CN113221235A - Method for establishing real-time combined strike optimization model - Google Patents

Method for establishing real-time combined strike optimization model Download PDF

Info

Publication number
CN113221235A
CN113221235A CN202110290487.1A CN202110290487A CN113221235A CN 113221235 A CN113221235 A CN 113221235A CN 202110290487 A CN202110290487 A CN 202110290487A CN 113221235 A CN113221235 A CN 113221235A
Authority
CN
China
Prior art keywords
equipment
interception
target
intercepting
striking
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110290487.1A
Other languages
Chinese (zh)
Inventor
苑文楠
侯师
吕鑫
欧阳铜
彭延云
贾彦翔
卞伟伟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Machinery Equipment Research Institute
Original Assignee
Beijing Machinery Equipment Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Machinery Equipment Research Institute filed Critical Beijing Machinery Equipment Research Institute
Priority to CN202110290487.1A priority Critical patent/CN113221235A/en
Publication of CN113221235A publication Critical patent/CN113221235A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD

Abstract

The application discloses a method for establishing a real-time combined strike optimization model, which comprises the following steps: acquiring a data set for establishing a combined strike optimization model, wherein the data set comprises weather environment data, target characteristic data, interception equipment data and a shielding relation between a target and interception equipment, and the interception equipment comprises laser equipment, radio equipment and flexible network equipment; respectively establishing space dimension constraint, time dimension constraint, resource dimension constraint, weather dimension constraint and environment dimension constraint of each interception device according to the data set; and establishing an interception weight factor according to the constraints, and establishing a combined strike optimization model according to the interception weight factor. According to the method, the equipment interception weight factor is introduced to restrain the equipment combat effectiveness according to the limiting conditions of the attack target and the interception equipment in five dimensions of space, time, resources, weather and environment, and the attack scheme optimization model is constructed according to the constraint conditions, so that the feasibility of the attack scheme result is improved in the aspect of model establishment.

Description

Method for establishing real-time combined strike optimization model
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle interception, and relates to a method for establishing a real-time combined strike optimization model.
Background
In recent years, with the gradual opening of low-altitude air-space domain control and the rapid development of unmanned aerial vehicle technology, the phenomenon of 'black flight' of unmanned aerial vehicles is increasing, and the illegal operation thereof generates non-negligible threat to civil aviation safety problems and seriously interferes with normal social order.
At present, the treatment means for the low-slow small target mainly comprises laser interception, radio interference, net interception and the like. However, the treatment and interception of the 'low-slow small' target by using the laser weapon has some disadvantages, which are mainly shown in the following steps: 1) the laser is easy to attenuate in the atmosphere, the range of the laser is influenced by the atmosphere, the laser does not have all-weather fighting capacity, once the laser encounters severe weather such as dense cloud, rain fog, thunder, electricity, snow haze and the like, the quality of a light beam is deteriorated, and the power of application is difficult to exert; 2) the tracking and aiming difficulty is high, and when a target is intercepted, if the sight is blocked or the target moves highly flexibly, the tracking and aiming of the target can reach ideal precision, so that the problem is solved; 3) with the increase of the range, the facula formed on the target by the light beam is gradually increased, so that the power density of the laser is reduced; 4) the energy conversion efficiency is low, the volume and the mass of the laser weapon system are large, and the maneuverability is not high. The radio interference device interferes the control signal and the GPS navigation signal of the unmanned aerial vehicle by transmitting a radio wave beam, but the treatment object is single, so that other normal civil activities are easily influenced, and unpredictable consequences of falling, hovering or returning can occur due to different program preset modes of the unmanned aerial vehicle. The net type soft killing interception technology is a novel interception technology, can implement high-precision net type interception on a suspicious target within a distance of three to four hundred meters, but has a smaller interception range, and the interception precision of a flexible net is greatly influenced by wind. Therefore, the intercepting means of the single system equipment cannot meet the prevention and control requirements. Therefore, a plurality of single-body intercepting equipment integrating flexible nets, lasers, directional electronic interference and the like needs to be developed, and an effective intercepting means of real-time combined striking is formulated for a low-slow small target with a typical threat mode. In the process of combined attack, it is very important to make a attack scheme decision with high interception rate and strong feasibility according to deployed interception equipment and an attack target or target group.
At present, in defense combat of a low-slow small target, an interception scheme can be formulated according to a manual experience mode, but the mode lacks theoretical derivation, and meanwhile, with the increasing of weapon types, the gradual expansion of a battlefield range or the gradual complicated situation evolution, a feasible and efficient interception scheme is difficult to decide only according to the experience of an index officer; on the other hand, a striking scheme can be formulated by referring to the existing weapon distribution model, but the existing model mainly aims at solving certain specific problems, such as ship striking, air defense and reverse guidance, and lacks a method for formulating a combined striking scheme in real time according to three equipment characteristics of laser, radio and flexible network aiming at a low-slow small target. Meanwhile, in the modeling process, the constraint condition is only constructed according to the relation between the target position and the fighting range of the intercepting equipment in the existing method, so that the feasibility of the obtained theoretical striking scheme in actual command fighting is low.
Disclosure of Invention
In order to solve the problems in the related art, the application provides a method for establishing a real-time combined strike optimization model, and the technical scheme is as follows:
a method for building a real-time joint strike optimization model, the method comprising:
acquiring a data set for establishing a combined strike optimization model, wherein the data set comprises weather environment data, target characteristic data, interception equipment data and an occlusion relation between a target and interception equipment, and the interception equipment comprises laser equipment, radio equipment and flexible network equipment;
establishing space dimension constraint of each intercepting equipment according to the target characteristic data in the data set and the intercepting equipment data;
according to the target characteristic data and the interception equipment data in the data set, judging the relation between the flight time of the target in the prevention and control range of the interception equipment and the response time of the interception equipment, and respectively establishing time dimension constraints of each interception equipment;
establishing resource dimension constraints of each interception equipment according to the interception equipment data in the data set;
establishing weather dimension constraints of each intercepting equipment according to the weather environment data in the data set;
according to the shielding relation between the targets and the intercepting equipment in the data set, environment dimension constraints between each intercepting equipment and each target are established;
establishing an interception weight factor according to the space dimension constraint, the time dimension constraint, the resource dimension constraint, the weather dimension constraint and the environment dimension constraint, and establishing a combined strike optimization model according to the interception weight factor.
Optionally, the spatial dimension constraint of each intercepting equipment comprises a horizontal direction constraint of the intercepting equipment, a vertical direction constraint of the intercepting equipment, wherein:
the horizontal direction constraint of the intercepting equipment is as follows: r is1sinα≤H≤r2sin α, wherein r1For the lower limit of the interception range of the intercepting equipment, r2Setting the upper limit of the interception range of the interception equipment, wherein alpha is the emission pitch angle of the interception equipment, and H is the distance in the vertical direction of the target;
the vertical direction constraint of the intercepting equipment is as follows: r is1cosβ≤D≤r2cos beta, wherein, r1For the lower limit of the interception range of the intercepting equipment, r2The upper limit of the interception range of the interception equipment, beta is the emission angle of the interception equipment, and D is the distance of the low-slow small target in the horizontal direction;
the spatial dimension constraint of the intercepting equipment is as follows:
Figure BDA0002982322670000021
wherein i is the equipment number, j is the target number, d is the space dimension mark,
Figure BDA0002982322670000022
is a spatial dimension constraint result.
Optionally, the time dimension constraint of each intercepting equipment is:
Figure BDA0002982322670000023
wherein, tiFor the battle response time of the ith equipment,
Figure BDA0002982322670000031
is a time dimension constraint result;
the passing time of the target in the interception range of the laser equipment is as follows:
Figure BDA0002982322670000032
the transit time of the target within the radio equipment interception range is:
Figure BDA0002982322670000033
the passing time of the target in the interception range of the flexible net equipment is as follows:
Figure BDA0002982322670000034
wherein s is the distance between the laser equipment and the target, rho is the distance between the laser equipment and the prevention and control central point, theta is the included angle of the connecting line of the laser equipment and the prevention and control central point in the horizontal direction,θmis the angle between the connecting line of the target and the prevention and control central point and the horizontal direction, v is the detected target flight speed, tjThe flight time of the target in the prevention and control range is taken as the target.
Optionally, the resource dimension constraint of each intercepting equipment is:
Figure BDA0002982322670000035
wherein Z isiAllocating the amount of ammunition for the ith intercepting equipment, Z0For the amount of remaining ammunition of the ith intercepting equipment at the present moment,
Figure BDA0002982322670000036
is the resource dimension constraint result.
Optionally, the weather dimension constraint of each intercepting equipment is:
Figure BDA0002982322670000037
wherein the content of the first and second substances,
Figure BDA0002982322670000038
the result is constrained for the resource dimension of the ith equipment.
Optionally, the environmental dimensional constraint between each intercepting equipment and the respective target is:
Figure BDA0002982322670000039
wherein the content of the first and second substances,
Figure BDA00029823226700000310
and (4) an occlusion constraint result of the ith intercepting equipment and the jth target.
Optionally, the interception weight factor is:
Figure RE-GDA00031404311900000310
wherein the content of the first and second substances,&representation and operation, qijRepresenting the values of the interception weight factors of the intercepting equipment i and the target j.
Optionally, the joint strike optimization model is:
Figure BDA0002982322670000041
Figure BDA0002982322670000042
wherein, mujIs the threat level, q, of target jijInterception weighting factor, e, for interception equipment i to target jijThe probability of interception of target j by intercepting equipment i.
Optionally, after the building of the joint strike optimization model according to the interception weight factor, the method further comprises:
determining feasibility of forming an interception relation between each interception device and each target according to the numerical value of the interception weight factor;
randomly selecting a target number from targets of which each intercepting equipment can form an intercepting relation as a hitting object of the intercepting equipment to form a hitting scheme XiAnd circulating the above processes to form a decision pool XP (X) consisting of N striking schemes1,X2,...,XN)。
Optionally, forming a decision pool XP ═ X (X) of N striking schemes1,X2,...,XN) Then, the method further comprises a process of performing iterative optimization on the decision pool, wherein the iterative optimization process is as follows:
respectively calculating the evaluation result value of each striking scheme in the decision pool XP according to the objective function of the combined striking optimization model, and sequencing the striking schemes in the decision pool XP according to the descending order of the evaluation result values; from the second striking scheme in the sequenced XP, each striking scheme and the next striking scheme are recombined one by one, and the recombined new schemes form a new decision pool XP';
respectively calculating the evaluation result value of each striking scheme in the decision pool XP and the decision pool XP' according to the objective function of the combined striking optimization model, sequencing in a descending order, and selecting striking schemes corresponding to the first N evaluation result values from the sequenced striking schemes to replace the striking schemes in the XP; from the second striking scheme in the replaced XP, each striking scheme and the next striking scheme are recombined one by one, and the recombined new schemes form a new decision pool XP';
replacing the decision pool XP' with the decision pool XP to perform the iteration operation again until an iteration ending condition is reached, wherein the iteration ending condition is that the iteration reaches a preset iteration number, or the first striking scheme X in the decision pool XP1No change occurred after multiple iterations.
Optionally, the recombining, from the second striking scenario in the decision pool XP, each striking scenario with a subsequent striking scenario one by one, and combining the recombined new scenarios into a new decision pool XP', includes:
striking scheme Xi' and Xi+1' in exchange for assigning all the intercepting equipment striking schemes to the target j, the striking scheme X is reconstitutedi' and Xi+1′；
Judging a striking scheme X based on the numerical value of the interception weight factori' and Xi+1Whether the requirement of feasibility of the interception relation between the interception equipment and the target is met or not is judged;
if so, combining Xi' and Xi+1'save to new decision pool XP'; if not, the target sequence number is re-randomized to carry out recombination operation;
if a new scheme meeting the feasibility requirement of the interception equipment and the target interception relation can not be generated within K times, the original scheme X is usediAnd Xi+1Directly saving into a new decision pool XP'.
Based on the technical scheme, the application can at least realize the following beneficial effects:
according to limiting conditions of an attack target and intercepting equipment in five dimensions of space, time, resources, weather and environment, equipment intercepting weight factors are introduced to restrict the fighting efficiency of the equipment, an attack scheme optimization model is constructed according to the equipment intercepting weight factors, and the feasibility of attack scheme results is improved by building a cube on the model.
In addition, a model solving method is established, so that the calculation iteration process is ensured to be always carried out in a range which accords with the constraint, and the feasibility of the striking scheme result is improved in the aspect of model solving.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart of a method for building a real-time joint percussive optimization model provided in an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Fig. 1 is a flowchart of a method for building a real-time joint percussion optimization model provided in an embodiment of the present application, where the method for building a real-time joint percussion optimization model provided in the present application may include the following steps:
step 101, acquiring a data set for establishing a combined strike optimization model;
the data set comprises weather environment data, target characteristic data, intercepting equipment data, and an occlusion relationship between the target and the intercepting equipment, wherein the intercepting equipment comprises laser equipment, radio equipment and flexible net equipment.
The ground radar device is used for detecting and acquiring basic information of an incoming low-slow small target, wherein the basic information comprises distance, speed, direction, high-low angle and the like; detecting weather environments including wind speed, wind direction, humidity and the like of the deployment position of the intercepting equipment through a weather radar; and detecting the shielding relation between each device and the target by utilizing the photoelectric equipment. Under severe environments such as strong wind, heavy rainfall, heavy fog or night, or under real-time fighting of enemies or severe electromagnetic interference conditions, the radar has poor possibility or cannot normally work, and the target information is captured and supplemented by adopting infrared detection. Meanwhile, the detection device relates to multi-platform cooperation, and a high-precision time system is used for ensuring that all the platforms realize real-time sharing of all detection data; and the advanced time keeping technology is adopted to ensure that the time synchronization of each platform is in a nanosecond level.
Data 1: weather environment data, data 2: target feature data, data 3: intercepting equipment (three types of laser equipment, radio equipment and flexible network equipment) data and data 4: and establishing a model analysis data set by the four groups of data according to the shielding relation between the target and the intercepting equipment, and numbering the target and the equipment respectively.
102, establishing space dimension constraint of each intercepting equipment according to target characteristic data and intercepting equipment data in a data set;
and establishing constraints of each intercepting equipment space dimension based on the target characteristic data and the intercepting equipment data in the model analysis data set.
Horizontal direction constraint of the intercepting equipment:
r1sinα≤H≤r2sinα (1)
wherein r is1For the lower limit of the interception range of the intercepting equipment, r2And the upper limit of the interception range of the interception equipment, alpha is the launching pitch angle of the interception equipment, and H is the distance in the vertical direction of the target of low speed and small speed.
Vertical direction constraint of the intercepting equipment:
r1cosβ≤D≤r2cosβ (2)
wherein r is1For the lower limit of the interception range of the intercepting equipment, r2And the upper limit of the interception range of the interception equipment, beta is the emission angle of the interception equipment, and D is the distance of the low-slow small target in the horizontal direction.
Based on the constraint of the equipment in two directions, the constraint conditions of the comprehensive equipment in the space dimension are as follows:
Figure BDA0002982322670000061
wherein i is the equipment number, j is the target number, d is the space dimension mark,
Figure BDA0002982322670000062
is a spatial dimension constraint result.
103, judging the relation between the flight time of the target in the prevention and control range of the intercepting equipment and the response time of the intercepting equipment according to the target characteristic data and the intercepting equipment data in the data set, and respectively establishing time dimension constraints of each intercepting equipment;
and based on the target characteristic data and the interception equipment data in the data set of the model analysis, judging the relation between the flight time of the target in the equipment prevention and control range and the equipment response time, and respectively establishing the time dimension constraints of the laser, the radio and the flexible network for intercepting the equipment.
According to the relation between the target end point position of low speed and small speed and the upper limit of the intercepting range of the equipment, calculating the passing time of the target in the intercepting range of the laser equipment:
Figure BDA0002982322670000063
wherein s is the distance between the laser equipment and the low-slow small target, rho is the distance between the laser equipment and the prevention and control central point, theta is the included angle between the connecting line of the laser equipment and the prevention and control central point in the horizontal direction, and theta is the included angle between the laser equipment and the prevention and control central point in the horizontal directionmIs the included angle between the connecting line of the low-slow small target and the prevention and control central point and the horizontal direction, and v is the detected low-slow small targetFlying speed, tjThe flight time of the target in the prevention and control range is taken as the target.
Similarly, the transit time of the low-slow small target in the interception range of the radio equipment is calculated:
Figure BDA0002982322670000071
similarly, calculating the passing time of the low-slow small target in the interception range of the flexible network equipment:
Figure BDA0002982322670000072
based on the analysis, the time dimension constraint conditions of the interception equipment are established:
Figure BDA0002982322670000073
wherein, tiFor the battle response time of the ith equipment,
Figure BDA0002982322670000074
is a time dimension constraint result.
104, establishing resource dimension constraints of each interception equipment according to the interception equipment data in the data set;
and (3) establishing the resource dimension constraints of the laser, the radio and the flexible network based on the interception equipment data in the model analysis data set.
Establishing resource constraints of the intercepting equipment as follows:
Figure BDA0002982322670000075
wherein Z isiAllocating the amount of ammunition in operation for the ith equipment, Z0For the ith equipment the amount of ammunition remaining at the present time,
Figure BDA0002982322670000076
and (4) restricting the result for the resource dimension.
105, establishing weather dimensional constraints of each intercepting equipment according to the weather environment data in the data set;
based on the weather environment data in the data set of model analysis, when wind power reaches more than 5 levels, air humidity is more than 80% or visibility resists 80%, the combat effectiveness of three kinds of intercepting equipment of laser, radio and flexible net is respectively reduced by 30%, so as to establish weather dimension constraint of each intercepting equipment:
Figure BDA0002982322670000077
wherein the content of the first and second substances,
Figure BDA0002982322670000078
the result is constrained for the resource dimension of the ith equipment.
106, establishing environmental dimension constraints between each interception equipment and each target according to the shielding relation between the targets in the data set and the interception equipment;
based on the shielding relation between targets and intercepting equipment in the model analysis data set, environment dimension constraints between the intercepting equipment and the targets are established, and the environment dimension constraints mainly comprise shielding relation factors:
Figure BDA0002982322670000081
wherein the content of the first and second substances,
Figure BDA0002982322670000082
and (4) an occlusion constraint result of the ith equipment and the jth target is obtained.
And 107, establishing an interception weight factor according to the space dimension constraint, the time dimension constraint, the resource dimension constraint, the weather dimension constraint and the environment dimension constraint, and establishing a combined strike optimization model according to the interception weight factor.
Establishing an interception weight factor of the interception equipment and a low-slow small target:
Figure RE-GDA0003140431190000083
wherein the content of the first and second substances,&representation and operation, qijRepresenting the values of the interception weight factors of the interception equipment i and the low slow small target j.
Based on the interception weight factor, establishing a combined strike relationship optimization model between a low-slow small target and the interception equipment:
Figure BDA0002982322670000084
wherein, mujThreat degree of "Low Slow Small" target j, qijInterception weighting factor, e, for interception equipment i to target jijThe probability of interception of target j by intercepting equipment i.
After the joint strike optimization model is built according to the interception weight factors, the interception relation (X) of the equipment and the target is expressed by using a matrix XijThe value is 1 or 0, which indicates that the equipment i and the target j form an interception or non-interception relationship), the row marks of the matrix indicate the equipment numbers, the column marks indicate the target numbers, each row of data in the matrix indicates the striking relationship of each equipment to all targets, and each column of data in the matrix indicates the striking relationship of each target to all equipment.
Determining feasibility (q) of forming interception relationships between each interception device and each target according to the values of the interception weight factorsij0 means that the device i cannot intercept the target j, qijNot equal to 0 indicates that the device i can perform interception on the target j); randomly selecting a target number from targets of which each intercepting equipment can form an intercepting relation as a striking object of the intercepting equipment to form a striking scheme XiAnd circulating the above processes to form a decision pool XP (X) consisting of N striking schemes1,X2,...,XN) And the following iterative process is performed:
according to combined striking excellenceRespectively calculating the evaluation result value of each striking scheme in the decision pool XP by the target function of the chemical model, and sequencing the striking schemes in the decision pool XP according to the descending order of the evaluation result values; from the second striking scheme in the sequenced XP, each striking scheme and the next striking scheme are recombined one by one, and the recombined new schemes form a new decision pool XP'; (in scheme X)iAnd Xi+1For example, randomly assigning the target serial number j, exchanging all equipment striking schemes allocated to the target j in the two schemes, and reconstructing the scheme Xi' and Xi+1', based on the value of the interception weight factor, X is judgedi' and Xi+1' whether the requirement of feasibility of equipment and target interception relation is met. If the answer is yes, storing the two groups of new schemes into a new decision pool XP'; if not, the target sequence number is re-randomized to carry out recombination operation, and if no new scheme can be generated within K times, the original scheme X is replacediAnd Xi+1And directly saving the decision into a new decision pool. )
Respectively calculating the evaluation result value of each percussion scheme in the decision pool XP and the decision pool XP' according to the objective function of the combined percussion optimization model, sequencing in a descending order, and selecting the percussion schemes corresponding to the first N evaluation result values from the sequenced percussion schemes to replace the percussion schemes in the XP; from the second striking scheme in the replaced XP, each striking scheme and the next striking scheme are recombined one by one, and the recombined new schemes form a new decision pool XP'; (in scheme X)iFor example, a target number j is randomly generated, and all the equipment striking schemes for the target in the scheme are updated to a new striking scheme Xi". Based on the value of the interception weight factor, X is judgediAnd whether the equipment and target interception relation feasibility requirement is met. If the answer is yes, the new scheme is stored in a new decision pool XP'; if not, the target sequence number is updated again randomly, and if the new scheme can not be generated within K times, the original scheme X is updatediDirectly saving into a new decision pool XP ". )
Replacing the decision pool XP' with the decision pool XP to carry out the iteration operation again until an iteration end condition is reached, and ending the iterationThe condition is that the iteration reaches a preset iteration number, or the first striking scheme X in a decision pool XP1After multiple iterations, X is not changed1As the final striking scheme.
Subsequently, the combined striking optimization model needs to be updated to specify the combined striking scheme in real time.
And updating data in the model analysis data set based on the target striking scheme equipped at the previous moment and the detection data at the next moment, and calculating the striking scheme at the next moment according to the process to realize the real-time combined striking of the low-slow small target.
In summary, according to the method for establishing the real-time combined strike optimization model, the equipment interception weight factor is introduced to constrain the fighting efficiency of the equipment according to the limiting conditions of the attack target and the interception equipment in five dimensions of space, time, resources, weather and environment, and the strike scheme optimization model is established, so that the feasibility of the strike scheme result is improved in the aspect of model establishment.
In addition, a model solving method is established, so that the calculation iteration process is ensured to be always carried out in a range which accords with the constraint, and the feasibility of the striking scheme result is improved in the aspect of model solving.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (11)

1. A method for establishing a real-time combined strike optimization model is characterized by comprising the following steps:
acquiring a data set for establishing a combined strike optimization model, wherein the data set comprises weather environment data, target characteristic data, interception equipment data and an occlusion relation between a target and interception equipment, and the interception equipment comprises laser equipment, radio equipment and flexible network equipment;
establishing space dimension constraint of each intercepting equipment according to the target characteristic data in the data set and the intercepting equipment data;
according to the target characteristic data and the interception equipment data in the data set, judging the relation between the flight time of the target in the prevention and control range of the interception equipment and the response time of the interception equipment, and respectively establishing time dimension constraints of each interception equipment;
establishing resource dimension constraints of each interception equipment according to the interception equipment data in the data set;
establishing weather dimension constraints of each intercepting equipment according to the weather environment data in the data set;
according to the shielding relation between the targets and the intercepting equipment in the data set, environment dimension constraints between each intercepting equipment and each target are established;
establishing an interception weight factor according to the space dimension constraint, the time dimension constraint, the resource dimension constraint, the weather dimension constraint and the environment dimension constraint, and establishing a combined strike optimization model according to the interception weight factor.
2. The method of claim 1, wherein the spatial dimension constraints of each intercepting equipment comprise horizontal direction constraints of the intercepting equipment, vertical direction constraints of the intercepting equipment, wherein:
the horizontal direction constraint of the intercepting equipment is as follows: r is1sinα≤H≤r2sin α, wherein r1For the lower limit of the interception range of the intercepting equipment, r2The upper limit of the interception range of the interception equipment, alpha is the interception equipmentTransmitting a pitch angle, wherein H is the distance in the vertical direction of the target;
the vertical direction constraint of the intercepting equipment is as follows: r is1cosβ≤D≤r2cos beta, wherein, r1For the lower limit of the interception range of the intercepting equipment, r2The upper limit of the interception range of the interception equipment, beta is the emission angle of the interception equipment, and D is the distance of the low-slow small target in the horizontal direction;
the spatial dimension constraint of the intercepting equipment is as follows:
Figure FDA0002982322660000011
wherein i is the equipment number, j is the target number, d is the space dimension mark,
Figure FDA0002982322660000012
is a spatial dimension constraint result.
3. The method of claim 2, wherein the time dimension constraint of each intercepting equipment is:
Figure FDA0002982322660000013
wherein, tiFor the battle response time of the ith equipment,
Figure FDA0002982322660000014
is a time dimension constraint result;
the passing time of the target in the interception range of the laser equipment is as follows:
Figure FDA0002982322660000021
the transit time of the target within the radio equipment interception range is:
Figure FDA0002982322660000022
the passing time of the target in the interception range of the flexible net equipment is as follows:
Figure FDA0002982322660000023
wherein s is the distance between the laser equipment and the target, rho is the distance between the laser equipment and the prevention and control central point, theta is the included angle of the connecting line of the laser equipment and the prevention and control central point in the horizontal direction, and theta is the included angle of the connecting line of the laser equipment and the prevention and control central point in the horizontal directionmIs the angle between the connecting line of the target and the prevention and control central point and the horizontal direction, v is the detected target flight speed, tjThe flight time of the target in the prevention and control range is taken as the target.
4. The method of claim 3, wherein the resource dimension constraint of each intercepting equipment is:
Figure FDA0002982322660000024
wherein Z isiAllocating the amount of ammunition for the ith intercepting equipment, Z0For the amount of remaining ammunition of the ith intercepting equipment at the present moment,
Figure FDA0002982322660000025
is the resource dimension constraint result.
5. The method of claim 4, wherein the weather dimensional constraint of each piece of intercepting equipment is:
Figure FDA0002982322660000026
wherein the content of the first and second substances,
Figure FDA0002982322660000027
the result is constrained for the resource dimension of the ith equipment.
6. The method of claim 5, wherein the environmental dimensional constraint between each intercepting equipment and the respective target is:
Figure FDA0002982322660000028
wherein the content of the first and second substances,
Figure FDA0002982322660000029
and (4) an occlusion constraint result of the ith intercepting equipment and the jth target.
7. The method of claim 6, wherein the interception weight factor is:
Figure RE-FDA00031404311800000110
wherein the content of the first and second substances,&representation and operation, qijRepresenting the values of the interception weight factors of the intercepting equipment i and the target j.
8. The method of claim 7, wherein the joint percussive optimization model is:
Figure FDA0002982322660000031
Figure FDA0002982322660000032
wherein, mujIs the threat level, q, of target jijInterception weighting factor of interception equipment i to target jSub, eijThe probability of interception of target j by intercepting equipment i.
9. The method of claim 8, wherein after said building a joint strike optimization model according to said interception weight factors, said method further comprises:
determining feasibility of forming an interception relation between each interception device and each target according to the numerical value of the interception weight factor;
randomly selecting a target number from targets of which each intercepting equipment can form an intercepting relation as a striking object of the intercepting equipment to form a striking scheme XiAnd circulating the above processes to form a decision pool XP (X) consisting of N striking schemes1,X2,...,XN)。
10. The method of claim 9, wherein the decision pool of N striking scenarios is formed by XP ═ X (X)1,X2,...,XN) Then, the method further comprises a process of performing iterative optimization on the decision pool, wherein the iterative optimization process is as follows:
respectively calculating the evaluation result value of each striking scheme in the decision pool XP according to the objective function of the combined striking optimization model, and sequencing the striking schemes in the decision pool XP according to the descending order of the evaluation result values; from the second striking scheme in the sequenced XP, each striking scheme and the next striking scheme are recombined one by one, and the recombined new schemes form a new decision pool XP';
respectively calculating the evaluation result value of each percussion scheme in the decision pool XP and the decision pool XP' according to the objective function of the combined percussion optimization model, sequencing in a descending order, and selecting the percussion schemes corresponding to the first N evaluation result values from the sequenced percussion schemes to replace the percussion schemes in the XP; from the second striking scheme in the replaced XP, each striking scheme and the next striking scheme are recombined one by one, and the recombined new schemes form a new decision pool XP';
replace the decision pool XP ″The pool XP performs the above iteration operation again until an iteration end condition is reached, wherein the iteration end condition is that iteration reaches a preset iteration number, or the first striking scheme X in the pool XP is decided1No change occurred after multiple iterations.
11. The method of claim 10, wherein said recombining each striking scheme one by one with a subsequent striking scheme from the second striking scheme in said decision pool XP, and combining the recombined new schemes into a new decision pool XP' comprises:
striking scheme Xi' and Xi+1' in exchange for assigning all the intercepting equipment striking schemes to the target j, the striking scheme X is reconstitutedi' and Xi+1′；
Judging a striking scheme X based on the numerical value of the interception weight factori' and Xi+1Whether the requirement of feasibility of the interception relation between the interception equipment and the target is met or not is judged;
if so, combining Xi' and Xi+1'save to new decision pool XP'; if not, the target sequence number is re-randomized to carry out recombination operation;
if a new scheme meeting the feasibility requirement of the interception equipment and the target interception relation can not be generated within K times, the original scheme X is usediAnd Xi+1Directly saving into a new decision pool XP'.
CN202110290487.1A 2021-03-18 2021-03-18 Method for establishing real-time combined strike optimization model Pending CN113221235A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110290487.1A CN113221235A (en) 2021-03-18 2021-03-18 Method for establishing real-time combined strike optimization model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110290487.1A CN113221235A (en) 2021-03-18 2021-03-18 Method for establishing real-time combined strike optimization model

Publications (1)

Publication Number Publication Date
CN113221235A true CN113221235A (en) 2021-08-06

Family

ID=77083787

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110290487.1A Pending CN113221235A (en) 2021-03-18 2021-03-18 Method for establishing real-time combined strike optimization model

Country Status (1)

Country Link
CN (1) CN113221235A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6134500A (en) * 1999-06-03 2000-10-17 United Air Lines, Inc. System and method for generating optimal flight plans for airline operations control
WO2012068877A1 (en) * 2010-11-22 2012-05-31 北京机械设备研究所 Low-altitude low-speed small target intercepting method based on firing table fitting
KR20140112621A (en) * 2013-03-12 2014-09-24 국방과학연구소 Apparatus for intercepting high-speed air threats for simulation analysis and Method thereof
IL261220A (en) * 2017-08-25 2019-01-31 Aurora Flight Sciences Corp Aerial vehicle interception system
CN110986680A (en) * 2019-10-12 2020-04-10 中国人民解放军国防科技大学 Composite interception method for low-speed small targets in urban environment

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6134500A (en) * 1999-06-03 2000-10-17 United Air Lines, Inc. System and method for generating optimal flight plans for airline operations control
WO2012068877A1 (en) * 2010-11-22 2012-05-31 北京机械设备研究所 Low-altitude low-speed small target intercepting method based on firing table fitting
KR20140112621A (en) * 2013-03-12 2014-09-24 국방과학연구소 Apparatus for intercepting high-speed air threats for simulation analysis and Method thereof
IL261220A (en) * 2017-08-25 2019-01-31 Aurora Flight Sciences Corp Aerial vehicle interception system
CN110986680A (en) * 2019-10-12 2020-04-10 中国人民解放军国防科技大学 Composite interception method for low-speed small targets in urban environment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
方浩;王雪源;陈杰;: "陆用运动体多系统协同中的智能优化与控制", 控制理论与应用, no. 07 *

Similar Documents

Publication Publication Date Title
CN108646589B (en) Combat simulation training system and method for attacking unmanned aerial vehicle formation
CN112880688B (en) Unmanned aerial vehicle three-dimensional track planning method based on chaotic self-adaptive sparrow search algorithm
CN106203870A (en) A kind of complex analysis towards combined operation and weapon allocation method
CN108318032A (en) A kind of unmanned aerial vehicle flight path Intelligent planning method considering Attack Defence
CN106779210A (en) Algorithm of Firepower Allocation based on ant group algorithm
CN110986680B (en) Composite interception method for low-speed small targets in urban environment
CN107832939A (en) Unmanned platform aerial opposition deduction method and device
CN116661496B (en) Multi-patrol-missile collaborative track planning method based on intelligent algorithm
Chen et al. Auction algorithm approaches for dynamic weapon target assignment problem
CN113221235A (en) Method for establishing real-time combined strike optimization model
CN116384436A (en) Unmanned aerial vehicle 'bee colony' countermeasure method
CN110119160B (en) Quick real-time dynamic task planning method for scouting and printing integrated unmanned aerial vehicle
CN114548674B (en) Multi-agent confrontation scene oriented threat situation assessment method, device and equipment
CN111612673A (en) Method and system for confirming threat degree of unmanned aerial vehicle to multiple grounds
CN108734334B (en) Bullet and cannon combined firepower distribution method based on D number and threat priority
CN106996789B (en) Multi-airborne radar cooperative detection airway planning method
CN116538865A (en) Unmanned aerial vehicle system
CN110825105A (en) Satellite film pattern spot inspection method and device based on unmanned aerial vehicle
Fu et al. Air combat assignment problem based on bayesian optimization algorithm
Zhang et al. Improvement of UAV track trajectory algorithm based on ant colony algorithm
CN111581878B (en) Open ground anti-unmanned aerial vehicle firepower deployment method based on improved PSO algorithm
CN114756049A (en) Target allocation method, system, equipment and medium in unmanned aerial vehicle cluster countermeasure
CN114330862A (en) Air defense weapon combat deployment algorithm
RU2684963C1 (en) Method for automatic group target allocation of fighters based on eventual elimination of participants
CN114047761A (en) Elastic killer network construction method and device based on formation cross-platform resource scheduling

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination