CN114329928A - Modular assembly and overall parameter rapid generation method of equipment model - Google Patents
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Abstract
A modular assembly and overall parameter rapid generation method of an equipment model is based on a gene-like map representation recombination technology and a modular theory, under the condition that a battlefield environment is complex and battlefield tasks are changeable, modular assembly and overall parameter rapid generation methods of the equipment model are established by building block modular equipment modeling and constructing a maximum likelihood model to fit multi-source gene recombination data of the equipment, the problems that a traditional modeling method is poor in reusability, modules are not universal and the equipment is difficult to adapt to rapid upgrading and updating are solved, and a more friendly and flexible modeling method is provided.
Description
Technical Field
The invention relates to a modular assembly and overall parameter rapid generation method of an equipment model, and belongs to the technical field of aerospace equipment modeling intelligence.
Background
In the prior art, a united XX scheme deduction and evaluation technical framework based on a monte carlo method comprises a deduction method and an effectiveness analysis and evaluation method, but does not reflect modular assembly of equipment; according to the research of a certain evaluation method based on simulation deduction, a sea warfare deduction evaluation index system is constructed according to the simulation deduction process, factors are mainly extracted from different stages, and modular assembly of equipment is not reflected; in addition, the integrated integration method of the combat deduction simulation and the efficiency evaluation in the prior art realizes the iterative evolution of the whole-flow design and evaluation of 'combat plan design-deduction simulation-data analysis and efficiency evaluation', and is biased to the top layer and does not reflect the modular assembly of equipment. Comprehensive analysis shows that the equipment modularization assembly plays a fundamental role in system scene modeling and simulation deduction, and is still a short board in the prior art.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method comprises the steps of building block modular equipment modeling and constructing a maximum likelihood model to fit equipment multi-source gene recombination data based on a gene-like map representation recombination technology and a modular theory under the conditions of complex battlefield environment and variable battlefield tasks, and establishing the method for modular assembly and rapid generation of the overall parameters of the equipment model.
The purpose of the invention is realized by the following technical scheme:
a modular assembly and overall parameter rapid generation method of an equipment model comprises the following steps:
treating each equipment as a unit, each unit comprising a vehicle and at least one load; the carrier is used for representing the motion characteristics of the equipment; different loads adopt different parameters to represent the capacity model according to the load types;
setting the damage capability, detection capability and motion characteristic of the equipment, and carrying out randomization generation on the confrontation parameters of the scene and the equipment;
constructing an intelligent equipment gene characterization model with knowledge graph and graph theory characteristics;
defining an equipment module unit through end point distribution and position relation, and representing equipment composition forms by a visual gene map method through configuration and calculation processing of end point attributes;
through the configuration and customization of the associated lines, the endpoints are combined into a regional graph,
the graph structure establishes a characterization scheme of a hardware integration prototype in the form of a topological network;
the visual representation and intelligent matching of the single machine attribute characteristics, the single machine function connection mode, the single machine and system coupling mechanism and the multi-element topological spectrum composition are realized through a graph structure and an information flow mode;
taking a reinforcement learning intelligent model as a frame, taking strong enemy equipment combat indexes generated by a battlefield as confrontation targets, simulating gene mutation and gene recombination behaviors generated in a biological competitive selection process, and carrying out equipment gene self-organization exchange and intelligent combination in a game deduction scene;
the modularized theoretical model adopts a diffusion construction method, and evaluation feedback of modularized theoretical evolution is given through confidence interval detection of multi-degree distribution, goodness-of-fit detection of multi-degree distribution and equipment diversity index detection in each game iteration process and serves as an indicative index of each round of evolutionary game tactical upgrading.
In an embodiment of the invention, the motion characteristics of the equipment at least comprise position, posture, speed and quality.
In one embodiment of the invention, an intelligent equipment gene characterization model with knowledge graph and graph theory characteristics is constructed, and the corresponding relation is as follows:
end points (gene DNA — equipment base module).
In one embodiment of the present invention, the equipment composition morphology is characterized by a visualized gene map method, specifically:
a correlation line (gene fragment) is an equipment unit interface.
In one embodiment of the invention, a modular theoretical model adopts a diffusion construction method, the influence of forced evolution factors of the environment is weakened, and zero and polynomial distribution of multi-degree distribution integrates Fisher logarithmic series distribution and Preston logarithmic normal distribution, namely in a regional assembled community (region), species-multi-degree relation accords with logarithmic series distribution; whereas within a local (local) community, the species-polytope relationship approaches a log-normal distribution.
Compared with the prior art, the invention has the following beneficial effects:
aiming at the problems of poor reusability and non-universality of modules of the traditional modeling method, the invention establishes the rapid modeling integration method of the equipment, solves the problem of modular assembly of the equipment model, and provides a more friendly and flexible modeling method for users. And a maximum likelihood model is constructed to fit the module construction data of the equipment, so that the understandability, robustness and predictability of the new concept equipment generation process are realized. The achievement can support the rapid closed-loop demonstration of the overall parameters of the novel aircraft adapting to complex battlefield environments and tasks, and can be popularized and applied to various tasks such as existing model transformation and upgrading.
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FIG. 1 is a block diagram of the present invention;
FIG. 2 is a component diagram of the equipment model of the present invention;
FIG. 3 is an assembled view of the physical assembly of the present invention;
FIG. 4 is a schematic diagram of a modular theoretical diffusion model.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Based on the gene-like map representation recombination technology and the modularization theory, under the condition that the battlefield environment is complex and the battlefield mission is changeable, building block modularization equipment modeling and building a maximum likelihood model are used for fitting multisource gene recombination data of the equipment, and establishing a modularization assembly and overall parameter rapid generation method of the equipment model.
In a specific implementation method, a method for modular assembly and rapid generation of overall parameters of an equipment model is divided into:
the building block modular equipment modeling method is based on a carrier and a load basic module, and realizes quick modeling integration of equipment through multi-module combined configuration. And constructing a maximum likelihood model to fit the module construction data of the equipment, and calculating to obtain corresponding basic multi-style indexes and mobility which are used as guide information of the equipment variation evolution rate in each deduction process, so that the rapid generation of the overall parameters of the new concept equipment is realized.
Specifically, the steps of the invention are as follows:
as shown in fig. 1, fig. 2, fig. 3 and fig. 4, a method for modular assembly and rapid generation of overall parameters of an equipment model includes the following steps:
(1) fig. 1 shows the main equipment model composition, the carrier includes 7 modules, the probe load includes 6 modules, the weapon load includes 6 modules, and the communication load includes 2 modules, each module using a class to implement its respective function.
(2) For system combat applications with various combat styles, equipment models are numerous in types and large in quantity, so that the construction of an equipment model system based on countermeasure learning needs to be carried out according to a classification and hierarchical method. The specific application mode of the equipment model in the combined operation is comprehensively considered, and the basic requirements of the model on universality, reusability, easy management and interactivity are taken from the aspect of object-oriented.
(3) Each equipment is a Unit and can be assembled by a carrier and a load, the carrier mainly represents the motion characteristics of the equipment, including dynamic parameters such as position, attitude, speed, mass and the like, the load comprises a detection load, a communication load and a weapon load, the equipment is composed as shown in figure 2, and the entity assembly is shown in figure 3. According to the characteristics of different load types, different parameters are adopted to represent the damage capability, detection capability and other capability models of the equipment. The carrier has one and only one carrier per Unit, but can be configured with multiple types and multiple loads.
The visual rapid equipment modeling adopts a normalized representation method, a new concept equipment model is rapidly constructed by utilizing a carrier and a load module, and the performance parameters of the equipment are edited and set. The performance parameters comprise four attributes of tasks, roles, capabilities and agents, and the tasks determine the target and constraint conditions for constructing the equipment model; the role is a bridge for establishing a relationship between the task and the intelligent agent; the capability is to further distinguish roles, the capability and the roles have many-to-many mapping relation, and the same role can have different capability attributes; an agent is the physical carrier of roles and capabilities.
(3.1), task attribute: when describing tasks/events, 5W description is generally adopted, namely, when, where, who people, what actions and why reasons, the task attributes of the project need to be considered on the basis of 5W, such as level, priority and other factors. The task should have a hierarchical attribute, such as a strategic level, a battle level or a tactical level, which represents the decomposition granularity of the task, the same role may be allocated with a plurality of tasks, at this time, a conflict may occur during execution, a priority is required to represent the importance degree of the task, and data support is provided for the equipment model to decide the execution sequence of the task. In order to evaluate the degree of execution of the current task to facilitate task re-planning, the degree of execution is required to characterize the completion of the task, and is part of the task attribute, during the task execution process, the execution of some tasks must be performed on the basis of the completion of some tasks, and here, the task attribute is characterized by using the pre-task, and based on the above factors, the task attribute can be characterized as { basic attribute, task execution attribute, task relation attribute, state, extended }.
(3.2), role attributes: the role needs to be started according to certain conditions, then corresponding tasks are executed according to environment information obtained by perception, friend or enemy information obtained by interaction, and predicted information and self capacity of the friend or enemy information, and finally the role is closed according to conditions such as the execution condition or environment of the tasks, so that the role is invalid, and the role switching is realized. Communication is the basis of information interaction in the battle process, and the abnormity of communication can influence the establishment of the cooperative relationship and the information acquisition, so the role attribute can be characterized as { basic attribute, capability, task ID list, knowledge set, environment, communication, extended }.
(3.3), capability attribute: the distribution of equipment model tasks and the establishment of the relationship between the roles need to complete the distribution of the tasks to the roles according to the capability attributes of the equipment models and the capabilities of the roles, so the capability description requirements of the fighting equipment need to be analyzed, and the capability attributes are established to describe how the tasks can be realized in the current environment and the change of the tasks to the current environment. In order to characterize the starting and ending of the capability, the triggering condition and the ending condition are required to be used for representing the implementation process of the capability, the performance parameter of the execution process is required to be in the capability attribute, so the triggering condition, the ending condition and the execution process are used for characterizing, the basic attribute and the capability value of the capability are added on the basis, and the capability attribute can be characterized as { ID, name, type, capability value, triggering condition, execution process, postcondition and extended }.
(3.3), agent attributes: the role needs to be played by the intelligent agent to realize the execution of various tasks, the role activation or the relationship establishment needs to be judged according to performance parameters such as the capability, resources and motion states of the intelligent agent, and the role interaction needs to be completed through interaction interfaces such as the receiving and sending of information of the intelligent agent. In addition, the intelligent agent also has basic attributes such as relationship, state, resource, decision possibility and the like when the task is completed, and performance parameters such as activation condition and failure condition representing the life cycle of the intelligent agent, and meanwhile, the relationship with the role is established in system battle, and based on the above factors, the attributes of the intelligent agent can be represented as { basic attribute, relationship, capability ID list, role, resource, motion, interaction and extended }.
(4) The method comprises the steps of providing a two-dimensional visual scene scenario editing and equipment model parameter editing interface through a user configuration module, realizing battlefield entity deployment based on a battlefield map, and supporting battlefield environment configuration, battle plan and battle task designation, visual path planning, scenario icon customization, generation, storage and loading of scenario files and the like. The visual VDL editing environment is provided, the visual operation based on the dragging mode is supported, the existing simulation scenario can be input by a user, and then the performance parameters of the equipment are modified and edited according to the requirements of the user. Performance parameters include damage capability (range, probability), detection capability (mode, range, accuracy, duration), and motion characteristics (speed, altitude, trajectory). Meanwhile, scene assumptions (such as bases, attack cities, arrangement unit positions, radar detection radiuses and interception radiuses) and equipment parameters (such as RCS (radar cross section), interference effects and cheating effects) can be randomly generated, and various uncertainties in battle scenes can be fully covered.
(5) And realizing rapid generation of overall parameters by an equipment configuration intelligent design technology of gene-like map representation/recombination.
(5.1) the equipment development idea is converted from a strong individual form with rich component elements, high integration level and fine functions of a single hardware system into a strong combined form with simple components, flexible interfaces and rich functional topological structures of the single hardware system, namely, the low-cost equipment functions are quickly increased, decreased and assembled. Considering that biological genes have similar trait characterization capability in different individuals (the gene sequence is overall stable but can be changed, the implicit genes correspond to biological explicit characteristics, and the explicit characteristics do not significantly change along with individual differences), an intelligent equipment gene characterization model with knowledge graph and graph theory characteristics is constructed, and the correspondence relationship is as follows:
endpoint (gene DNA is the basic module of the equipment)
And (5.2) end points in the graph structure are the basis for forming information flow, the distribution state of the end points can reflect the sequencing situation of the gene DNA, the end points are mapped into the equipment model, equipment module units can be defined through the end point distribution and position relation, and the equipment composition form is characterized by a visualized gene graph method through the configuration and calculation processing of end point attributes.
Correlation line (gene fragment as equipment unit interface)
(5.3) the associated lines in the graph structure represent the transmission relationship between information flows, the endpoints are combined into a regional graph, namely gene segments in the genes, and the adjustment and recombination of a plurality of segments can be realized through the configuration and the customization of the associated lines, and in the configuration of the equipment scheme, the connecting line can be used as a general interface unit such as a middleware and the like.
Topological network (gene sequence as hardware integration prototype)
And (5.4) finally establishing a characterization scheme of a hardware integration prototype in a topological network form by the graph structure, and carrying out efficiency evaluation on the topological configuration scheme, wherein the modular theory evolution game is the reconstruction and the re-optimization of the graph topological structure.
And (5.5) the visual representation and intelligent distribution of the single machine attribute characteristics, the single machine function connection mode, the single machine and system coupling mechanism and the multi-element topological spectrum composition can be realized through the graph structure and the information flow mode, and support is provided for the flexible recombination of equipment modularization.
(6) The intelligent design module takes a reinforcement learning intelligent model with the evolution characteristic of a 'modularization theory' as a framework, takes the competitive index of a strong enemy equipment generated in a battlefield as a confrontation target (environmental feedback), simulates the behaviors of gene mutation, gene recombination and the like (intelligent generation of a hardware configuration scheme) in the process of biological 'physical competition and natural selection', and performs equipment gene self-organization exchange and intelligent combination in a game deduction scene.
(7) As shown in fig. 4, unlike the conventional ecological niche-based evolutionary computation model, the modular theoretical model of the present invention employs a diffusion construction method to weaken the influence of forced evolution factors of the environment, and the zero and polynomial distributions of the multi-degree distribution thereof integrate Fisher logarithmic series distribution and Preston logarithmic normal distribution, that is, in a regional assembled community (region), species-multi-degree relationship conforms to the logarithmic series distribution; whereas within a local (local) community, the relationship is close to a log-normal distribution. The device gene is characterized in a community-like mode, and the mathematical characterization of the modular theory evolution of the invention is as follows:
wherein the content of the first and second substances,
graph structure polynomial states representing the number of M equipment genes in n iterations, M being the number of individual equipment genes in the graph structure, SMIs Fisher logarithmic progression distribution vector of M equipment gene numbers, P0The method is characterized in that the method comprises the following steps of (1) a Preston lognormal distribution vector, b is the average generation rate of new concept equipment, d is the average elimination rate of the new concept equipment, theta is a simplification coefficient, and n is the iteration number. The polynomial state of the graph structure is closely related to the iteration rate of the components of the equipment modules in the system countermeasure process, and the equipment gene evolutionary distribution based on the modularization theory is generated in the graph structure.
(8) On the basis, in each game iteration process, evaluation feedback of modular theoretical evolution is given through confidence interval detection of multi-degree distribution, goodness-of-fit detection of multi-degree distribution and equipment diversity index detection, and is used as an indicative index of the battle force upgrading of each round of evolutionary game, so that after new concept weapons of strong enemies are sensed and identified, the hardware prototype scheme can be quickly generated (overall parameters, principle patterns, scene initial models and the like) and the deficiency completion of key hardware units can be realized.
The invention innovatively provides a building block modular equipment modeling method, solves the problems that the traditional modeling method is poor in reusability, the modules are not universal, and the equipment is difficult to adapt to rapid upgrading and upgrading, realizes rapid modeling integration of the equipment through multi-module combined configuration based on a carrier and a load basic module, and provides a more friendly and flexible modeling method for users.
According to the method, a maximum likelihood model is creatively built according to a modular theory to fit the module building data of the equipment, and corresponding basic multi-style index and mobility are obtained through calculation and serve as guide information of the equipment variation evolution rate in each deduction process, so that the forming mode of the new concept equipment conforms to the system evolution rule, and the understandability, the robustness and the predictability of the new concept equipment generation process are realized.
Those skilled in the art will appreciate that those matters not described in detail in the present specification are well known in the art.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.
Claims (6)
1. A modular assembly and overall parameter rapid generation method of an equipment model is characterized by comprising the following steps:
treating each equipment as a unit, each unit comprising a vehicle and at least one load; the carrier is used for representing the motion characteristics of the equipment; different loads adopt different parameters to represent the capacity model according to the load types;
setting the damage capability, detection capability and motion characteristic of the equipment, and carrying out randomization generation on the confrontation parameters of the scene and the equipment;
constructing an intelligent equipment gene characterization model with knowledge graph and graph theory characteristics;
defining an equipment module unit through end point distribution and position relation, and representing equipment composition forms by a visual gene map method through configuration and calculation processing of end point attributes;
through the configuration and customization of the associated lines, the endpoints are combined into a regional graph,
the graph structure establishes a characterization scheme of a hardware integration prototype in the form of a topological network;
the visual representation and intelligent matching of the single machine attribute characteristics, the single machine function connection mode, the single machine and system coupling mechanism and the multi-element topological spectrum composition are realized through a graph structure and an information flow mode;
taking a reinforcement learning intelligent model as a frame, taking strong enemy equipment combat indexes generated by a battlefield as confrontation targets, simulating gene mutation and gene recombination behaviors generated in a biological competitive selection process, and carrying out equipment gene self-organization exchange and intelligent combination in a game deduction scene;
the modularized theoretical model adopts a diffusion construction method, and evaluation feedback of modularized theoretical evolution is given through confidence interval detection of multi-degree distribution, goodness-of-fit detection of multi-degree distribution and equipment diversity index detection in each game iteration process and serves as an indicative index of each round of evolutionary game tactical upgrading.
2. The modular assembly and rapid generation of overall parameters method according to claim 1, characterized in that the motion characteristics of the equipment comprise at least position, attitude, speed, mass.
3. The modular assembly and rapid generation method of general parameters according to claim 1, characterized in that an intelligent equipment gene characterization model with knowledge graph and graph theory characteristics is constructed, and the corresponding relations are as follows:
end points (gene DNA — equipment base module).
4. The modular assembly and rapid generation method of overall parameters according to claim 1, characterized in that the equipment composition morphology is characterized by a visualized genetic map method, specifically:
a correlation line (gene fragment) is an equipment unit interface.
5. The modular assembly and rapid generation method of overall parameters according to claim 1, characterized in that the modular theoretical model adopts a diffusion construction method, the forced evolution factor influence of the weakened environment, the zero and polynomial distributions of the multi-degree distribution integrate the Fisher logarithmic series distribution and the Preston logarithmic normal distribution, i.e. in the region assembly community (region), the species-multi-degree relationship conforms to the logarithmic series distribution; whereas within a local (local) community, the species-polytope relationship approaches a log-normal distribution.
6. A computer readable storage medium having stored thereon computer program instructions which, when loaded and executed by a processor, cause the processor to perform the method of any of claims 1 to 5.
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