A method and device for dynamically constructing a killing path model based on a killing net

Abstract

The application discloses a kind of based on killing net's killing path model dynamic construction method and device, the method includes: constructing killing net element model set and killing net node intercommunication model;Based on task purpose information, construct simulation scenario information;The killing net element model set is handled using the simulation scenario information, and killing path dynamic closed model is obtained;The killing path dynamic closed model is evaluated and handled, and dynamic closed killing path efficiency evaluation result is obtained.In the confrontation simulation deduction of killing net, the killing path dynamic construction and closure are realized, the killing net concept is demonstrated quickly by killing path construction and actual closure, the efficiency and performance of killing net are evaluated and calculated, the flexibility, adaptability and anti-destroying toughness of killing net can be experimented and verified, the killing path system toughness and confrontation are improved, and the timeliness, resource utilization and use efficiency of killing are also improved.

Description

Technical Field

[0001] This invention relates to the field of military modeling and simulation technology, and in particular to a method and apparatus for dynamically constructing a kill path model based on a kill net, which realizes the construction and closure of a dynamic kill path model in kill net confrontation simulation, and the capability assessment of the kill net model based on the dynamic closure of the kill path model. Background Technology

[0002] To achieve the goal of eliminating a specific target, the complex combat process is broken down into a closed-loop kill path consisting of stages such as detection, identification, tracking, decision-making, strike, and assessment. This has become one of the important theories in modern warfare. Resources are concentrated at each stage to improve the efficiency and accuracy of the strike. With the increasing complexity and variability of modern warfare, kill paths are evolving from single linear chains to multi-dimensional network structures.

[0003] By utilizing the specific capabilities of widely distributed game units as needed and in real-time, and constructing a more efficient, flexible, and survivable kill network system to achieve self-organization, self-adaptation, self-recovery, and dynamic, reconfigurable kill paths, a crucial gameplay tactic in modern warfare has emerged. A kill network can be considered a multidimensional network topology structure composed of one or more links from a complete kill path in a traditional game system; it can be viewed as a mesh-like kill path. The kill paths constructed in real-time within a kill network still constitute the kill process, consisting of steps such as "discovery, identification, tracking, targeting, attack, and assessment."

[0004] Discovery: Based on the game task and commands, detect dynamic events and targets in the area of ​​interest under responsibility, and plan and deploy the sensors for the task.

[0005] Location: The main tasks are to classify, identify and locate the detected objects, make a preliminary judgment on their importance and priority, distribute information and determine the need for further information acquisition.

[0006] Tracking: The main task is to continuously track the target object, further assess and judge its importance, determine the value of the target object, evaluate its trend, and adjust the mission plan according to the situation.

[0007] Targeting: This mainly involves determining the priority of the target, assessing the timing of the strike, the impact of weather, and determining the strike method and attack plan.

[0008] Engagement: This mainly involves issuing decision-making orders and executing attacks, launching weapons, fire guidance, and effect assessment.

[0009] Assessment: This mainly involves detecting and determining the extent of damage to the target being attacked.

[0010] In the early stages of kill path system construction, a pre-defined approach was adopted, involving selecting equipment, designating dedicated links, confirming dedicated messages, and clarifying dedicated capabilities. While its structured, serial, and pre-configured characteristics played a role in standardizing processes and improving efficiency, it also presented vulnerabilities and rigidities in its system architecture. With the development of information technology, through capability decoupling, network interconnection, and system interoperability, kill systems have entered a dynamic kill path construction stage based on kill networks. This stage enables flexible, on-demand kill path construction, allowing for the combination of multiple kill paths for the same target. This represents a shift from single kill paths to multi-link kill networks, allowing for the selection of the least costly, fastest, most accurate, and most damaging kill path for closed-loop execution. This enhances system resilience and countermeasures, as well as the timeliness of kills, resource utilization, and overall effectiveness. Summary of the Invention

[0011] This invention addresses the needs of kill net modeling and simulation by proposing a dynamic construction method and apparatus for kill path models based on kill nets. In adversarial simulations against kill nets, it can rapidly construct and close kill paths based on the current state and capabilities of each participating element in the kill net, evaluate and calculate the effectiveness and performance of the kill net, and conduct experiments and verifications on the flexibility, adaptability, and resilience of the kill net. This not only improves the resilience and adversarial capabilities of the kill net system but also enhances the timeliness of kills, resource utilization, and usage benefits.

[0012] To address the aforementioned technical problems, the first aspect of this invention discloses a method for dynamically constructing a kill path model based on a kill net, the method comprising:

[0013] S1, Construct the kill network element model set and the communication model between kill network nodes;

[0014] The kill network element model set includes a detection node model set, a location node model set, a tracking node model set, an aiming node model set, a game node model set, and an evaluation node model set.

[0015] S2, Based on the mission objective information, construct simulation scenario information;

[0016] S3, using the simulation scenario information to process the kill network element model set to obtain a dynamic closed model of the kill path;

[0017] S4, evaluate the dynamic closure model of the kill path to obtain the evaluation result of the dynamic closure kill path effectiveness.

[0018] As an optional implementation, in a first aspect of the present invention, the step of processing the kill network element model set using the simulation scenario information to obtain a dynamic closure model of the kill path includes:

[0019] S31, Process the simulation scenario information to obtain the target detection task;

[0020] S32, Based on the target detection task, the kill network element model set is processed to construct a dynamic closure model of the kill path.

[0021] As an optional implementation, in the first aspect of the present invention, the step of processing the kill network element model set based on the target detection task to construct a kill path dynamic closure model includes:

[0022] S321, the detection node model set processes the target detection task to obtain a dynamic kill path detection node model and detection object information;

[0023] S322, Based on the detected object information, the location node model set is processed using the communication model between the kill network nodes to obtain the dynamic kill path location node model and target location result information;

[0024] S323, Based on the positioning result information, the tracking node model set is processed using the communication model between the kill network nodes to obtain the dynamic kill path tracking node model and target tracking result information;

[0025] S324, Based on the target tracking result information, the aiming node model set is processed using the kill network node communication model to obtain the dynamic kill path aiming node model and target attack command information;

[0026] S325, Based on the target attack instruction information, the game node model set is confirmed using the kill network node communication model to obtain the dynamic kill path game node model and target attack result information.

[0027] S326, Based on the target attack result information, the evaluation node model set is processed to obtain the dynamic kill path evaluation node model and target evaluation result information;

[0028] S327, Based on the dynamic closure rule of the kill path, the dynamic kill path detection node model, the dynamic kill path positioning node model, the dynamic kill path tracking node model, the dynamic kill path aiming node model, the dynamic kill path game node model, the dynamic kill path evaluation node model, and the communication node model are used to obtain the dynamic closure model of the kill path.

[0029] As an optional implementation, in a first aspect of the present invention, the detection node model set processes the target detection task to obtain a dynamic kill path detection node model and detection object information, including:

[0030] S3211, Obtain the target detection task;

[0031] S3212, Based on the target detection task, obtain detection capability requirement information;

[0032] S3213, Based on the detection capability requirement information, match the detection node model set to obtain a dynamic kill path detection node model;

[0033] The detection node model set includes several detection node models;

[0034] S3214, the dynamic kill path detection node model detects the detection area and obtains the detection object information.

[0035] As an optional implementation, in the first aspect of the present invention, the step of processing the location node model set based on the detected object information using the kill network node communication model to obtain dynamic kill path location node models and target location result information includes:

[0036] S3221, Based on the detected object information, obtain the positioning requirement information;

[0037] S3222, The kill network node communication model publishes the positioning request information to the positioning node model;

[0038] The positioning requirement information includes the location information of the positioning object, the environmental area information of the positioning object, and the constraint information of the positioning object;

[0039] S3223, the positioning node model set processes the positioning requirement information to obtain a positioning result information set;

[0040] The location node model set includes several location node models;

[0041] The location result information set includes several location result information items;

[0042] The positioning result information includes positioning time information, positioning accuracy information, and positioning cost information;

[0043] S3224, using the positioning node optimization model, the positioning result information set is processed to the dynamic kill path positioning node model;

[0044] The expression for the preferred positioning node model is:

[0045] ,

[0046] Where l represents the index of the location node model; This represents the overall benefit of the l-th positioning node model; This indicates the location completion time of the l-th location node model; This represents the optimal positioning accuracy of the l-th positioning node model; This represents the minimum resources required for the l-th location node model;

[0047] S3225, Based on the dynamic kill path positioning node model, match the positioning result information set to obtain target positioning result information.

[0048] As an optional implementation, in the first aspect of the present invention, the step of processing the tracking node model set based on the target localization result information using the kill network node communication model to obtain a dynamic kill path tracking node model and target tracking result information includes:

[0049] S3231, Based on the target positioning result information, obtain tracking requirement information;

[0050] The tracking requirement information includes tracking area information, tracking spatial information, tracking time information, and tracking accuracy information;

[0051] S3232, the tracking node model set acquires and processes the tracking requirement information to obtain a tracking result information set;

[0052] The tracking result information set includes several tracking result information items;

[0053] The tracking result information includes the fastest tracking time, tracking accuracy, trackable time, and resource consumption.

[0054] S3233, The tracking result information set is processed using the tracking benefit calculation model to obtain the tracking benefit set;

[0055] S3234, Based on the tracking benefit set and the tracking result information set, determine the dynamic kill path tracking node model;

[0056] S3235, Based on the dynamic kill path tracking node model, match the tracking result information set to obtain target tracking result information.

[0057] As an optional implementation, in the first aspect of the present invention, the evaluation processing of the dynamic closure model of the kill path to obtain the dynamic closure kill path effectiveness evaluation result includes:

[0058] S41, Obtain the kill path action effect information of the kill path dynamic closure model;

[0059] S42, using the kill network timeliness analysis model, process the kill path action effect information to obtain kill network timeliness result information;

[0060] The expression for the kill network timeliness analysis model is as follows:

[0061] ,

[0062] ,

[0063] Where T represents the kill network closure time; i represents the index of the kill path dynamic closure model; and n represents the number of kill path dynamic closure models in the kill network. This represents the closing time of the dynamic closing model of the i-th kill path; This indicates the moment when the game node of the i-th dynamic closure model of the kill path completes its attack; This indicates the moment when the i-th dynamic closure model of the kill path detects the target object;

[0064] S43, Based on the kill network scope model, the kill path action effect information is processed to obtain kill network scope information;

[0065] The expression for the effective range model of the kill net is:

[0066] ,

[0067] in, This represents the distance between the aiming node and the game object of the game node in the i-th dynamic closure model of the kill path;

[0068] S44, Based on the kill network benefit analysis model, the kill path action effect information is processed to obtain kill network benefit information;

[0069] The expression for the kill net effectiveness analysis model is as follows:

[0070] ,

[0071] Where E represents the damage network benefit; M represents the number of damaged targets in the kill network; and m represents the index of the damaged target object in the kill network. This represents the value of the m-th damaged target object; The damage level of the m-th damaged target object is indicated; Q represents the amount of resources consumed in the kill net; q represents the index of the consumed resources. This represents the value of the q-th resource consumed;

[0072] S45, Based on the kill network element node influence analysis model, the kill network benefit information of the kill network is processed to obtain the kill network element node influence information;

[0073] S46, integrate the kill network timeliness result information, the kill network range information, the kill network benefit information, and the kill network element node influence information to obtain the dynamic closed kill path effectiveness evaluation result.

[0074] The second aspect of this invention discloses a dynamic construction device for a kill path model based on a kill network, the device comprising: a kill network element model construction module, a task information acquisition module, a kill path closure model acquisition module, and a kill path effectiveness evaluation module;

[0075] The kill net feature model construction module is used to construct a kill net feature model set;

[0076] The kill network element model set includes a detection node model set, a location node model set, a tracking node model set, an aiming node model set, a game node model set, an evaluation node model set, and a kill network node communication model.

[0077] The task information acquisition module is used to construct simulation scenario information based on task objective information;

[0078] The kill path closure model acquisition module is used to process the kill network element model set using the simulation scenario information to obtain a dynamic closure model of the kill path.

[0079] The kill path effectiveness evaluation module is used to evaluate the dynamic closure model of the kill path and obtain the dynamic closure kill path effectiveness evaluation result.

[0080] A third aspect of this invention discloses another apparatus for dynamically constructing a kill path model based on a kill net, the apparatus comprising:

[0081] Memory containing executable program code;

[0082] A processor coupled to the memory;

[0083] The processor calls the executable program code stored in the memory to execute the dynamic construction method of kill path model based on kill net disclosed in the first aspect of the present invention.

[0084] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked, are used to execute the dynamic construction method of kill path model based on kill net disclosed in the first aspect of the present invention.

[0085] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0086] This invention discloses a method and apparatus for dynamically constructing a kill path model based on a kill net. It presents the node model and its capability design, the communication model design between kill net nodes, and the dynamic construction and closure method for the kill path model in the kill net. It also proposes a kill net performance evaluation method based on kill path effectiveness indices. This invention can design kill net system models, dynamically construct kill path models during kill net simulation, realize kill path closure and kill net performance analysis, provide support for kill net conceptual design and demonstration verification, dynamic kill path construction design and demonstration verification, and provide methods for analyzing kill net system capabilities and adjusting the capabilities of kill net components. Attached Figure Description

[0087] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0088] Figure 1 This is a schematic diagram illustrating an application scenario of the dynamic construction method for kill path model based on kill net disclosed in Embodiment 1 of the present invention.

[0089] Figure 2 This is a flowchart illustrating the dynamic construction method of the kill path model based on the kill network disclosed in Embodiment 1 of the present invention.

[0090] Figure 3 This is a schematic diagram of the structure of a kill network-based kill path model dynamic construction device disclosed in Embodiment 2 of the present invention;

[0091] Figure 4 This is a schematic diagram of another dynamic construction device for kill path model based on kill net disclosed in Embodiment 3 of the present invention. Detailed Implementation

[0092] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0093] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0094] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0095] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0096] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0097] This application provides a method, apparatus, computer device, and computer-readable storage medium for dynamically constructing a kill path model based on a kill net, which will be described in detail below.

[0098] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating a scenario in which the kill network-based kill path model dynamic construction device provided in this application is used in a game simulation system. The game simulation system may include a computer device 100, which integrates a kill network-based kill path model dynamic construction device. Figure 1 Computer equipment in the country.

[0099] In this embodiment, the computer device 100 can be a standalone server, a server network, or a server cluster. For example, the computer device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0100] It is understood that the computer device 100 used in the embodiments of this application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0101] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of computer devices shown is more or less, for example Figure 1 Only one computer device is shown in the diagram. It is understood that the system may also include one or more other services, which are not limited here.

[0102] In addition, such as Figure 1 As shown, the game simulation system may also include a memory 200 for storing sensor-collected data, scenario data, processing result data, etc.

[0103] It should be noted that, Figure 1The schematic diagram illustrating the application scenario of the kill path model dynamic construction device based on kill nets is merely an example. The kill path model dynamic construction device and scenario described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of simulation control management systems and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0104] This invention discloses a method and apparatus for dynamically constructing a kill path model based on a kill net. It presents the node model and its capability design, the communication model design between kill net nodes, and the dynamic construction and closure method for the kill path model in the kill net. It also proposes a kill net performance evaluation method based on kill path effectiveness indices. This invention can design kill net system models, dynamically construct kill path models during kill net simulation, realize kill path closure and kill net performance analysis, provide support for kill net conceptual design and demonstration verification, dynamic kill path construction design and demonstration verification, and provide methods for analyzing kill net system capabilities and adjusting the capabilities of kill net components. These are described in detail below.

[0105] Example 1

[0106] Please refer to Figure 2 In order to better understand the technical solution of the present invention. Figure 2 This is a flowchart illustrating a method for dynamically constructing a kill path model based on a kill net, as disclosed in an embodiment of the present invention. Figure 2 The described method for dynamically constructing kill path models based on kill nets is applied to game simulation systems, such as local servers or cloud servers used in game simulation systems; however, this embodiment of the invention is not limited to these applications. Figure 2 As shown, the method for dynamically constructing a kill path model based on a kill net can include the following operations:

[0107] S1, Construct the kill network element model set and the communication model between kill network nodes;

[0108] It should be noted that in this invention, the kill network element model set is divided according to the roles and capabilities of the kill network node elements. The node types in the kill network model of this invention include detection nodes, location nodes, tracking nodes, aiming nodes, strike nodes, and evaluation nodes, which respectively have the capabilities of detection and discovery, location identification, continuous tracking, attack decision-making, attack initiation, and verification and evaluation, so as to realize the kill process of "discovery, location, tracking, aiming, engagement, and evaluation".

[0109] The kill network element model set includes a detection node model set, a location node model set, a tracking node model set, an aiming node model set, a game node model set, and an evaluation node model set.

[0110] It should be noted that the kill network node communication model is used for communication interconnection and information exchange between any node model and other node models;

[0111] It should be noted that, in this embodiment of the invention, the communication model between the kill network nodes is an efficiency level model, in which any node can communicate with other nodes. The communication connection between nodes takes into account communication distance and line-of-sight obstruction, that is, the communication connectivity is determined by checking the maximum communication distance and line-of-sight obstruction. The communication content between nodes in the kill network consists of the node's received and output information.

[0112] S2, Based on the mission objective information, construct simulation scenario information;

[0113] S3, using the simulation scenario information to process the kill network element model set to obtain a dynamic closed model of the kill path;

[0114] S4, evaluate the dynamic closure model of the kill path to obtain the evaluation result of the dynamic closure kill path effectiveness.

[0115] Optionally, constructing the kill network element model set includes:

[0116] S11, Based on the capabilities of the detection nodes and the information interaction requirements, construct the detection node model set;

[0117] It should be noted that the set of detection node models includes several detection node models;

[0118] It should be noted that the aforementioned detection node model primarily simulates systems with reconnaissance and detection capabilities, such as visible light reconnaissance satellites, electronic reconnaissance satellites, radar reconnaissance satellites, electronic reconnaissance aircraft, photographic reconnaissance aircraft, reconnaissance ships, ground radars, ground electronic reconnaissance equipment, and submarine sonar reconnaissance. These systems possess reconnaissance capabilities that can be mobile or fixed in location. The model's main capabilities are abstracted as reconnaissance type, reconnaissance range, reconnaissance distance, reconnaissance resolution, maximum reconnaissance speed, and reconnaissance capability attenuation. Information interaction capabilities include receiving detection commands and outputting the location, type, and identity of the reconnaissance target. Based on these information interaction capabilities, information interaction requirements are determined.

[0119] S12, Based on the positioning node capabilities and information interaction requirements, construct the positioning node model set;

[0120] It should be noted that the location node model set includes several location node models;

[0121] It should be noted that the positioning node model is used to simulate a system with reconnaissance, positioning and identification capabilities. The main capabilities of the model are abstracted as positioning time, positioning type, object type, location, positioning accuracy, etc. The main output is the location, identity and object type of the positioning object.

[0122] S13, Based on the tracking node capabilities and information interaction requirements, construct the tracking node model set;

[0123] It should be noted that the tracking node model set includes several tracking node models;

[0124] It should be noted that the tracking node model is used to simulate systems with detection and tracking capabilities, such as visible light reconnaissance satellites, electronic reconnaissance satellites, radar reconnaissance satellites, electronic reconnaissance aircraft, photographic reconnaissance aircraft, reconnaissance ships, ground radar, ground electronic reconnaissance equipment, and submarine sonar reconnaissance. The main capabilities of the model are abstracted as tracking type, tracking range, maximum tracking distance, maximum tracking speed, tracking duration, and tracking accuracy. The interactive capabilities include receiving tracking commands and outputting the type of the tracked object, the importance of the tracked object, its real-time position, stable tracking duration, and the importance judgment of the tracked object.

[0125] S14, Based on the targeting node capabilities and information interaction requirements, construct the targeting node model set;

[0126] It should be noted that the aiming node model set includes several aiming node models;

[0127] It should be noted that the target node model is used to simulate a decision-making entity that can compare targets for attack, formulate attack plans, determine attack action plans, determine effect verification plans, and issue attack and verification commands. The model's capabilities are characterized by the type of plan generated, the timing, and the efficiency of information processing. The interactive capabilities include receiving information on the task execution status of detection, location, tracking, attack, and evaluation nodes, sending attack and verification commands, and outputting information such as attack priority, execution platform, attack time window, and attack method.

[0128] S15, Based on the ability to attack nodes and the need for information interaction, construct the game node model set;

[0129] It should be noted that the game node model set includes several game node models;

[0130] It should be noted that the strike node model is used to simulate weapon systems such as ammunition, shells, and missiles that can cause physical and functional damage to the target through strikes, and can execute attack and strike missions according to the strike plan; the information interaction capability includes receiving strike commands, and the main output information is the information of the launched ammunition, ammunition type, launch time, and ammunition impact point;

[0131] S16, Based on the evaluation node capabilities and information interaction requirements, construct the evaluation node model set;

[0132] It should be noted that the evaluation node model set includes several evaluation node models;

[0133] It should be noted that the aforementioned evaluation node model mainly simulates a system with the ability to reconnoiter, identify, and assess damage. It can receive damage verification instructions, perform information collection, information transmission, and damage result assessment. Its information interaction capabilities include receiving verification instructions and outputting information such as the verification time, the time of verification, the damaged object, and the degree of damage to the target.

[0134] Optionally, the step of processing the kill network element model set using the simulation scenario information to obtain a dynamic closure model of the kill path includes:

[0135] S31, Process the simulation scenario information to obtain the target detection task;

[0136] S32, Based on the target detection task, the kill network element model set is processed to construct a dynamic closure model of the kill path.

[0137] Optionally, the step of processing the kill network element model set based on the target detection task to construct a dynamic closure model of the kill path includes:

[0138] S321, the detection node model set processes the target detection task to obtain a dynamic kill path detection node model and detection object information;

[0139] S322, Based on the detected object information, the location node model set is processed using the communication model between the kill network nodes to obtain the dynamic kill path location node model and target location result information;

[0140] S323, Based on the target location result information, the tracking node model set is processed using the communication model between the kill network nodes to obtain the dynamic kill path tracking node model and target tracking result information;

[0141] S324, Based on the target tracking result information, the aiming node model set is processed using the kill network node communication model to obtain the dynamic kill path aiming node model and target attack command information;

[0142] S325, Based on the target attack instruction information, the game node model set is confirmed using the kill network node communication model to obtain the dynamic kill path game node model and target attack result information.

[0143] S326, Based on the target attack result information, the evaluation node model set is processed to obtain the dynamic kill path evaluation node model and target evaluation result information;

[0144] S327, Based on the dynamic closure rule of the kill path, the dynamic kill path detection node model, the dynamic kill path positioning node model, the dynamic kill path tracking node model, the dynamic kill path aiming node model, the dynamic kill path game node model, the dynamic kill path evaluation node model, and the communication node model are used to obtain the dynamic closure model of the kill path.

[0145] It should be noted that, according to the roles and capabilities of the kill network node elements, the dynamic kill path detection node model, the dynamic kill path location node model, the dynamic kill path tracking node model, the dynamic kill path aiming node model, the dynamic kill path game node model, the dynamic kill path evaluation node model, and the communication node model are ordered to transmit commands in the sequence of the kill process of "discovery, location, tracking, aiming, engagement, and evaluation", thereby realizing the dynamic closure of a kill path model and obtaining a dynamic closure model of the kill path.

[0146] Optionally, the detection node model set processes the target detection task to obtain a dynamic kill path detection node model and detection object information, including:

[0147] S3211, Obtain the target detection task;

[0148] S3212, Based on the target detection task, obtain detection capability requirement information;

[0149] S3213, Based on the detection capability requirement information, match the detection node model set to obtain a dynamic kill path detection node model;

[0150] The detection node model set includes several detection node models;

[0151] S3214, The dynamic kill path detection node model detects the detection area and obtains the detection object information;

[0152] It should be noted that during the dynamic simulation of the kill net model, based on the predetermined target detection task, a designated area is probed. According to the initial mission objective, detection node models with detection and reconnaissance capabilities will perform detection according to their own capabilities. All detection node models that detect objects can publish location requests. The location of each detected object can become the starting node for the dynamic kill path construction. During the dynamic construction of the kill path model, the time when the i-th detection node model detects an object is denoted as . .

[0153] Optionally, the step of processing the location node model set based on the detected object information using the kill network node communication model to obtain dynamic kill path location node models and target location result information includes:

[0154] S3221, Based on the detected object information, obtain the positioning requirement information;

[0155] S3222, The kill network node communication model publishes the positioning request information to the positioning node model;

[0156] The positioning requirement information includes the location information of the positioning object, the environmental area information of the positioning object, and the constraint information of the positioning object;

[0157] S3223, the positioning node model set processes the positioning requirement information to obtain a positioning result information set;

[0158] The location node model set includes several location node models;

[0159] The location result information set includes several location result information items;

[0160] The positioning result information includes positioning time information, positioning accuracy information, and positioning cost information;

[0161] S3224, using the positioning node optimization model, the positioning result information set is processed to the dynamic kill path positioning node model;

[0162] The expression for the preferred positioning node model is:

[0163] ,

[0164] Where l represents the index of the location node model; This represents the overall benefit of the l-th positioning node model; This indicates the location completion time of the l-th location node model; This represents the optimal positioning accuracy of the l-th positioning node model; This represents the minimum resources required for the l-th location node model;

[0165] It should be noted that the comprehensive benefits of all positioning node models with positioning capabilities are calculated using the positioning node optimization model, and the positioning node model with the largest comprehensive benefits is selected as the dynamic kill path positioning node model.

[0166] S3225, Based on the dynamic kill path positioning node model, match the positioning result information set to obtain target positioning result information.

[0167] Optionally, the step of processing the positioning result information set using the positioning node optimization model to obtain the dynamic kill path positioning node model includes:

[0168] S32241, Based on the aforementioned positioning node optimization model, obtain the comprehensive benefits of all positioning node models;

[0169] S32242, obtain the maximum value among the comprehensive benefits of all the positioning node models, and obtain the optimal comprehensive benefit value;

[0170] S32243, Obtain the positioning node model corresponding to the optimal comprehensive benefit value, and obtain the dynamic kill path positioning node model.

[0171] Optionally, the step of processing the tracking node model set based on the target localization result information using the kill network node communication model to obtain the dynamic kill path tracking node model and target tracking result information includes:

[0172] S3231, Based on the target positioning result information, obtain tracking requirement information;

[0173] The tracking requirement information includes tracking area information, tracking spatial information, tracking time information, and tracking accuracy information;

[0174] It should be noted that the target location result information includes the category information of the located object and the importance information of the located object;

[0175] It should be noted that the location object category information and the location object importance information are obtained by the dynamic kill path location node model to identify the location object, and based on the location object category information and the location object importance information, the model obtains and publishes tracking request information to the tracking node model set;

[0176] S3232, the tracking node model set acquires and processes the tracking requirement information to obtain a tracking result information set;

[0177] The tracking result information set includes several tracking result information items;

[0178] The tracking result information includes the fastest tracking time, tracking accuracy, trackable time, and resource consumption.

[0179] S3233, The tracking result information set is processed using the tracking benefit calculation model to obtain the tracking benefit set;

[0180] S3234, Based on the tracking benefit set and the tracking result information set, determine the dynamic kill path tracking node model;

[0181] S3235, Based on the dynamic kill path tracking node model, match the tracking result information set to obtain target tracking result information.

[0182] Optionally, the expression for the tracking benefit calculation model is:

[0183] ,

[0184] Where j represents the index of the tracking node model; The tracking result information set is used to obtain the tracking benefit of the earliest representation of the j-th tracking node model; This represents the tracking accuracy of the j-th tracking node model; This represents the trackable duration of the j-th tracking node model; This represents the tracking resource consumption of the j-th tracking node model;

[0185] Optionally, determining the dynamic kill path tracking node model based on the tracking benefit set and the tracking result information set includes:

[0186] S32341, Based on the tracking result information set, obtain the earliest tracking time. Optimal tracking accuracy Longest trackable duration Minimal tracking resource consumption ;

[0187] S32342, Based on the aforementioned tracking benefit set, determine the maximum tracking benefit. ;

[0188] S32343, Obtain the earliest tracking time Or, optimal tracking accuracy Or, the longest trackable duration Or, minimum tracking resource consumption Or, maximum tracking benefit The corresponding tracking node model is used to obtain the dynamic kill path tracking node model;

[0189] It should be noted that in this embodiment, the tracking efficiency is maximized. The corresponding tracking node model serves as the dynamic kill path tracking node model.

[0190] Optionally, the step of processing the targeting node model set based on the target tracking result information using the kill network node communication model to obtain the dynamic kill path targeting node model and target attack command information includes:

[0191] S3241, the targeting node model set, based on the target tracking result information, publishes attack and damage requirement information to the game platform through the kill network node communication model;

[0192] S3242, Based on the attack damage requirement information, the target gaming platform is determined;

[0193] It should be noted that the determination of the target gaming platform based on the attack damage requirement information includes: the gaming platform that meets the attack damage requirement information is the target gaming platform.

[0194] S3243, the target game platform sends the earliest launch time and launch cost information to the target node model to obtain the earliest launch time set and launch cost information set;

[0195] S3244, Based on the earliest launch time set and the launch cost information set, determine the preferred earliest launch time and the minimum launch cost information;

[0196] S3245, Based on the preferred earliest launch time, or the minimum launch cost information, determine the dynamic kill path aiming node model;

[0197] It should be noted that, in this embodiment, the aiming node model corresponding to the preferred earliest launch time is used as the dynamic kill path aiming node model;

[0198] S3246, Based on the dynamic kill path aiming node model, determine the strike platform information;

[0199] S3247, Based on the strike platform information, determine the target attack command information;

[0200] Optionally, the step of confirming the game node model set based on the target attack command information using the kill network node communication model to obtain the dynamic kill path game node model and target attack result information includes:

[0201] S3251, the dynamic kill path targeting node model uses the kill network node inter-communication model to send the target attack instruction information to the game node model set;

[0202] S3252, Based on the game node performance calculation model, the game node model set processes the target attack instruction information to obtain the game node performance information set;

[0203] The expression for the game node performance calculation model is as follows:

[0204] ,

[0205] in, c represents the resource consumption of the game node model; c represents the index of the game node model. This represents the risk coefficient when the c-th game node model is attacked; This represents the reconnaissance time of the c-th game node model; This represents the reconnaissance accuracy of the c-th game node model;

[0206] S3253, Based on the game node effectiveness information set, determine the dynamic kill path game node model;

[0207] It should be noted that the game node model corresponding to the maximum value in the game node effectiveness information set is selected as the dynamic kill path game node model.

[0208] S3254, using the dynamic kill path game node model, attack the target and obtain the target attack result information.

[0209] Optionally, the step of processing the evaluation node model set based on the target attack result information to obtain a dynamic kill path evaluation node model and target evaluation result information includes:

[0210] Using the evaluation node selection model, the evaluation node model set processes the target attack result information to obtain the dynamic kill path evaluation node model and target evaluation result information;

[0211] The expression for the evaluation node selection model is:

[0212] ,

[0213] in, d represents the overall benefit of the evaluation node model; d represents the index of the evaluation node model; This represents the reconnaissance duration of the d-th evaluation node model; This indicates the longest reconnaissance window that is expected to be able to conduct verification. This represents the risk coefficient of the d-th evaluation node model; This represents the reconnaissance accuracy of the d-th evaluation node model; This represents the resource consumption of the d-th evaluation node model;

[0214] It should be noted that, based on the comprehensive benefits of the aforementioned evaluation node model... A dynamic kill path evaluation node model is determined. When the comprehensive benefit of the evaluation node model is maximized, the corresponding evaluation node model is selected as the dynamic kill path evaluation node model.

[0215] Optionally, the evaluation process of the dynamic closure model of the kill path to obtain the dynamic closure kill path effectiveness evaluation result includes:

[0216] S41, Obtain the kill path action effect information of the kill path dynamic closure model;

[0217] S42, using the kill network timeliness analysis model, process the kill path action effect information to obtain kill network timeliness result information;

[0218] The expression for the kill network timeliness analysis model is as follows:

[0219] ,

[0220] ,

[0221] Where T represents the kill network closure time; i represents the index of the kill path dynamic closure model; and I represents the number of kill path dynamic closure models in the kill network. This represents the closing time of the dynamic closing model of the i-th kill path; This indicates the moment when the game node of the i-th dynamic closure model of the kill path completes its attack; This indicates the moment when the i-th dynamic closure model of the kill path detects the target object;

[0222] S43, Based on the kill network scope model, the kill path action effect information is processed to obtain kill network scope information;

[0223] The expression for the effective range model of the kill net is:

[0224] ,

[0225] in, This represents the distance between the aiming node and the game object of the game node in the i-th dynamic closure model of the kill path;

[0226] S44, Based on the kill network benefit analysis model, the kill path action effect information is processed to obtain kill network benefit information;

[0227] The expression for the kill net effectiveness analysis model is as follows:

[0228] ,

[0229] Where E represents the damage network benefit; M represents the number of damaged targets in the kill network; and m represents the index of the damaged target object in the kill network. This represents the value of the m-th damaged target object; The damage level of the m-th damaged target object is indicated; Q represents the amount of resources consumed in the kill net; q represents the index of the consumed resources. This represents the value of the q-th resource consumed;

[0230] S45, Based on the kill network element node influence analysis model, the kill network benefit information of the kill network is processed to obtain the kill network element node influence information;

[0231] It should be noted that the expression for the kill network element node influence analysis model is as follows:

[0232] ,

[0233] Where P represents the number of nodes in the initial kill web model; Q represents the number of features of interest removed from the kill web model; express The impact of each node on the kill network; This indicates the effectiveness of the kill net after removing the elements of interest; This indicates the effectiveness of the initial kill web model; This represents the value of the q-th node removed from the initial kill net model; This represents the value of the P-th node in the initial kill net model;

[0234] It should be noted that, through The value can provide a basis for the design of the kill net system and the optimization of node platform capabilities;

[0235] S46, integrate the kill network timeliness result information, the kill network range information, the kill network benefit information, and the kill network element node influence information to obtain the dynamic closed kill path effectiveness evaluation result;

[0236] It should be noted that the integration refers to combining and outputting data according to user needs.

[0237] As can be seen, the method for dynamically constructing a kill network-based kill path model provided in this embodiment constructs a kill network element model set and a kill network node communication model, including a detection node model set, a location node model set, a tracking node model set, an aiming node model set, a game node model set, and an evaluation node model set. Based on the mission objective information, simulation scenario information is constructed. The kill network element model set is processed using the simulation scenario information to obtain a dynamic closure model of the kill path. The dynamic closure model of the kill path is evaluated to obtain the dynamic closure kill path effectiveness evaluation result. In the adversarial simulation and deduction against the kill network, the dynamic construction and closure of the kill path can be realized. By demonstrating the kill network concept and evaluating and calculating the effectiveness and performance of the kill network through the construction and actual closure of the kill path, the flexibility, adaptability, and resilience of the kill network can be experimentally verified. This not only improves the resilience and adversarial nature of the kill path system, but also improves the timeliness of the kill, resource utilization, and usage efficiency.

[0238] Example 2

[0239] Please see Figure 3 , Figure 3 This is a schematic diagram of a dynamic killing path model construction device based on a kill net, as disclosed in an embodiment of the present invention. Figure 3 The described kill network-based kill path model dynamic construction device is applied to game simulation systems, such as local servers or cloud servers used in game simulation systems; however, this embodiment of the invention is not limited to such applications. Figure 3 As shown, the device may include:

[0240] Kill network element model construction module 101, mission information acquisition module 102, kill path closure model acquisition module 103, and kill path effectiveness evaluation module 104;

[0241] The kill network element model construction module 101 is used to construct a kill network element model set;

[0242] The kill network element model set includes a detection node model set, a location node model set, a tracking node model set, an aiming node model set, a game node model set, an evaluation node model set, and a kill network node communication model.

[0243] The task information acquisition module 102 is used to construct simulation scenario information based on task objective information;

[0244] The kill path closure model acquisition module 103 is used to process the kill network element model set using the simulation scenario information to obtain a dynamic closure model of the kill path.

[0245] The kill path effectiveness evaluation module 104 is used to evaluate the kill path dynamic closure model and obtain the dynamic closure kill path effectiveness evaluation result.

[0246] The device provided in this embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment for dynamically constructing a kill path model based on a kill net.

[0247] Example 3

[0248] Please see Figure 4 , Figure 4 This is a schematic diagram of another dynamic construction device for a kill path model based on a kill net, as disclosed in an embodiment of the present invention. Figure 4 The described apparatus can be applied to game simulation systems, such as local servers or cloud servers used in game simulation systems, and the embodiments of the present invention are not limited thereto. Figure 4 As shown, the device may include:

[0249] Memory 201 storing executable program code;

[0250] Processor 202 coupled to memory 201;

[0251] The processor 202 calls the executable program code stored in the memory 201 to execute the steps in the dynamic construction method of the kill path model based on the kill net described in Embodiment 1.

[0252] Example 4

[0253] This invention discloses a computer-readable storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps in the method for dynamically constructing a kill path model based on a kill net as described in Embodiment 1.

[0254] Example 5

[0255] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the method for dynamically constructing a kill path model based on a kill net as described in Embodiment 1.

[0256] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0257] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), once programmable read-only memory (OTPROM), electronically erasable rewritable read-only memory (EEPROM), read-only optical disc (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0258] It should be noted that all calculation expressions or mathematical functions in the embodiments of the present invention have undergone dimensionless processing of the variables involved before calculation.

[0259] It should be noted that in all the calculation expressions or mathematical functions in the embodiments of the present invention, the values ​​of the input independent variables all meet the reasonable requirements of the input value range of the calculation expression or mathematical function, and can ensure that the calculation expression or mathematical function can be calculated smoothly without violating physical laws or mathematical rules.

[0260] Finally, it should be noted that the method and apparatus for dynamically constructing a kill path model based on a kill net disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically constructing a kill path model based on a kill net, characterized in that, The method includes: S1, Construct the kill network element model set and the communication model between kill network nodes; The kill network element model set includes a detection node model set, a location node model set, a tracking node model set, an aiming node model set, a game node model set, and an evaluation node model set. S2, Based on the mission objective information, construct simulation scenario information; S3, using the simulation scenario information to process the kill network element model set to obtain a dynamic closed model of the kill path; S4. The dynamic closure model of the kill path is evaluated in multiple dimensions, including timeliness, scope of action, comprehensive benefits, and node influence, to obtain the dynamic closure kill path effectiveness evaluation results. The step of processing the kill network element model set using the simulation scenario information to obtain a dynamic closure model of the kill path includes: S31, Process the simulation scenario information to obtain the target detection task; S32, Based on the target detection task, the kill network element model set is processed to construct a dynamic closure model of the kill path; The step of processing the kill network element model set based on the target detection task to construct a dynamic closure model of the kill path includes: S321, the detection node model set processes the target detection task to obtain a dynamic kill path detection node model and detection object information; S322, Based on the detected object information, the location node model set is processed using the communication model between the kill network nodes to obtain the dynamic kill path location node model and target location result information; S323, Based on the target location result information, the tracking node model set is processed using the communication model between the kill network nodes to obtain the dynamic kill path tracking node model and target tracking result information; S324, Based on the target tracking result information, the aiming node model set is processed using the kill network node communication model to obtain the dynamic kill path aiming node model and target attack command information; S325, Based on the target attack instruction information, the game node model set is confirmed using the kill network node communication model to obtain the dynamic kill path game node model and target attack result information. S326, Based on the target attack result information, the evaluation node model set is processed to obtain the dynamic kill path evaluation node model and target evaluation result information; S327, according to the dynamic closure rule of the kill path of "discovery, location, tracking, aiming, engagement, and evaluation", the dynamic kill path detection node model, the dynamic kill path location node model, the dynamic kill path tracking node model, the dynamic kill path aiming node model, the dynamic kill path game node model, the dynamic kill path evaluation node model, and the kill network node communication model are combined to obtain the kill path dynamic closure model; The evaluation process of the dynamic closure model of the kill path to obtain the dynamic closure kill path effectiveness evaluation result includes: S41, Obtain the kill path action effect information of the kill path dynamic closure model; S42, using the kill network timeliness analysis model, process the kill path action effect information to obtain kill network timeliness result information; The expression for the kill network timeliness analysis model is as follows: ， ， Where T represents the kill network closure time; i represents the index of the kill path dynamic closure model; and n represents the number of kill path dynamic closure models in the kill network. This represents the closing time of the dynamic closing model of the i-th kill path; This indicates the moment when the game node of the i-th dynamic closure model of the kill path completes its attack; This indicates the moment when the i-th dynamic closure model of the kill path detects the target object; S43, Based on the kill network scope model, the kill path action effect information is processed to obtain kill network scope information; The expression for the effective range model of the kill net is: ， in, This represents the distance between the aiming node and the game object of the game node in the i-th dynamic closure model of the kill path; S44, Based on the kill network benefit analysis model, the kill path action effect information is processed to obtain kill network benefit information; The expression for the kill net effectiveness analysis model is as follows: ， Where E represents the damage network benefit; M represents the number of damaged targets in the kill network; and m represents the index of the damaged target object in the kill network. This represents the value of the m-th damaged target object; The damage level of the m-th damaged target object is indicated; Q represents the amount of resources consumed in the kill net; q represents the index of the consumed resources. This represents the value of the q-th resource consumed; S45, Based on the kill network element node influence analysis model, the kill network benefit information of the kill network is processed to obtain the kill network element node influence information; S46, integrate the kill network timeliness result information, the kill network range information, the kill network benefit information, and the kill network element node influence information to obtain the dynamic closed kill path effectiveness evaluation result.

2. The method for dynamically constructing a kill path model based on a kill net according to claim 1, characterized in that, The detection node model set processes the target detection task to obtain a dynamic kill path detection node model and detection object information, including: S3211, Obtain the target detection task; S3212, Based on the target detection task, obtain detection capability requirement information; S3213, Based on the detection capability requirement information, match the detection node model set to obtain a dynamic kill path detection node model; The detection node model set includes several detection node models; S3214, the dynamic kill path detection node model detects the detection area and obtains the detection object information.

3. The method for dynamically constructing a kill path model based on a kill net according to claim 1, characterized in that, Based on the detected object information, the location node model set is processed using the kill network node communication model to obtain dynamic kill path location node models and target location result information, including: S3221, Based on the detected object information, obtain the positioning requirement information; S3222, The kill network node communication model publishes the positioning request information to the positioning node model; The positioning requirement information includes the location information of the positioning object, the environmental area information of the positioning object, and the constraint information of the positioning object; S3223, the positioning node model set processes the positioning requirement information to obtain a positioning result information set; The location node model set includes several location node models; The location result information set includes several location result information items; The positioning result information includes positioning time information, positioning accuracy information, and positioning cost information; S3224, The positioning result information set is processed using the positioning node optimization model to obtain the dynamic kill path positioning node model; The expression for the preferred positioning node model is: Where l represents the index of the location node model; This represents the overall benefit of the l-th positioning node model; This indicates the location completion time of the l-th location node model; This represents the optimal positioning accuracy of the l-th positioning node model; This represents the minimum resources required for the l-th location node model; S3225, Based on the dynamic kill path positioning node model, match the positioning result information set to obtain target positioning result information.

4. The method for dynamically constructing a kill path model based on a kill net according to claim 1, characterized in that, Based on the target localization result information, the tracking node model set is processed using the kill network node communication model to obtain dynamic kill path tracking node models and target tracking result information, including: S3231, Based on the target positioning result information, obtain tracking requirement information; The tracking requirement information includes tracking area information, tracking spatial information, tracking time information, and tracking accuracy information; S3232, the tracking node model set acquires and processes the tracking requirement information to obtain a tracking result information set; The tracking result information set includes several tracking result information items; The tracking result information includes the fastest tracking time, tracking accuracy, trackable time, and resource consumption. S3233, The tracking result information set is processed using the tracking benefit calculation model to obtain the tracking benefit set; S3234, Based on the tracking benefit set and the tracking result information set, determine the dynamic kill path tracking node model; S3235, Based on the dynamic kill path tracking node model, match the tracking result information set to obtain target tracking result information.

5. A dynamic construction device for kill path model based on kill net, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the dynamic construction method of kill path model based on kill net as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the dynamic construction method for kill path model based on kill net as described in any one of claims 1 to 4.

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