Multi-target killing chain optimization method and device based on combinatorial optimization

Abstract

The invention discloses a multi-target killing chain optimization method and device based on combinatorial optimization, and the method comprises the steps: constructing a combat network according to the interaction relation between combat units, and determining an edge attribute as a combat index according to a network node parameter; according to the number of targets attacked in the current combat scene and the number of combat indexes required for optimization, whether a static killing chain or a dynamic killing chain is generated is judged; if the number of the attacking targets in the current combat scene is single, carrying out monocular single-index killing chain optimization, or carrying out monocular multi-index killing chain optimization to generate a static killing chain; and if the number of the combat targets in the current combat scene is multiple and the number of the combat indexes is multiple, performing multi-view and multi-index killing chain optimization by combining and optimizing different killing chain paths, and generating a dynamic killing chain. According to the invention, a commanding mechanism can be assisted to select a static killing chain or a dynamic killing chain which most meets requirements from a plurality of killing chains based on the four combat indexes.

Description

Technical Field

[0001] The present invention relates to the field of firepower allocation, and in particular to a multi-target kill chain optimization method, device, electronic device and computer-readable medium based on combination optimization. Background Art

[0002] Kill web is a kind of agile and efficient strike against targets in complex and changing environments by using multiple platforms and weapon systems through information sharing and collaborative control. The core of kill web is to quickly build a kill chain, which is a closed-loop process from detecting targets, making mission decisions, striking targets, and evaluating engagement results. The optimal kill link selection problem in kill web refers to how to select one or more kill links from the kill web that are most suitable for completing the strike mission under given combat environments and mission conditions.

[0003] In the existing technology, when modeling a kill chain-based network, most can only calculate the number of kill chains and provide a fixed kill chain solution. It cannot assist the command organization in selecting the most suitable combat link from all kill chains, nor can it adapt to the needs of a dynamic combat environment. Summary of the invention In view of this, the present invention provides a multi-target kill chain optimization method, device, electronic device and computer-readable medium based on combinatorial optimization, in order to at least partially solve at least one of the above-mentioned technical problems.

[0004] In order to solve the above technical problems, the first aspect of the present invention proposes a multi-target kill chain optimization method based on combinatorial optimization, the method comprising: Taking combat units as nodes, a combat network is constructed based on the interaction between combat units, and edge attributes are determined as combat indicators based on network node parameters. Determine whether to generate a static kill chain or a dynamic kill chain based on the number of targets to be attacked in the current combat scenario and the number of combat indicators required for optimization; If the current combat scenario has only one target, a single-eye single-index kill chain optimization is performed, or a single-eye multi-index kill chain optimization is performed to generate a static kill chain for each combat unit; If the current combat scenario has multiple targets and multiple combat indicators, a multi-target and multi-indicator kill chain is optimized by combining and optimizing different kill chain paths to generate a dynamic kill chain.

[0005] According to a preferred embodiment of the present invention, if the number of targets to be attacked in the current combat scenario is single and the number of combat indicators required for optimization is single, a single-eye single-indicator kill chain is preferably generated to generate a static kill chain, further comprising: Select a starting point, store the starting point in a first array, and store all vertices except the starting point and the distances from each vertex to the starting point in a second array, wherein: the first array is used to store vertices for which the shortest path has been found and the corresponding shortest path lengths, and the second array is used to store vertices for which the shortest path has not yet been found and the distances from the vertex to the starting point; Loop through the vertex removal step until all vertices in the second array are traversed; The loop performs the vertex removal step including: Find the target vertex with the shortest distance to the starting point from the second array, and add the target vertex to the first array. At the same time, remove the target vertex from the second array and update the distance from each vertex in the second array to the starting point.

[0006] According to a preferred embodiment of the present invention, the step of performing multi-target and multi-indicator kill chain optimization by combining and optimizing different kill chain paths to generate a dynamic kill chain includes: Mapping the attack nodes and the attack targets into a weighted bipartite graph; Finding the best complete match between the strike node and the strike target in the weighted bipartite graph; Generates a static kill chain based on the best complete match.

[0007] According to a preferred embodiment of the present invention, mapping the attack nodes and the attack targets into a weighted bipartite graph includes: Assume that the weighted graph is , where all vertices can be divided into attack node subsets and the target node subset ,satisfy Do not intersect, Take any edge<i,j> , which is the weighted bipartite graph obtained by mapping the attack nodes and the attack targets.

[0008] According to a preferred embodiment of the present invention, the step of searching for the best complete match between the attack node and the attack target in the weighted bipartite graph includes: Using an adjacency matrix to store the weighted bipartite graph; Initialize the vertex coordinates and use the Hungarian algorithm to find the best complete match. If the best complete match is not found, update the vertex coordinate values ​​and use the Hungarian algorithm to find the best complete match until the best complete match is found. Where: Let M be a complete matching of a weighted bipartite graph, give each point a top label, and let the top label of the attack node be , the top label of the target node is , if all edges<i,j> ∈G, both , It is the edge<i,j> The weight of<i,j> ∈M, both , then M is an optimal complete matching.

[0009] According to a preferred embodiment of the present invention, if the number of strike targets in the current combat scenario is single and the number of combat indicators is multiple, a single-eye multi-indicator kill chain is preferably generated to generate a static kill chain, further comprising: The following formula is used to convert multiple operational indicators into a single overall indicator: ; in, , is the indicator weighting coefficient, is the closing time of the current kill chain, is the minimum value of all kill chain closure times, is the maximum closing time of all kill chains, The damage efficiency of the current kill chain, is the minimum value of the effectiveness of all kill chains, is the maximum damage efficiency in all kill chains, For the reliability of the current kill chain, is the minimum reliability of all kill chains, is the maximum reliability value in all kill chains, The cost performance of the current kill chain, is the minimum value of all kill chain cost effectiveness, is the maximum cost-effectiveness of all kill chains, and A is a single total indicator; A single-eye single-indicator kill chain is preferably used to generate a static kill chain based on the single overall indicator.

[0010] According to a preferred embodiment of the present invention, the combat indicators include: Closing time: ; in, is the consumption time of the node or edge, such as the decision time of the command node. If there is information transmission time on the edge, it is also added in sequence; Damage effectiveness: ; in, Represents the weapon coefficient, Represents heavy weapons, value 4, Represents light weapons, with a value of 1. The basic damage capability is inversely proportional to the target's resistance to destruction, and is taken as 0.25, that is, the damage of light weapons is 0.25*1=0.25, and that of heavy weapons is 0.25*4=1 (complete destruction). For strike accuracy, weapon nodes have this attribute. Represents positioning accuracy. The detection node has this attribute.

[0011] reliability: ; in, Represents the information reliability of different nodes, mainly including detection nodes, which are detection intelligence information sensors and information transmission reliability. For communication nodes, it is a value related to the bit error rate. Cost performance: ; in, is the target value, To damage performance, is the ammunition consumption, n is the single-shot weapon cost, is the fuel consumption, m is the fuel unit price, Other cost consumption.

[0012] In order to solve the above technical problems, the second aspect of the present invention provides a multi-target kill chain optimization device based on combination optimization, the device comprising: A construction module is used to construct a combat network based on the interaction relationship between combat units and combat units by taking combat units as nodes, and to determine edge attributes as combat indicators based on network node parameters; A judgment module is used to determine whether to generate a static kill chain or a dynamic kill chain based on the number of targets to be attacked in the current combat scenario and the number of combat indicators required for optimization; The first generation module is used to perform a single-eye single-index kill chain optimization if the number of targets to be attacked in the current combat scenario is single, or to perform a single-eye multi-index kill chain optimization to generate a static kill chain for each combat unit; The second generation module is used to generate a dynamic kill chain by optimizing different kill chain paths to select a multi-target and multi-indicator kill chain if there are multiple targets and multiple combat indicators in the current combat scenario.

[0013] In order to solve the above technical problem, the third aspect of the present invention provides an electronic device, including: A processor; and a memory storing computer executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0014] In order to solve the above technical problem, the fourth aspect of the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the above method is implemented.

[0015] The present invention constructs a combat network according to the interactive relationship between combat units, and determines the edge attributes as combat indicators according to the network node parameters to form a comprehensive optimization index of the kill chain. According to the number of targets attacked in the current combat scenario and the number of combat indicators required for optimization, a single-target single-indicator kill chain optimization and a single-target multi-indicator kill chain optimization can be performed to generate a static kill chain for each combat unit. At the same time, a multi-target multi-indicator kill chain optimization can also be performed by combining and optimizing different kill chain paths to generate a dynamic kill chain. Further, a single kill chain is used as a combat component, horizontally and vertically integrated between the same and different combat elements, and a collaborative combat path is quickly formed to achieve its own goals, and finally a kill network is formed, and the combat path is dynamically planned to form the final optimized path according to the existing combat environment and combat unit allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects achieved more clearly, the specific embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, it should be noted that the drawings described below are only drawings of exemplary embodiments of the present invention, and those skilled in the art can obtain drawings of other embodiments based on these drawings without creative work.

[0017] Figure 1 It is a flow chart of a multi-target kill chain optimization method based on combination optimization of the present invention; Figure 2 1. It is a schematic diagram of a process for optimizing a kill chain for a single camera and multiple indicators according to an embodiment of the present invention; Figure 3 It is a schematic diagram of a process of searching for the best complete match between a strike node and a strike target in a weighted bipartite graph according to an embodiment of the present invention; Figure 4 It is a schematic diagram of the structural framework of a multi-target kill chain optimization device based on combination optimization of the present invention; Figure 5 is a structural block diagram of an exemplary embodiment of an electronic device according to the present invention; Figure 6 is a schematic diagram of an embodiment of a computer readable medium of the present invention. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. Although each exemplary embodiment can be implemented in a variety of specific ways, it should not be understood that the present invention is limited to the embodiments set forth herein. On the contrary, these exemplary embodiments are provided to make the content of the present invention more complete and to facilitate the comprehensive communication of the inventive concept to those skilled in the art.

[0019] Under the premise of conforming to the technical concept of the present invention, the structure, performance, effect or other characteristics described in a specific embodiment may be combined with one or more other embodiments in any appropriate manner.

[0020] In the process of introducing specific embodiments, the detailed description of the structure, performance, effect or other characteristics is to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can implement the present invention with a technical solution that does not contain the above-mentioned structure, performance, effect or other characteristics under certain circumstances.

[0021] The flowchart in the accompanying drawings is only an exemplary process demonstration, and does not mean that the solution of the present invention must include all the contents, operations and steps in the flowchart, nor does it mean that it must be executed in the order shown in the figure. For example, some operations / steps in the flowchart can be decomposed, some operations / steps can be combined or partially combined, etc. The execution order shown in the flowchart can be changed according to actual conditions without departing from the main purpose of the present invention.

[0022] Frames in the attached figure Figure 1 Generally, it refers to functional entities, which do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0023] The same reference numerals in the drawings represent the same or similar elements, components or parts, and thus the repeated description of the same or similar elements, components or parts may be omitted below. It should also be understood that although the first, second, third and other attributives representing the numbers may be used herein to describe various devices, elements, components or parts, these devices, elements, components or parts should not be limited by these attributives. In other words, these attributives are only used to distinguish one from another. For example, the first device may also be called the second device, but it does not deviate from the essential technical solution of the present invention. In addition, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0024] Figure 1 This is a flowchart of a multi-target kill chain optimization method based on combined optimization provided by the present invention; this method can assist the command organization in selecting the most suitable combat link from a large number of kill chains based on the four combat indicators of "closing time, damage effectiveness, reliability, and cost effectiveness" in distributed maritime operations by comprehensively considering the characteristics of each combat unit, the requirements of combat missions, and the dynamic changes of the battlefield environment, which is conducive to further carrying out equipment system effectiveness analysis and optimization related work. Figure 1As shown, the method includes: S1. Taking combat units as nodes, a combat network is constructed based on the interaction between combat units, and edge attributes are determined as combat indicators based on network node parameters; Among them: the combat unit can be equipment, then the equipment information can be represented by nodes, and the interaction relationship between the equipment can be represented by edges, then: A node can be represented as: The edge between node i and node j can be expressed as: Among them, Ep represents performance parameters, such as coverage, information acquisition efficiency, strike accuracy, etc., and Sp represents spatiotemporal information, such as longitude and latitude. Indicates the node type of the corresponding edge out point, It represents the entry point type of the corresponding edge. T, E, S and C represent the attributes of the corresponding edge respectively, where T is the closing time, E is the damage efficiency, S is the reliability and C is the cost efficiency.

[0025] Furthermore, the attributes of the edge (i.e., the attributes of the basic unit of the kill chain) can be calculated according to the node parameters as combat indicators. In this embodiment, the edge attributes can include: closing time T, damage efficiency E, reliability S, and cost efficiency C.

[0026] Among them: Closure time T refers to the time interval from the launch of the attack by the combat system to the successful destruction or termination of the target, which can be expressed as: in, is the consumption time of the node or edge, such as the decision time of the command node. If there is information transmission time on the edge, it is added in sequence.

[0027] The damage effectiveness E refers to the actual degree and effect of damage caused by the weapon system to the target. It reflects the degree of damage and killing effect that the weapon system can cause when attacking the target; it can be expressed as: in, Represents the weapon coefficient, Represents heavy weapons, value 4, Represents light weapons, with a value of 1. The basic damage capability is inversely proportional to the target's resistance to destruction, and is taken as 0.25, that is, the damage of light weapons is 0.25*1=0.25, and that of heavy weapons is 0.25*4=1 (complete destruction). For strike accuracy, weapon nodes have this attribute. Represents positioning accuracy. The detection node has this attribute.

[0028] Reliability S refers to the comprehensive evaluation of the reliability of each link in the kill chain (such as reconnaissance, communication conditions, weapons and equipment, hit accuracy, etc.); it can be expressed as: in, Represents the information reliability of different nodes, mainly including the detection node, which is the detection intelligence information sensor and information transmission reliability. For the communication node, it is a value related to the bit error rate.

[0029] Cost effectiveness C refers to the relationship between the resources and costs invested and the effects produced when implementing a killing action. It is an indicator to measure the economy and efficiency of combat operations. It can be expressed as: in, is the target value, To damage performance, is the ammunition consumption, n is the single-shot weapon cost, is the fuel consumption, m is the fuel unit price, Other cost consumption.

[0030] S2. Determine whether to generate a static kill chain or a dynamic kill chain based on the number of targets to be attacked in the current combat scenario and the number of combat indicators required for optimization; Among them: the number of targets to be struck and the number of combat indicators required for optimization are determined according to the combat scenario.

[0031] S3. If the number of targets to be attacked in the current combat scenario is only one, a single-eye single-index kill chain optimization is performed, or a single-eye multi-index kill chain optimization is performed to generate a static kill chain for each combat unit; In one example, if the current combat scenario has only one target and only one combat indicator is needed for optimization, a single-target single-indicator kill chain optimization is performed to generate a static kill chain. Among them: the single-target single-indicator kill chain optimization can be transformed into a shortest path problem for a certain layer or multiple layers of the complex network system model, expanding outward layer by layer (breadth-first traversal idea) with the starting point as the center until it reaches the end point. Specifically, it may include: S31. Select a starting point, store the starting point in a first array, and store all vertices except the starting point and the distance from each vertex to the starting point in a second array, wherein: the first array is used to store vertices for which the shortest path has been calculated and the corresponding shortest path lengths, and the second array is used to store vertices for which the shortest path has not yet been calculated and the distance from the vertex to the starting point; if a vertex is not adjacent to the starting point, the distance between the starting point and the vertex is infinite.

[0032] S32, looping and executing the vertex removal step until all vertices in the second array are traversed; Wherein: the step of cyclically executing vertex removal comprises: Find the target vertex with the shortest distance to the starting point from the second array, where the distance is a defined indicator, such as the shortest closing time of the kill chain or the comprehensive indicator A.

[0033] The target vertex is added to the first array, and at the same time, the target vertex is removed from the second array, and the distance from each vertex in the second array to the starting point is updated.

[0034] After traversing all vertices in the second array, move to all vertices in the first array to generate a static kill chain for each combat unit; For details, please refer to Figure 2 , at the beginning, read the combat network data, set the source point (starting point) s, the first array ps = empty and the second array ds = 0, and set the counting unit Num = 1 to record the number of traversals, N is the total number of vertices in the combat data. Determine whether the current value of Num is less than N. If it is less than, execute the vertex removal step. If it is not less than, it means that all vertices have been traversed and the process ends. Among them: executing the vertex removal step may include: checking the distance from all marked points K to unmarked points j in the second array, and taking dj = min [dj, dk + w (k, j)]; selecting the point i with the smallest dj from the unmarked points; finding the point directly connected to i from the unmarked points, recorded as pi; marking point i, Num++.

[0035] In another example, if the number of strike targets in the current combat scenario is single and the number of combat indicators is multiple, a single-eye multi-indicator kill chain is performed to preferentially generate a static kill chain, which may include: S301. Convert multiple combat indicators into a single overall indicator using the following formula: ; S302: Perform a single-target single-indicator kill chain optimization to generate a static kill chain according to the single overall indicator.

[0036] For details, please refer to steps S31-32, and replace the single indicator in steps S31-S32 with a single total indicator.

[0037] S4. If the current combat scenario has multiple targets and multiple combat indicators, a multi-target and multi-indicator kill chain is optimized by combining and optimizing different kill chain paths to generate a dynamic kill chain.

[0038] Exemplarily, the step of performing multi-target and multi-indicator kill chain optimization by combining and optimizing different kill chain paths to generate a dynamic kill chain may include: S41, mapping the attack nodes and the attack targets into a weighted bipartite graph; Assume that the weighted graph is , where all vertices can be divided into attack node subsets and the target node subset ,satisfy Do not intersect, Take any edge<i,j> , which is the weighted bipartite graph obtained by mapping the attack nodes and the attack targets.

[0039] S42, searching for the best complete match between the strike node and the strike target in the weighted bipartite graph; Exemplarily, this step may include: S421, storing the weighted bipartite graph using an adjacency matrix; Use the adjacency matrix to store the weighted bipartite graph, such as ,like Indicates that the two nodes are not connected.

[0040] S422, initializing the vertex coordinates, and using the Hungarian algorithm to find the best complete match. If the best complete match is not found, the value of the vertex coordinates is updated, and the Hungarian algorithm is used to find the best complete match until the best complete match is found. Initialize vertex coordinates, , Use the Hungarian algorithm to find the best complete match. If the best complete match is not found, update the value of the vertex coordinates and use the Hungarian algorithm to find the best complete match until the best complete match is found. Where: Let M be a complete matching of a weighted bipartite graph, give each point a top label, let the top label of the attack node be, the top label of the target node be, if all the edges<i,j> ∈G, all have, are edges<i,j> The weight of<i,j> ∈M, if all of them exist, then M is an optimal complete matching.

[0041] like Figure 3 As shown, the Hungarian algorithm can be used to find the best complete match by reading the adjacency matrix at the beginning and performing an empty match. An empty match means that no two vertices are matched together in the initial state. It can be understood that all vertices are in an unmatched state. This is the initial situation at the beginning of the Hungarian algorithm, which provides a starting point for the subsequent step-by-step search for the best match. Find an augmentable path from each vertex in turn, and determine whether all vertices have been traversed. If so, end the matching and take the matching result that meets the above conditions as the best match. If not, determine whether there is an augmenting path. If there is an augmenting path, after updating the number of matches, return to execute the step of finding an augmentable path from each vertex in a part in turn. If there is no augmenting path, directly return to execute the step of finding an augmentable path from each vertex in a part in turn until a matching result that meets the above conditions is found as the best match.

[0042] S43. Generate a static kill chain based on the best complete match.

[0043] For details, please refer to steps S31-32, and replace the single indicator in steps S31-S32 with the best complete match.

[0044] Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention is driven by combat missions and combines the characteristics of combat equipment to propose four combat indicators (closing time, damage effectiveness, reliability, and cost effectiveness) as kill chain optimization indicators. By modeling combat units, a comprehensive optimization indicator of the kill chain is formed; 2. A dynamic optimization technology for the kill chain path of the combat system is proposed, which can be used to optimize the kill chain of a single target and a single indicator, as well as the kill chain of multiple targets and multiple indicators.

[0045] 3. In combat scenarios, combined with existing positions and combat environments, static kill chains are formed for each combat unit according to combat missions. Combination optimization is used to optimize different kill chain paths to form a dynamic kill network. A single kill chain is used as a combat component to integrate horizontally and vertically between the same and different combat elements to quickly form a coordinated combat path, complete its own goals, and ultimately form a kill network. The combat path is dynamically planned to form the final optimized path based on the existing combat environment and combat unit allocation.

[0046] Based on the above multi-target kill chain optimization method based on combination optimization, the present invention also provides a multi-target kill chain optimization device based on combination optimization. Figure 4 Schematic diagram of the structural framework of a multi-target kill chain optimization device based on combination optimization of the present invention. Figure 4 As shown, the device comprises: A construction module 41 is used to construct a combat network based on the interaction relationship between the combat units and the combat units by taking the combat units as nodes, and to determine the edge attributes as combat indicators based on the network node parameters; A judgment module 42 is used to judge whether to generate a static kill chain or a dynamic kill chain according to the number of targets to be attacked in the current combat scenario and the number of combat indicators required for optimization; The first generating module 43 is used to perform a single-eye single-index kill chain optimization if the number of strike targets in the current combat scenario is single, or perform a single-eye multi-index kill chain optimization to generate a static kill chain for each combat unit; The second generation module 44 is used to generate a dynamic kill chain by optimizing different kill chain paths to select a kill chain with multiple targets and multiple indicators if there are multiple targets and multiple combat indicators in the current combat scenario.

[0047] In a specific implementation, if the number of strike targets in the current combat scenario is single and the number of combat indicators required for optimization is single, the first generation module 43 includes: A starting unit, used for selecting a starting point, storing the starting point in a first array, and storing all vertices except the starting point and the distances from each vertex to the starting point in a second array, wherein: the first array is used for storing vertices for which the shortest path has been obtained and the corresponding shortest path lengths, and the second array is used for storing vertices for which the shortest path has not yet been obtained and the distances from the vertex to the starting point; A loop unit, used for looping and executing the vertex removal step until all vertices in the second array are traversed; The loop performs the vertex removal step including: Find the target vertex with the shortest distance to the starting point from the second array, and add the target vertex to the first array. At the same time, remove the target vertex from the second array and update the distance from each vertex in the second array to the starting point.

[0048] The second generating module 44 comprises: A mapping unit, used for mapping the strike nodes and strike targets into a weighted bipartite graph; A matching unit, used for finding the best complete match between the attack node and the attack target in the weighted bipartite graph; A sub-generation unit is used to generate a static kill chain based on the best complete match.

[0049] Optionally, the mapping unit assumes that the weighted graph is , where all vertices can be divided into attack node subsets and the target node subset ,satisfy Do not intersect, Take any edge<i,j> , which is the weighted bipartite graph obtained by mapping the attack nodes and the attack targets.

[0050] The matching unit comprises: A storage subunit, used for storing the weighted bipartite graph using an adjacency matrix; The sub-matching unit is used to initialize the vertex coordinates and use the Hungarian algorithm to find the best complete match. If the best complete match is not found, the value of the vertex coordinates is updated and the Hungarian algorithm is used to find the best complete match until the best complete match is found. Where: Let M be a complete matching of a weighted bipartite graph, give each point a top label, and let the top label of the attack node be , the top label of the target node is , if all edges<i,j> ∈G, both , It is the edge<i,j> The weight of<i,j> ∈M, both , then M is an optimal complete matching.

[0051] In a specific implementation, if the number of strike targets in the current combat scenario is single and the number of combat indicators is multiple, the first generating module 43 includes: The conversion subunit is used to convert multiple combat indicators into a single overall indicator using the following formula: ; The first sub-generation unit is used to perform a single-eye single-indicator kill chain optimization to generate a static kill chain according to the single total indicator.

[0052] In this embodiment, the combat indicators include: Closing time: ; in, is the consumption time of the node or edge, such as the decision time of the command node. If there is information transmission time on the edge, it is also added in sequence; Damage effectiveness: ; in, Represents the weapon coefficient, Represents heavy weapons, value 4, Represents light weapons, with a value of 1. The basic damage capability is inversely proportional to the target's resistance to destruction, and is taken as 0.25, that is, the damage of light weapons is 0.25*1=0.25, and that of heavy weapons is 0.25*4=1 (complete destruction). For strike accuracy, weapon nodes have this attribute. Represents positioning accuracy. The detection node has this attribute.

[0053] reliability: ; in, Represents the information reliability of different nodes, mainly including detection nodes, which are detection intelligence information sensors and information transmission reliability. For communication nodes, it is a value related to the bit error rate. Cost performance: ; in, is the target value, For damage efficiency, is the ammunition consumption, n is the single-shot weapon cost, is the fuel consumption, m is the fuel unit price, Other cost consumption.

[0054] Those skilled in the art will appreciate that the modules in the above device embodiments may be distributed in the device as described, or may be changed accordingly and distributed in one or more devices different from the above embodiments. The modules in the above embodiments may be combined into one module, or may be further split into multiple submodules.

[0055] An electronic device embodiment of the present invention is described below. The details described in the electronic device embodiment of the present invention should be regarded as a supplement to the above-mentioned method or device embodiment. Details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above-mentioned method or device embodiment.

[0056] Figure 5 is a structural block diagram of an exemplary embodiment of an electronic device according to the present invention. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0057] like Figure 5 As shown, the electronic device 400 of this exemplary embodiment is in the form of a general data processing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different electronic device components (including the storage unit 420 and the processing unit 410), a display unit 440, etc.

[0058] The storage unit 420 stores a computer-readable program, which may be a source program or a code of a read-only program. The program may be executed by the processing unit 410, so that the processing unit 410 performs the steps of various embodiments of the present invention. For example, the processing unit 410 may perform the following steps: Figure 1 Steps shown.

[0059] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 4201 and / or a cache storage unit 4202, and may further include a read-only storage unit (ROM) 4203. The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 include but are not limited to: operating electronic devices, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0060] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0061] The electronic device 400 may also communicate with one or more external devices 300 (e.g., keyboard, display, network device, Bluetooth device, etc.) so that a user can interact with the electronic device 400 via these external devices 300, and / or enable the electronic device 400 to communicate with one or more other data processing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 450, and may also be performed through a network adapter 460 with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet). The network adapter 460 may communicate with other modules of the electronic device 400 through the bus 430. It should be understood that although Figure 5 Not shown, other hardware and / or software modules may be used in the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID electronic devices, tape drives, and data backup storage electronic devices, etc.

[0062] Figure 6 Schematic diagram of a computer readable medium embodiment of the present invention. Figure 6 As shown, the computer program can be stored on one or more computer-readable media. The computer-readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electronic device, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. When the computer program is executed by one or more data processing devices, the computer-readable medium can implement the above method of the present invention, namely: taking combat units as nodes, constructing a combat network according to the interactive relationship between combat units, and determining edge attributes as combat indicators according to network node parameters; judging whether to generate a static kill chain or a dynamic kill chain according to the number of targets attacked in the current combat scenario and the number of combat indicators required for optimization; if the number of targets attacked in the current combat scenario is single, performing a single-target single-indicator kill chain optimization, or performing a single-target multi-indicator kill chain optimization to generate a static kill chain for each combat unit; if the number of targets attacked in the current combat scenario is multiple and the combat indicators are multiple, performing a multi-target multi-indicator kill chain optimization by combining and optimizing different kill chain paths to generate a dynamic kill chain.

[0063] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation mode of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a data processing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the present invention.

[0064] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction-executing electronic device, device, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0065] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0066] In summary, the present invention can be implemented by a method, apparatus, electronic device or computer-readable medium that executes a computer program. In practice, a general-purpose data processing device such as a microprocessor or a digital signal processor (DSP) can be used to implement some or all of the functions of the present invention.

[0067] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-objective kill chain optimization method based on combinatorial optimization, characterized in that: The method comprises: Taking combat units as nodes, a combat network is constructed based on the interaction between combat units, and edge attributes are determined as combat indicators based on network node parameters. Determine whether to generate a static kill chain or a dynamic kill chain based on the number of targets to be attacked in the current combat scenario and the number of combat indicators required for optimization; If the current combat scenario has only one target, a single-eye single-index kill chain optimization is performed, or a single-eye multi-index kill chain optimization is performed to generate a static kill chain for each combat unit; If the current combat scenario has multiple targets and multiple combat indicators, a multi-target and multi-indicator kill chain is optimized by combining and optimizing different kill chain paths to generate a dynamic kill chain.

2. The method according to claim 1, characterized in that If the number of targets to be attacked in the current combat scenario is single and the number of combat indicators required for optimization is single, a single-eye single-indicator kill chain is optimally generated to generate a static kill chain, further comprising: Select a starting point, store the starting point in a first array, and store all vertices except the starting point and the distances from each vertex to the starting point in a second array, wherein: the first array is used to store vertices for which the shortest path has been found and the corresponding shortest path lengths, and the second array is used to store vertices for which the shortest path has not yet been found and the distances from the vertex to the starting point; Loop through the vertex removal step until all vertices in the second array are traversed; The loop performs the vertex removal step including: Find the target vertex with the shortest distance to the starting point from the second array, and add the target vertex to the first array. At the same time, remove the target vertex from the second array and update the distance from each vertex in the second array to the starting point.

3. The method according to claim 1, characterized in that The step of performing multi-target and multi-indicator kill chain optimization by combining and optimizing different kill chain paths to generate a dynamic kill chain includes: Mapping the attack nodes and the attack targets into a weighted bipartite graph; Finding the best complete match between the strike node and the strike target in the weighted bipartite graph; Generates a static kill chain based on the best complete match.

4. The method according to claim 3, characterized in that Mapping the attack nodes and the attack targets into a weighted bipartite graph includes: Assume that the weighted graph is , where all vertices can be divided into attack node subsets and the target node subset ,satisfy Do not intersect, Take any edge<i,j> , which is the weighted bipartite graph obtained by mapping the attack nodes and the attack targets.

5. The method according to claim 3, characterized in that: The step of searching for the best complete match between the attack node and the attack target in the weighted bipartite graph includes: Using an adjacency matrix to store the weighted bipartite graph; Initialize the vertex coordinates and use the Hungarian algorithm to find the best complete match. If the best complete match is not found, update the vertex coordinate values ​​and use the Hungarian algorithm to find the best complete match until the best complete match is found. Where: Let M be a complete matching of a weighted bipartite graph, give each point a top label, and let the top label of the attack node be , the top label of the target node is , if all edges<i,j> ∈G, both , It is the edge<i,j> The weight of<i,j> ∈M, both , then M is an optimal complete matching.

6. The method according to claim 1, characterized in that If the number of strike targets in the current combat scenario is single and the number of combat indicators is multiple, a single-eye multi-indicator kill chain is optimally generated to generate a static kill chain, further including: The following formula is used to convert multiple operational indicators into a single overall indicator: ； in, , is the indicator weighting coefficient, is the closing time of the current kill chain, is the minimum value of all kill chain closure times, is the maximum closing time of all kill chains, The damage efficiency of the current kill chain, is the minimum value of the effectiveness of all kill chains, is the maximum damage efficiency in all kill chains, For the reliability of the current kill chain, is the minimum reliability of all kill chains, is the maximum reliability value in all kill chains, The cost performance of the current kill chain, is the minimum value of all kill chain cost effectiveness, is the maximum cost-effectiveness of all kill chains, and A is a single total indicator; A single-eye single-indicator kill chain is preferably used to generate a static kill chain based on the single overall indicator.

7. The method according to claim 1, characterized in that The combat indicators include: Closing time: ; in, is the consumption time of the node or edge, such as the decision time of the command node. If there is information transmission time on the edge, it is also added in sequence; Damage effectiveness: ; in, Represents the weapon coefficient, Represents heavy weapons, value 4, Represents light weapons, with a value of 1. The basic damage capability is inversely proportional to the target's resistance to destruction, and is taken as 0.25, that is, the damage of light weapons is 0.25*1=0.25, and that of heavy weapons is 0.25*4=1 (complete destruction). For strike accuracy, weapon nodes have this attribute. Represents positioning accuracy. The detection node has this attribute. reliability: ; in, Represents the information reliability of different nodes, mainly including detection nodes, which are detection intelligence information sensors and information transmission reliability. For communication nodes, it is a value related to the bit error rate. Cost performance: ; in, is the target value, For damage efficiency, is the ammunition consumption, n is the single-shot weapon cost, is the fuel consumption, m is the fuel unit price, Other cost consumption.

8. A multi-target kill chain optimization device based on combinatorial optimization, characterized in that: The device comprises: A construction module is used to construct a combat network based on the interaction relationship between combat units and combat units by taking combat units as nodes, and to determine edge attributes as combat indicators based on network node parameters; A judgment module is used to determine whether to generate a static kill chain or a dynamic kill chain based on the number of targets to be attacked in the current combat scenario and the number of combat indicators required for optimization; The first generation module is used to perform a single-eye single-index kill chain optimization if the number of targets to be attacked in the current combat scenario is single, or to perform a single-eye multi-index kill chain optimization to generate a static kill chain for each combat unit; The second generation module is used to generate a dynamic kill chain by optimizing different kill chain paths to select a multi-target and multi-indicator kill chain if there are multiple targets and multiple combat indicators in the current combat scenario.

9. An electronic device, characterized in that: include: processor; as well as A memory storing computer executable instructions which, when executed, cause the processor to perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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