to the combat effectiveness maximization principle and the high-value target defense principle of the target to be protected.
Specifically, a challenge task list of the unmanned aerial vehicle bee colony capable of challenge can be obtained according to a clustering result of the enemy unmanned aerial vehicle bee colony, and the challenge unmanned aerial vehicle bee colony is subjected to task allocation according to a fight efficiency maximization principle and an important high-value target optimal defense principle aiming at the type and the number of unmanned aerial vehicles of the unmanned aerial vehicle bee colony capable of challenge.
Unmanned aerial vehicles capable of achieving antagonism generally include electronic antagonism unmanned aerial vehicles, observation and striking integrated unmanned aerial vehicles, unmanned aerial vehicles carrying combat attack weapons, unmanned aerial vehicles with the ability to capture and block enemy unmanned aerial vehicles, and the like, but are not limited thereto, and in embodiments thereof, unmanned aerial vehicles may be used. The fight capability of the unmanned aerial vehicle is mainly represented by fight radius, attack countermeasure capability and the like, and has the limitations of flight distance, flight height and the like. For electronic fight unmanned aerial vehicles, including radar fight and communication fight unmanned aerial vehicles, the fight radius is the maximum range of the electronic interference, and the corresponding attack fight capability can be set to a maximum value, which means that unmanned aerial vehicles in the fight radius can be attacked without quantity limitation. For an unmanned aerial vehicle against attack, the maximum operational radius is generally limited by the weapon attack range, and due to the mobility of the unmanned aerial vehicle, the proper operational radius can be set to be 3-10 times of the weapon attack range. For the unmanned aerial vehicle capable of capturing and blocking the flight of the unmanned aerial vehicle with the shooting capturing net and the like, the fight radius is dependent on the actual capturing net area, the flight speed and the like, the capturing net area is generally more than ten squares, the flight speed is generally more than 50 km/h, the fight radius is about 1000 meters, the attack resistance is related to the number of unmanned aerial vehicles distributed in the range, and the maximum value of the attack resistance can be set, so that the unmanned aerial vehicle is generally suitable for resisting denser low, slow and small unmanned aerial vehicle clusters.
Examples of main warfare technical indicators for typical classes of unmanned aerial vehicles used for countermeasures are shown in the following table:
according to the clustering result of the enemy unmanned aerial vehicle swarm, a task list of the unmanned aerial vehicle swarm fight for realizing the fight task can be obtained, and aiming at the type and the number of the unmanned aerial vehicles capable of playing for realizing the fight task, the fight unmanned aerial vehicle swarm for realizing the fight task can be allocated according to the fight efficiency maximization principle and the important high-value target optimal defense principle.
The task allocation method comprises the following steps:
if the number of clusters of the enemy unmanned aerial vehicle bee colony is a, the countermeasure tasks can be respectively expressed as T1 to Ta.
For each task Ti, the expression method is as follows:
ti= { priority, center, member, short_dist, mean_density, max_radius }, respectively representing its priority, cluster center position (longitude, latitude, altitude), number of members, shortest distance (meters) of cluster center to my important target, cluster average core density (meters), and cluster maximum radius (meters).
The b-frame unmanned aerial vehicle capable of achieving the fight task can be respectively expressed as U1-Ub. For each unmanned aerial vehicle Uj, the representation method comprises the following steps:
uj= { type, max_radius, max_capability, max_dist, max_height }, respectively representing the category, maximum combat radius, attack countermeasure capability, maximum flight distance (meters), and maximum flying height of each unmanned aerial vehicle.
The operational efficiency parameter effectiveness calculation of matching tasks with unmanned aerial vehicles is difficult to uniformly calculate by using an absolute formula, and therefore, the information of each cluster is firstly converted from the result of each cluster analysis:
the number of the members in the cluster can be divided into 3 stages, which are respectively represented by 0-2, and the number of the representative members is small, general and large; the average core density of clusters of each cluster is divided into 3 levels, which are respectively represented by 0-2, and the clusters are small, general and large in density; the maximum radius of each cluster is divided into 3 stages, which are respectively represented by 0-2, and represent that the cluster density is small, general and large. Secondly, aiming at various task unmanned aerial vehicles, the unmanned aerial vehicle group style conditions adapting to the countermeasure can be represented in a grading manner according to the combat capability, and then the unmanned aerial vehicle group style conditions can be classified into 5 grades and respectively represented by 0-4, and the unmanned aerial vehicles represent complete mismatching, general, very matching and complete matching.
Task matching degree tables of different types of unmanned aerial vehicles aiming at different antagonistic unmanned aerial vehicle bee groups are shown in the following table:
the method for distributing the fight efficiency values of different types of unmanned aerial vehicles in the fight unmanned aerial vehicle bee colony to each unmanned aerial vehicle in the enemy unmanned aerial vehicle bee colony comprises the following steps: as shown in fig. 2, if the distance between the unmanned aerial vehicle and the clustering center exceeds 1/2 of the maximum fight distance of the unmanned aerial vehicle, or if the height of the clustering center exceeds the maximum flight height, the fight efficiency index value e is 0, namely the unmanned aerial vehicle cannot be used for the task at all; otherwise, calculating the task matching degree between the unmanned aerial vehicle and the task, wherein k is the task matching degree between the unmanned aerial vehicle and the task; the matching values are 4,3,2,1 from high to low, respectively. Calculating the combat effectiveness index value according to 100- (4-k) x 25; the fight efficiency index values are classified according to grades, when the fight efficiency indexes of different unmanned aerial vehicles on the same task are the same, the priority sequences of the different unmanned aerial vehicles are defined, and the electronic fighter unmanned aerial vehicle > attacks the unmanned aerial vehicle > to capture the unmanned aerial vehicle, namely, the electronic fighter unmanned aerial vehicle is preferentially used under the same condition.
In step S4, as shown in fig. 3, the combat task allocation method includes: setting the number of clusters of the enemy unmanned aerial vehicle cluster as t, and setting the number of unmanned aerial vehicles capable of moving as u, wherein u is a non-zero natural number, and two cases of t < u or t is more than or equal to u are included; the fight task allocation is to arrange all the unmanned aerial vehicles capable of being started to fight, and each unmanned aerial vehicle can only execute one task; firstly, meeting the defense principle of important targets, performing task allocation on clusters of enemy unmanned aerial vehicle bee colonies with highest priority and greatest threat distance, and then sequentially allocating unallocated tasks; when t is more than or equal to u, the process is finished after all unmanned aerial vehicle tasks are distributed, and the far low-level remote clusters can be used as next combat targets for reprocessing; or when t is smaller than u, after all the tasks are distributed, calculating the fight efficiency value of the left u-t unmanned aerial vehicle and each task, and distributing the tasks to all the unmanned aerial vehicles according to the principle that the fight efficiency value is the largest.
Furthermore, the small unmanned aerial vehicle 'bee colony' is usually released at the middle and low altitudes close to the target area group, and the method for countering the 'bee colony' by the 'bee colony' provided by the invention is mainly aimed at the unmanned aerial vehicle group at the middle and low altitudes and closer to the distance of the important target area, and the early warning value of low altitude early warning can be selected as that the flying height is between 1000 meters and 5000 meters, and the distance from the important target with high value is less than 200km. However, in other embodiments, the specific parameter setting may be adjusted according to the actual usage requirement.
Working principle:
according to the countering method, the density clustering is carried out on the enemy unmanned aerial vehicle bee colony based on OPTICS to obtain the augmented cluster sequencing of the enemy unmanned aerial vehicle bee colony, and the clustering result of the unmanned aerial vehicle bee colony can be obtained according to the enemy-resistant bee colony target. And calculating a neighborhood radius eps according to the target of the anti-enemy bee colony, acquiring a clustering result of the unmanned aerial vehicle bee colony to be counteracted, and adjusting and optimizing the clustering result according to the distance between the unmanned aerial vehicle bee colony to be counteracted and the important target of the user. According to the clustering result, the central node position, the node number, the average core distance of the clusters, the maximum clustering radius and the like of each cluster of the unmanned aerial vehicle to be countered can be obtained, the fight against priority is set for each cluster according to the shortest distance between the centers of each cluster of the unmanned aerial vehicle to be countered and an important target to be protected, the fight against efficiency is optimized as the target, and reasonable task allocation is carried out on the fight against unmanned aerial vehicle which can move according to the number and the characteristics of each cluster of the unmanned aerial vehicle to be countered, so that the fight against capability of the unmanned aerial vehicle to be enemy is weakened to the greatest extent, the low-altitude defending difficulty is reduced, and the saturated attack of the unmanned aerial vehicle to be enemy is effectively countered. To achieve safe and efficient interference countermeasures for military management areas or other institutions or areas of unknown origin unmanned aerial vehicles involving privacy.
The above-described preferred embodiments according to the present invention are intended to suggest that, from the above description, various changes and modifications can be made by the person skilled in the art without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. An unmanned aerial vehicle 'bee colony' countermeasure method is characterized by comprising the following steps:
step S1, acquiring three-dimensional space position distribution information of an enemy unmanned aerial vehicle bee colony according to a radar, acquiring information of the enemy unmanned aerial vehicle bee colony based on a clustering algorithm, and obtaining an augmented cluster ordering of the enemy unmanned aerial vehicle bee colony;
s2, acquiring a clustering result of the enemy unmanned aerial vehicle bee colony through an OPTICS clustering algorithm according to the acquired information and the amplified cluster sequencing parameters;
step S3, obtaining an optimized clustering result according to the relationship data between the enemy unmanned aerial vehicle bee colony and the target to be protected;
and S4, optimizing the countermeasure allocation according to the optimized clustering result and the priority level.
2. A method of unmanned aerial vehicle "swarm" challenge according to claim 1, wherein: in step S2, calculating eps values according to the scale and distribution of the to-be-opposed enemy unmanned aerial vehicle bee colony, wherein the eps values are neighborhood radius values; obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering diagram and the core distance matrix obtained by the clustering algorithm;
Or/and, in step S3, calculating the shortest distance index between the unmanned aerial vehicle in the enemy unmanned aerial vehicle bee colony and the target to be protected, optimizing the clustering result obtained based on the clustering algorithm, so that the clustering number is greater than or equal to minPts, wherein minPts is the minimum number of the Eps neighborhood, and meanwhile, the unmanned aerial vehicle which is closer to the target to be protected and has a closer countermeasure distance can be used as an expansion node to add into the clustering; calculating cluster type parameters related to the expansion node;
or/and, in step S4, setting priority for each cluster according to the shortest distance between the cluster center of each cluster and the target to be protected; and performing countermeasure task allocation according to the combat effectiveness maximization principle and the high-value target defense principle of the target to be protected.
3. A method of unmanned aerial vehicle "swarm" challenge according to claim 2, wherein: the three-dimensional space position distribution information comprises longitude, latitude and altitude; the longitude and latitude data are converted into radians, and the altitude is expressed by taking meters as units;
or/and, each cluster type parameter includes one or more of cluster center, number of members, average core density, and neighborhood radius of each cluster.
4. A method of unmanned aerial vehicle "swarm" challenge according to claim 2, wherein: in the step S1, minPts are set as the minimum neighbor number in the radius epsilon of the field, the detected target position is three-dimensional data, and the value of the minPts is more than or equal to 4;
Density clustering is carried out on the enemy unmanned aerial vehicle bee colony based on a density clustering algorithm OPTICS, wherein the calculation method of the distance between the enemy unmanned aerial vehicles is as follows:
setting the spatial positions of a first node and a second node to be (lon_1, lat_1, alt_1) and (lon_12, lat_2, alt_2) respectively, wherein lon refers to longitude, lat refers to latitude and alt refers to altitude;
setting the Haverine distance of the longitude and latitude of the node I and the node II as a formula haverine ((lon_1, lat_1)). M, wherein the measurement unit is meter;
the distance calculation method for the altitude set longitude and latitude of the node I and the node II comprises the following steps:
dist=sqrt (haverine ((lon_1, lat_1)) · (lon_12, lat_2) ·m·2, (alt_1-a lt_2) ·2), where "·m" represents the distance measured in meters;
after density clustering is carried out on the enemy unmanned aerial vehicle bee colony, a cluster structure of the enemy unmanned aerial vehicle bee colony density, namely the augmented cluster sequencing, can be obtained.
5. A method of unmanned aerial vehicle "swarm" challenge according to claim 2, wherein: in step S2, obtaining an OPTICS clustering result, namely calculating an eps value according to the scale and the distribution of the enemy unmanned aerial vehicle bee colony to be subjected to countermeasure according to a cluster of a valley portion formed by the eps value in the reachability distance graph;
Aiming at a detected enemy unmanned aerial vehicle bee colony with the scale of n, if the fight attack (n > x) is expected to be carried out on x unmanned aerial vehicles in the enemy unmanned aerial vehicle bee colony, firstly, the reachability distance matrix calculated by an OPTICS clustering algorithm is ordered according to ascending order, then the reachability distance value of the x-th bit is taken as an eps value, and the number of the clustered unmanned aerial vehicles obtained at the moment is less than or equal to x; wherein some nodes are noise points, the value of x/n is 0.4-0.7 according to the moderate adjustment value of the noise points and the clustering quantity, namely the ratio of the unmanned aerial vehicle to the attack resistance to the enemy bee colony is 40-70%;
and according to the computed eps value, in the reachability distance map obtained by OPTICS clustering, obtaining a clustering result of the unmanned aerial vehicle bee colony according to the reachability distance ordering map obtained by the OPTICS clustering algorithm and a core distance matrix thereof.
6. A method of unmanned aerial vehicle "swarm" challenge according to claim 2, wherein: in step S3, calculating the shortest distance between each unmanned plane in the enemy bee colony and the target to be protected, quantifying the distance value to a [0.9,1.1] interval, representing the distance index between the unmanned plane and the target to be protected, if the distance index value is less than 1, representing that the unmanned plane is relatively close to the important target on the my side, otherwise, relatively far; according to the clustering result of the enemy unmanned aerial vehicle bee colony obtained by calculating the eps value, when the number of nodes in the clustering cluster is smaller than minPts, the clustering clusters are required to be subjected to node processing.
7. According to claim 6The unmanned aerial vehicle 'bee colony' countermeasure method is characterized in that: let the number of unmanned aerial vehicles of enemy bee colony be n, count as enemy bee colony unmanned aerial vehicle set U= { U 1 ,u 2 ,…u n The number of the targets to be protected on the my side is q, and the targets to be protected on the my side are counted as a set O= { O 1 ,o 2 ,…o n -a }; then for any one of the unmanned aerial vehicles u i With any target Q in the key target set Q j Distance d of (2) ij Then a distance matrix D can be obtained n×q Wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to q; let d min =min (D) and D max =max (D) represents the closest and farthest distances between each unmanned aerial vehicle in the enemy bee colony and the my target to be protected, respectively; for any unmanned aerial vehicle u i Definition u i The nearest key target distance min (d ij ) Is d i U is i Distance quantization value l from my target to be protected i Is that
8. The unmanned aerial vehicle "swarm" challenge method of claim 6, wherein: in step S3, the node processing includes: firstly, obtaining distance indexes of all nodes in a cluster, wherein the distance indexes are larger than 1, and if the distance indexes are larger than 1, the distance indexes indicate that all unmanned aerial vehicles in the cluster are far away from an important target of the user, and the nodes in the cluster are used as noise points;
otherwise, the nearest minPts non-clustered adjacent nodes of each node are taken to be added into a to-be-processed node list, and for each node in the list, the weighted core distance of the adjacent nodes is smaller than eps, namely the multiplication of the core distance and the distance index is smaller than eps, the adjacent nodes are used as the expansion nodes of the clusters to be added into the clusters, otherwise, the nodes are used as noise nodes;
And sequentially processing all the nodes to be processed, if the total number of the nodes of the cluster after expansion is more than or equal to minPts, reserving the cluster, otherwise, setting all the nodes in the cluster as noise points.
9. The unmanned aerial vehicle "swarm" challenge method of claim 8, wherein: in step S3, after optimizing the clustering result, further analyzing the clustering result of the enemy unmanned aerial vehicle bee colony, calculating the cluster center, the number of members, the shortest distance from the cluster center to the target to be protected, the average core density of the clusters and the maximum radius of the clusters, setting the priority of each cluster according to the shortest distance from the cluster center to the target to be protected, namely, the priority of the node closest to the target to be protected is the highest, along with the increase of the distance, the lower the priority is, the number of clusters is defined as p, the priority can be allocated according to 0-p-1, and 1, (p-1)/p, 1/p represent the priority weight of the cluster respectively;
in step S4, a challenge task list of the unmanned aerial vehicle bee colony capable of challenge is obtained according to the clustering result of the enemy unmanned aerial vehicle bee colony, and the task allocation is performed on the unmanned aerial vehicle bee colony capable of challenge according to the fight efficiency maximization principle and the important high-value target optimal defense principle for the unmanned aerial vehicle type and the number of the unmanned aerial vehicle bee colony capable of challenge.
10. A method of unmanned aerial vehicle "swarm" challenge according to claim 2, wherein: in the step S4 of the process,
the method for distributing the fight efficiency values of different types of unmanned aerial vehicles in the fight unmanned aerial vehicle bee colony to each unmanned aerial vehicle in the enemy unmanned aerial vehicle bee colony comprises the following steps:
the distance between the unmanned aerial vehicle and the clustering center exceeds 1/2 of the maximum fight distance of the unmanned aerial vehicle, or the height of the clustering center exceeds the maximum flight height, the fight efficiency index value e is 0, namely the task cannot be completed; otherwise, calculating the task matching degree between the unmanned aerial vehicle and the task as k, and calculating the combat effectiveness index value according to 100- (4-k) x 25;
the fight efficiency index values are classified according to grades, when the fight efficiency indexes of different unmanned aerial vehicles on the same task are the same, the priority order of the different unmanned aerial vehicles is defined, and the electronic fight unmanned aerial vehicle > attacks the unmanned aerial vehicle > to capture the unmanned aerial vehicle, namely, the electronic fight unmanned aerial vehicle is preferentially used under the same condition;
or/and the combination of the two,
in step S4, the combat task allocation method includes: setting the number of clusters of the enemy unmanned aerial vehicle cluster as t, and setting the number of unmanned aerial vehicles capable of moving as u, wherein u is a non-zero natural number, and two cases of t < u or t is more than or equal to u are included; the fight task allocation is to arrange all the unmanned aerial vehicles capable of being started to fight, and each unmanned aerial vehicle can only execute one task; firstly, meeting the defense principle of important targets, performing task allocation on clusters of enemy unmanned aerial vehicle bee colonies with highest priority and greatest threat distance, and then sequentially allocating unallocated tasks; when t is more than or equal to u, the process is finished after all unmanned aerial vehicle tasks are distributed, and the far low-level remote clusters can be used as next combat targets for reprocessing;
Or when t is smaller than u, after all the tasks are distributed, calculating the fight efficiency value of the left u-t unmanned aerial vehicle and each task, and distributing the tasks to all the unmanned aerial vehicles according to the principle that the fight efficiency value is the largest.
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