Modeling method for rapid recombination operation mechanism of electromagnetic system

Abstract

The invention discloses an electromagnetic system rapid recombination operation mechanism modeling method, and belongs to the technical field of electromagnetic system recombination. The invention aims to solve the problem that the equipment unit of the existing electromagnetic system cannot be quickly and flexibly recombined. Comprising the steps that according to model description files of different hierarchies, a component-level model, a part-level model and a platform-level model are adopted to build a plurality of modularized digital element models; physical response characteristics, multi-resolution modeling and behavior modeling of the digital element model are configured; performing verification, verification and iterative optimization; performing task configuration on the optimized digital element model according to a task scene to obtain an electromagnetic system model; according to a task target, an electromagnetic system model is selected, then the selected electromagnetic system model is rapidly recombined based on the optimized digital element model, and a recombined electromagnetic system model is obtained; and performing performance evaluation on the recombined electromagnetic system model until the recombined electromagnetic system model meets a task target requirement. Rapid recombination of the electromagnetic system is realized.

Description

Technical Field

[0001] This invention relates to a modeling method for the rapid reconfiguration and operation mechanism of electromagnetic systems, belonging to the field of electromagnetic system reconfiguration technology. Background Technology

[0002] In dynamic deployment missions in complex environments, communication and information systems need to have rapid reconfiguration capabilities to accurately perceive the signal environment of the target area during air movement, maintain stable communication during the landing phase of personnel and equipment, and effectively support relevant signal activities in the subsequent ground operation phase.

[0003] Research on rapid reconfiguration mechanisms must adapt to the continuous development of information technology and the changing application needs, while also meeting the differentiated requirements of various task scenarios. Currently, system module modeling techniques for fixed or ground-based deployment scenarios are relatively mature, but modeling methods for achieving rapid and flexible reconfiguration of equipment units in dynamically deployed tasks still require further in-depth exploration. Summary of the Invention

[0004] To address the problem that existing electromagnetic systems cannot achieve rapid and flexible reconfiguration of equipment units, this invention provides a modeling method for a rapid reconfiguration operation mechanism of electromagnetic systems.

[0005] The present invention provides a method for modeling the rapid reconfiguration mechanism of an electromagnetic system, comprising:

[0006] Based on model description files at different levels, multiple component-level models, part-level models, and platform-level models are built. Among them, the component-level model is the smallest model unit with basic functions. The part-level model is assembled based on the component-level model, and the platform-level model is assembled based on the part-level model.

[0007] Feature modeling is performed on the digital meta-model to configure its physical response features; then multi-resolution modeling and behavioral modeling are performed to obtain the configured digital meta-model.

[0008] The configured digital meta-model is verified, validated, and iteratively optimized to obtain the optimized digital meta-model.

[0009] The optimized digital meta-model is configured according to the task scenario to obtain the electromagnetic system model; and the electromagnetic system model is managed.

[0010] Based on the mission objectives, an electromagnetic system model is selected, and then the selected electromagnetic system model is rapidly recombined based on the optimized digital meta-model to obtain a recombined electromagnetic system model. The performance of the recombined electromagnetic system model is evaluated until it meets the mission objective requirements.

[0011] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the component-level model includes a transmitter model, a receiver model, an antenna model, and a processor model;

[0012] The component-level models include radar models, communication models, electronic countermeasures models, optoelectronic models, and motion models;

[0013] The platform-level models include land-based platform models, sea-based platform models, air-based platform models, space-based platform models, and weapon platform models;

[0014] The digital meta-model possesses task capabilities through the interaction between the processor models of each level of the model and the data bus.

[0015] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the physical response characteristics include the radar cross-section, infrared radiation intensity and optical contrast of the digital meta-model, which are obtained by typical model calculation method, table lookup method or simulation table lookup method.

[0016] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the digital meta-model is modeled in multiple resolutions, including coarse-grained system parameter model, medium-grained pulse data model, fine-grained baseband signal model and fine-grained intermediate frequency signal model; the four granularities of the model are automatically adapted to the functional level simulation of different granularities according to the simulation step size.

[0017] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the behavior modeling of the digital meta-model includes configuring a behavior processing component for the digital meta-model to receive decision instructions, decompose them into action sequences of component-level models and component-level models, and send them to the action components of the component-level models and component-level models, so that the action components control the corresponding component-level models and component-level models to complete the decision instructions.

[0018] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the decision instructions include task planning initialization phase instructions for the digital meta-model, automatic action planning instructions during the operation of the digital meta-model, and task action planning instructions during the operation of the digital meta-model.

[0019] The digital meta-model executes decision instructions based on a first-come, first-served principle.

[0020] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the verification, validation and iterative optimization of the configured digital meta-model includes the confirmation, validation and identification of the configured digital meta-model;

[0021] The verification includes verification of the validity of the theoretical model, verification of the validity of the data, and verification of the validity of the operation; by comparing the consistency between the output of the simulated theoretical model and the configured digital meta-model under the same input conditions and operating environment, the credibility and usability of the configured digital meta-model are evaluated.

[0022] The verification includes implementing the configured digital meta-model using a computer program, and verifying the consistency between the computer program implementation result and the configured digital meta-model.

[0023] The determination is based on the confirmation and verification of the digital meta-model after the current configuration.

[0024] The electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention includes subjective method, graphical method and mathematical statistics method for confirming, verifying and identifying the configured digital meta-model.

[0025] The subjective method involves domain experts distinguishing the output of the configured digital meta-model from the output of the corresponding real model until they can no longer be distinguished, thus determining the final configured digital meta-model.

[0026] The graphical method lists the output of the configured digital meta-model and the output of the corresponding real model in a one-to-one correspondence form as a table or a characteristic curve until the error of the corresponding table or the corresponding characteristic curve is within a set threshold range, and then determines the final configured digital meta-model.

[0027] The mathematical statistics method uses confidence interval method, hypothesis testing method or window spectrum estimation method to compare the consistency of the configured digital meta-model with the parameters, sample distribution and sample statistics of the corresponding actual equipment until the set target is met, and the final configured digital meta-model is determined.

[0028] For the configured digital meta-model determined by the subjective method or the graphical method, the mathematical statistics method is used to continue to judge until the set target is met and the required fidelity of the configured digital meta-model is achieved, thus obtaining the optimized digital meta-model.

[0029] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the management of the electromagnetic system model includes adding, deleting, modifying, querying, importing or exporting the optimized digital meta-model, as well as performing compliance checks on the optimized digital meta-model.

[0030] According to the electromagnetic system rapid reconfiguration operation mechanism modeling method of the present invention, the performance evaluation of the reconfigured electromagnetic system model includes early warning detection capability evaluation, intelligence processing capability evaluation, auxiliary decision-making capability evaluation, and collaborative task capability evaluation.

[0031] The early warning and detection capability assessment is an effectiveness evaluation of the target detection capability, target localization capability, and target tracking capability of the recombined electromagnetic system model for single nodes.

[0032] The intelligence processing capability assessment is an evaluation of the intelligence transmission capability and distributed computing capability of the recombined electromagnetic system model for single nodes.

[0033] The assessment of auxiliary decision-making capabilities includes evaluating the situation sharing and reinforcement learning capabilities of the recombined electromagnetic system model.

[0034] The collaborative task capability assessment involves evaluating the reaction reorganization capability and module survivability of each optimized digital element model in the recombined electromagnetic system model.

[0035] The beneficial effects of the present invention are as follows: The method of the present invention can respond to changes in the task environment and task scenario, and on the basis of making full use of existing platform and component resources, it can quickly form an electromagnetic system that adapts to the task requirements by appropriately adjusting or changing the system configuration.

[0036] The optimized recombination achieved by the method of this invention has the following advantages:

[0037] The platform and component system can be rapidly configured to adapt to changes in mission scenarios and environments, and the system configuration scheme conforms to the principle of maximizing mission benefits. It makes full use of existing resources and plans, combines, and optimizes various types of individual components in a targeted manner to tactically compensate for performance deficiencies. By using mission process simulation and effect evaluation, the electromagnetic system configuration is continuously iterated, so that the recombined output system improves performance and meets the requirements for mission execution. It also meets the conditions of system reconfigurability, flexible principles, and physical configuration of the simulation system according to rules, and can be evaluated and decided based on benefits.

[0038] The method of this invention enables electromagnetic systems to achieve maximum flexibility, realize technological upgrades to achieve sustainable development, and fundamentally improve efficiency and reduce manufacturing costs. Attached Figure Description

[0039] Figure 1 This is the overall flowchart of the modeling method for the rapid reconfiguration operation mechanism of the electromagnetic system described in this invention;

[0040] Figure 2 It is a flowchart for confirming, verifying, and verifying the configured digital meta-model;

[0041] Figure 3 This is a schematic diagram of the component-based design of the digital meta-model;

[0042] Figure 4 This is a schematic diagram of feature modeling of the digital meta-model;

[0043] Figure 5 This is a schematic diagram of multi-resolution modeling of the digital meta-model;

[0044] Figure 6 This is a schematic diagram of the behavior processing method for digital meta-models;

[0045] Figure 7 It is the structural design diagram of the platform-level model library;

[0046] Figure 8 It is the structural design diagram of the component-level model library;

[0047] Figure 9 It is the structural design diagram of the component-level model library;

[0048] Figure 10 This is a typical interface diagram of a digital metamodel management program;

[0049] Figure 11 This is a schematic diagram for evaluating the performance of the reconstructed electromagnetic system model. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0051] Specific Implementation Method 1: Combination Figures 1 to 11 As shown, this invention provides a method for modeling the rapid reconfiguration mechanism of an electromagnetic system, including:

[0052] Based on model description files at different levels, multiple component-level models, part-level models, and platform-level models are built. Among them, the component-level model is the smallest model unit with basic functions. The part-level model is assembled based on the component-level model, and the platform-level model is assembled based on the part-level model.

[0053] Feature modeling is performed on the digital meta-model to configure its physical response features; then multi-resolution modeling and behavioral modeling are performed to obtain the configured digital meta-model.

[0054] The configured digital meta-model is verified, validated, and iteratively optimized to obtain the optimized digital meta-model.

[0055] The optimized digital meta-model is configured according to the task scenario to obtain the electromagnetic system model; and the electromagnetic system model is managed.

[0056] Based on the mission objectives, an electromagnetic system model is selected, and then the selected electromagnetic system model is rapidly recombined based on the optimized digital meta-model to obtain a recombined electromagnetic system model. The performance of the recombined electromagnetic system model is evaluated until it meets the mission objective requirements.

[0057] The overall technical architecture of this embodiment is as follows: Figure 1 As shown, by using component-based and plug-in-based modeling, a meta-model library based on meta-components is established to realize the changes and innovations in the configuration of electromagnetic platforms and component models.

[0058] This implementation achieves component-based modeling of the digital meta-model. Based on model description files at different levels, the digital meta-model establishes platform and component models respectively, and assembles them into a simulation digital platform. Specifically, the simulation digital platform selects corresponding components based on the model description files, and the components then select corresponding components and algorithms based on the description data, executing a platform-component-component-algorithm search process, and then reversing this process to complete the algorithm-component-component-platform assembly process.

[0059] The modular modeling approach enables the rapid construction of various general-purpose platforms and components based on modular templates. It can cover component types such as platforms, radar, reconnaissance, jamming, optoelectronics, communication, data links, navigation, identification, guidance, and behavior processing, as well as component types such as transmitters, receivers, processors, antennas, and behaviors.

[0060] As an example, combined Figures 7 to 9 As shown, the component-level model includes a transmitter model, a receiver model, an antenna model, and a processor model;

[0061] The component-level models include radar models, communication models, electronic countermeasures models, optoelectronic models, and motion models;

[0062] The platform-level models include land-based platform models, sea-based platform models, air-based platform models, space-based platform models, and weapon platform models;

[0063] The digital meta-model possesses task capabilities through the interaction between the processor models of each level of the model and the data bus.

[0064] Combination Figure 4 As shown, the physical response features include the radar cross-section (RCS), infrared radiation intensity (TCS), and optical contrast ratio (OCS) of the digital meta-model, used for rapid calculation and representation of the three features of the digital platform. Feature values ​​for a specified angle can be obtained through linear fitting using typical model calculation methods, lookup table methods, or simulation lookup table methods.

[0065] Combination Figure 5As shown, multi-resolution modeling of the digital meta-model includes coarse-grained system parameter model, medium-grained pulse data model, fine-grained baseband signal model, and fine-grained intermediate frequency signal model; the four granularities of the model are automatically adapted to the functional level simulation of different granularities according to the simulation step size.

[0066] This step involves consistently describing the same system, architecture, or process at different resolutions or levels of abstraction. Typical electronic platform and component resolution modeling can be divided into two types: functional-level modeling and signal-level modeling, which can be further decomposed into the four granularity modeling methods mentioned above.

[0067] The digital meta-model has multi-resolution modeling capabilities with different granularities. It can automatically adapt to two functional levels of simulation, coarse-grained and medium-grained, based on the simulation step size. In addition, electronic device models all support signal waveform description methods, and users can edit and generate signal description files for each model's corresponding working mode.

[0068] Combination Figure 6 As shown, behavioral modeling of the digital meta-model includes configuring a behavioral processing component for the digital meta-model to receive decision instructions, decompose them into action sequences of component-level models and component-level models, and send them to the action components of the component-level models and component-level models. The action components then control the corresponding component-level models and component-level models to complete the decision instructions.

[0069] This step involves modeling the behavior or performance of the human being that needs to be represented in adversarial simulation. A behavior processing component is added to the digital meta-model. This component acts as the brain of the digital platform. It receives decision-making instructions from the commander / pilot / agent and decomposes complex behavioral decision-making instructions into action sequences that each component / assembly can recognize. Then, it distributes the action sequences to each action component, which controls each model to collaboratively complete the platform's predetermined decision-making instructions according to the instructions.

[0070] The digital platform behavior pattern mainly defines the set of behaviors that the platform can respond to, and decomposes each behavior into a sequence of actions that the model can recognize based on the task. Action event triggering includes three methods: time, time and location triggering.

[0071] Furthermore, the decision instructions include instructions for the task planning initialization phase of the digital meta-model, instructions for automatic action planning during the operation of the digital meta-model, and instructions for task action planning during the operation of the digital meta-model.

[0072] The digital meta-model executes decision instructions based on a first-come, first-served principle.

[0073] Combination Figure 2 As shown, the verification, validation, and iterative optimization of the configured digital meta-model includes confirming, validating, and identifying the configured digital meta-model.

[0074] The verification includes verification of the validity of the theoretical model, verification of the validity of the data, and verification of the validity of the operation; by comparing the consistency between the output of the simulated theoretical model and the configured digital meta-model under the same input conditions and operating environment, the credibility and usability of the configured digital meta-model are evaluated.

[0075] The verification includes implementing the configured digital meta-model using a computer program, and verifying the consistency between the computer program implementation result and the configured digital meta-model.

[0076] The determination is based on the confirmation and verification of the digital meta-model after the current configuration.

[0077] Furthermore, the methods for confirming, verifying, and identifying the configured digital meta-model include subjective methods, graphical methods, and mathematical statistical methods;

[0078] The subjective method involves domain experts distinguishing the output of the configured digital meta-model from the output of the corresponding real model until they can no longer be distinguished, thus determining the final configured digital meta-model.

[0079] The graphical method lists the output of the configured digital meta-model and the output of the corresponding real model in a one-to-one correspondence form as a table or a characteristic curve until the error of the corresponding table or the corresponding characteristic curve is within a set threshold range, and then determines the final configured digital meta-model.

[0080] The mathematical statistics method uses confidence interval method, hypothesis testing method or window spectrum estimation method to compare the consistency of the configured digital meta-model with the parameters, sample distribution and sample statistics of the corresponding actual equipment until the set target is met, and the final configured digital meta-model is determined.

[0081] For the configured digital meta-model determined by the subjective method or the graphical method, the mathematical statistics method is used to continue to judge until the set target is met and the required fidelity of the configured digital meta-model is achieved, thus obtaining the optimized digital meta-model.

[0082] This step is a crucial process for verifying and evaluating the credibility and usability of the digital meta-model, and it accompanies the entire digital meta-modeling process. This step includes digital model validation, digital model verification, and digital model identification, such as... Figure 2 As shown.

[0083] In the calibration of digital meta-models, it is necessary to comprehensively utilize subjective methods, graphical methods, and mathematical statistics. For systems that are immediately rejected by subjective and graphical methods, the reasons should be identified from the overall model and each sub-module, and continuous revisions should be made until the subjective feeling is positive. Then, quantitative mathematical statistics methods should be used for comparison and evaluation until the digital meta-model achieves the required level of realism.

[0084] Model validation, verification, and accreditation, or VV&A, are detailed below:

[0085] Digital model validation is a process that evaluates the model's reliability and usability by comparing the consistency between the simulated theoretical model and the actual system output under the same input conditions and operating environment. It includes three parts: theoretical model validity validation, data validity validation, and operational validity validation. Theoretical model validity validation verifies the correctness of the principles, methods, and assumptions of the theoretical model. Data validity validation ensures that the data used for model building, evaluation, testing, and experimentation is sufficient and correct, specifically including key variables, key parameters, random variables, and initial values ​​in the model. Operational validity validation calculates and evaluates the accuracy of the model's output results. Operational validity validation is the core of model validation.

[0086] Digital model validation is used to determine whether the computer implementation of a model accurately represents the model developer's expression and description of the system concept, that is, to emphasize the consistency between the theoretical model and the computer program.

[0087] Digital model certification is a decision to believe in and accept a model, that is, after the model has been confirmed and verified, it is recognized that the model is an effective model applicable to a specific purpose.

[0088] The digital meta-model verification methods used include:

[0089] Subjective method. Domain experts compare the data obtained from the digital meta-model. If they cannot distinguish between the two, the simulation model is considered effective; if they can distinguish between them, they should further investigate how they distinguished between them and where the differences lie, continuously calibrating the model until the experts feel satisfied.

[0090] Graphical method. Present the simulated data and real data in a one-to-one correspondence format as a numerical table or plot characteristic curves such as mean and standard deviation, observe their changing trends, and compare their numerical values ​​and ranges. If significant differences exist, analyze the magnitude and range of these differences in detail to identify the problem and continuously improve the digital meta-model until it is very close to the actual data.

[0091] Mathematical statistics. Various mathematical statistics methods are used to quantitatively analyze the effectiveness of the model, including confidence interval method, hypothesis testing method, and window spectrum estimation method, to compare the consistency between the digital meta-model and the parameters, sample distribution, and sample statistics of the actual equipment.

[0092] The design of the electromagnetic system model provides model support for complex task environments. The electromagnetic system design is divided into three levels: platform level, component level and component level. The platform is assembled from components, and the components are assembled from components. They are independent of each other in terms of data content. For example, modifying the parameters of the component model attached to the platform will not affect the parameters of the corresponding components in the component library.

[0093] As an example, managing electromagnetic system models includes adding, deleting, modifying, querying, importing or exporting optimized digital meta-models, as well as performing compliance checks on optimized digital meta-models.

[0094] To facilitate rapid and efficient platform and component reconfiguration for task elements, platforms and components can be grouped based on task objectives, primary tasks, or the timing and requirements for platform and component usage.

[0095] Combination Figure 11 As shown, the performance evaluation of the recombined electromagnetic system model includes evaluation of early warning and detection capabilities, intelligence processing capabilities, decision support capabilities, and collaborative task capabilities.

[0096] The early warning and detection capability assessment is an effectiveness evaluation of the target detection capability, target localization capability, and target tracking capability of the recombined electromagnetic system model for single nodes.

[0097] The intelligence processing capability assessment is an evaluation of the intelligence transmission capability and distributed computing capability of the recombined electromagnetic system model for single nodes.

[0098] The assessment of auxiliary decision-making capabilities includes evaluating the situation sharing and reinforcement learning capabilities of the recombined electromagnetic system model.

[0099] The collaborative task capability assessment involves evaluating the reaction reorganization capability and module survivability of each optimized digital element model in the recombined electromagnetic system model.

[0100] This step involves making command decisions in situations characterized by environmental complexity, time urgency, and ambiguity of information.

[0101] The electromagnetic system reconfiguration assessment and decision-making method can be divided into a multi-stage cycle:

[0102] Planning: Before the reorganization begins, sufficient contingency plans need to be made for the task scenario and environment, that is, an analysis of possible situations;

[0103] Preparation: In order to cope with the situation where the electromagnetic system is destroyed, each module needs to be evaluated according to the established mission indicator system before the mission begins, so as to quickly form a new electromagnetic system.

[0104] Execution: Once a mission module in the electromagnetic system is destroyed, a rapid response is required based on the module evaluation results in the preparation steps to make decisions on module reconstruction and form a new electromagnetic system to maintain the stability of the mission system.

[0105] Evaluation: The results of the module reconfiguration need to be evaluated to determine the effectiveness of the decision for the electromagnetic system.

[0106] Example:

[0107] Step 1: Component-based modeling of the digital meta-model:

[0108] Digital platforms are primarily implemented through the hierarchical assembly of models at the component, part, and platform levels. The required models are selected from component, part, and platform model libraries and assembled onto a digital platform. Through interaction between the processing components and the bus at each level, the digital platform gains the ability to complete corresponding tasks. A typical digital platform carries models such as... Figure 3 As shown.

[0109] The digital meta-model hierarchy includes:

[0110] Atom: Algorithm, basic unit, such as: tracking algorithm, filter model, etc.;

[0111] Components: Assembled into components using atomic models, such as receivers, actions, modes, signals, etc.

[0112] Components: Components are assembled from modular models, such as transmitters, receivers, antennas, and scanners, to form a radar.

[0113] Platform: A platform is assembled from component models, such as a flight trajectory, a mid-course guidance receiver, and a terminal guidance radar, which are assembled into a guided missile;

[0114] Nesting: It has multi-level nesting capabilities, that is, a platform nests a sub-platform or component, a component nests a sub-component, etc., ultimately forming an external entity. For example, an aircraft carrying multiple weapons or air-launched decoys, a radar site configuring decoys and other jamming models are assembled into a digital platform with multi-tasking capabilities.

[0115] Step 2: Feature Modeling of the Digital Meta-Model

[0116] The radar cross section (RCS), infrared radiation intensity (TCS), and optical contrast ratio (OCS) of a digital platform are rapidly calculated and represented. Characteristic values ​​for a specified angle are obtained through linear fitting using typical model calculation methods, lookup table methods, or simulation lookup table methods.

[0117] The typical model calculation method provides the software with target fluctuation type selection and target feature reference value setting. For target fluctuation characteristics, the software provides typical fluctuation models such as swerling I, swerling II, swerling III, and swerling IV, as well as non-fluctuation models. During simulation, the task environment calculation model will calculate fluctuation feature values ​​based on the fluctuation model on the basis of the reference values ​​to reflect the fluctuation characteristics of the target.

[0118] There are generally two different approaches to calculating and obtaining features using the lookup table method or the simulated lookup table method, such as... Figure 4 Show.

[0119] The first approach is to pass the characteristic values ​​of all digital platforms to a third-party computing module, such as an electromagnetic environment computing module, during initialization. During simulation, the characteristic values ​​between digital platforms are obtained from the global perspective by the third-party electromagnetic environment computing module, which calculates the angle of arrival and characteristic values.

[0120] The second approach is that the digital platform manages its own feature model data and function interfaces, while other platforms provide the signal angle of arrival. The digital platform's feature calculation function then calculates the feature values ​​and outputs them to the access platform.

[0121] Step 3: Multi-resolution modeling of the digital meta-model:

[0122] During simulation, the electronic device model will call the signal file and generate a waveform signal (baseband or intermediate frequency signal) corresponding to the current operating mode. At this time, the transmitting and receiving sides of the electronic device model perform signal and data processing based on the signal waveform. The model will then operate in a signal-level simulation scenario, such as... Figure 5 As shown.

[0123] Step 4: Digital Meta-model Behavior Modeling

[0124] The action planning of the adversarial model can be divided into three stages:

[0125] 1. Task planning initialization phase. This phase defines the default values ​​for the action plans of each digital meta-model;

[0126] 2. Automated action planning during operation. For complex collaborative behaviors, especially those between digital meta-models, the response is typically designed by the processing plugins of the upper-level digital meta-model;

[0127] 3. Task action planning during operation. The commander / operator sends manual control commands through the model display console of the operating entity. These commands are then sent to the functions of each model for response.

[0128] For the behaviors defined in the three stages, the digital meta-model executes the action sequence according to a first-come, first-served principle; that is, the corresponding action is executed as soon as the condition is met. The action parameters set in the previous stage may be modified by subsequent action instructions. The specific execution process is controlled by the action component, which controls each model to collaboratively complete the platform's predetermined decision instructions, such as... Figure 6 As shown.

[0129] Step 5: Digital Meta-model Verification and Iterative Optimization

[0130] During modeling, the model provides standardized parameters for users to input. For known parameters, the user-input parameters will be used as a benchmark; for unknown parameters, the model will provide empirical values ​​for user reference. In addition, the model offers visual transmission demonstration functions such as power zones, interference zones, and frequency ranges. These functions serve two purposes: first, the model calculates based on the input parameters, allowing users to verify the parameters' rationality; second, a parameter verification function prompts users to correct parameters that fail to pass calibration.

[0131] To assist in verifying the validity of digital meta-model parameters, the platform, component, and assembly levels all have model verification functions. The platform, component, and assembly levels verify model parameters and output verification results, including single-level and cascaded component / assembly verification methods. Verification content includes:

[0132] Component / part model compliance verification: Single-item verification of motion model, single-item verification of radar model, single-item verification of electronic countermeasures model, single-item verification of communication identification data link model, single-item verification of optoelectronic infrared model, single-item verification of weapon model, single-item verification of network model, comprehensive verification of platform performance parameters, comprehensive verification of motion model, comprehensive verification of radar model, comprehensive verification of electronic countermeasures model, comprehensive verification of communication identification data link model, comprehensive verification of optoelectronic infrared model, comprehensive verification of weapon model, and comprehensive verification of network model;

[0133] Compliance verification of planning: compliance verification of flight planning, compliance verification of radar planning, compliance verification of data link planning, compliance verification of electronic countermeasures planning (compliance verification of reconnaissance and jamming planning, compliance verification of decoy planning), compliance verification of weapon planning (compliance verification of surface-to-air missile planning), compliance verification of operational planning;

[0134] Model and planning compliance verification: electronic countermeasures model and planning compliance verification, radar model and planning compliance verification, communication model and planning compliance verification, optoelectronic model and planning compliance verification, decoy model and planning compliance verification, surface-to-air missile model and planning compliance verification;

[0135] Feature compliance verification: Radar cross section (RCS) feature verification, infrared radiation intensity (TCS) feature verification, and optical characteristic intensity (OCS) feature verification;

[0136] Other compliance checks.

[0137] Step Six: Design of the Airborne Electromagnetic System Model:

[0138] Platform-level digital models are digital physical platforms that can be directly used during experimental planning. They are divided into five types: land-based platforms, sea-based platforms, air-based platforms, space-based platforms, and weapon platforms. Each type has its own subcategories, such as... Figure 7 As shown.

[0139] Component-level digital models are the main content of modeling, including five types: radar, communication, electronic countermeasures, optoelectronic, and motion models. Each type has its own subcategories such as... Figure 8 As shown, the platform-level data model is assembled from component-level digital models, such as intelligence radar model + vehicle motion model + radar decoy model, etc. = mobile radar site.

[0140] Based on the classification design requirements, component-level models can be further decomposed. For example, a radar model can be decomposed into radar and guidance models, a communication model can be decomposed into communication, data link, navigation, and IFF models, and an electronic countermeasures model can be decomposed into reconnaissance, jamming, and anti-radiation seeker models, etc.

[0141] Component-level digital models are the basic model units, including four types: transmitter, receiver, antenna, and processor. Each type has its own subclasses, such as... Figure 9 As shown, a component-level model is assembled from a modular model, such as radar transmitter + radar transmitting antenna + radar receiving antenna + radar receiver + radar processor = intelligence radar model.

[0142] The processor component primarily handles the model's data processing functions, including trajectory tracking, interference identification, and threat identification. Furthermore, the processor also handles the model's behavior processing; the actions of upper-level commanders / operators / AI agents are translated into specific action sequences within the digital model, and the processor controls the operating parameters and states of each component based on these action sequences.

[0143] Step 7: Management of the airborne electromagnetic system model:

[0144] This step involves adding, deleting, modifying, querying, importing, exporting, and performing compliance checks on digital models. A typical example of digital model library management software is shown below. Figure 10 As shown.

[0145] Step 8: Rapid Reassembly of the Electromagnetic System

[0146] This step allows for rapid and efficient reorganization of platforms and components to adapt to mission elements. Platform and component grouping can be selected based on mission objectives, primary tasks, or the timing and requirements for platform and component deployment in the airborne mission.

[0147] 1. Grouping based on task objectives:

[0148] Identify the electromagnetic targets threatening airborne operations. The main electromagnetic threats faced by airborne operations come from electromagnetic targets along the air transport route, the drop zone, and the ground area and their vicinity, which are also the primary targets of the mission. During air transport, the main targets are ground-based early warning and detection systems, ground-based air defense fire control systems, airborne early warning aircraft, airborne radars of interceptor aircraft, and other communication and command systems; during landing, the main targets are anti-aircraft and ground communication and command systems; during ground operations, the main targets are artillery locating and fire correction radars, communication and command systems, etc.

[0149] Determine the priority sequence of electronic warfare targets. Since there are many electronic warfare targets in airborne missions, their threat levels and mission value vary significantly. The threat level of a target is mainly analyzed based on factors such as detection distance, probability of detection, and probability of interception and destruction; the mission value is mainly analyzed based on its role in ensuring mission success. For example, command and communication facilities themselves do not pose a direct threat to the airborne mission, but their ability to rapidly command and deploy forces for counter-airborne operations makes them highly valuable.

[0150] Clearly define the jamming and damage effects on electronic targets. To ensure the safety of airborne forces and meet the requirements of the airborne mission, it is essential to clearly define the jamming and damage effects achieved on each target. The jamming and damage effect requirements can be analyzed based on overall effects and specific effects. Overall effect requirements are primarily reflected by the degree of jamming or damage, clarifying the extent to which electronic targets are subjected to strong jamming or destruction to meet the airborne mission requirements. Specific effects are mainly defined according to the airborne mission requirements and for different types of forces, such as the effects of active jamming, passive jamming, and anti-radiation attacks on electronic targets.

[0151] 2. Grouping based on primary tasks:

[0152] The purpose is to acquire the electromagnetic situation of the airborne mission, and to organize reconnaissance platforms and components. Electronic target intelligence is mainly gathered through peacetime reconnaissance and specialized reconnaissance for the airborne mission. Reconnaissance should focus on electromagnetic threat targets near pre-transport routes, airborne landing areas, and ground target areas, primarily utilizing strategic and operational electronic reconnaissance aircraft, unmanned electronic reconnaissance aircraft, and space-based electronic reconnaissance satellites. Electronic countermeasures reconnaissance is integrated throughout the entire airborne mission.

[0153] To disrupt anti-airborne decision-making, electronic jamming platforms and components should be organized. To cover the airborne mission's objectives, electronic jamming platforms and components should be organized according to mission requirements, primarily including air suppression and deception jamming.

[0154] The mission is to support air transport and landing, and includes the formation of support jamming platforms and components. During air transport and landing, the main tasks are to assist in opening air corridors and providing cover for transport. The mission formations are largely the same, differing only in size. These primarily include avionics jamming forces, airborne anti-radiation attack forces, ground-based electronic jamming forces, and ground-based anti-radiation attack forces.

[0155] Platforms and components are grouped to support airborne ground operations. This mainly includes platforms and components that are airdropped to the ground and those providing air support. Platforms and components airdropped to the ground primarily include communication and radar jamming systems. Air support mainly includes airborne anti-radiation attack capabilities, taking real-time action against targets as required by the ground mission.

[0156] 3. Grouping according to the timing and requirements of use:

[0157] During the establishment of the air corridor, long-range support jamming aircraft will be used to electronically jam early warning and guidance radars, air-to-ground command and control communication systems, target indication radars, and guidance radars, reducing or weakening their effectiveness. Simultaneously, accompanying support jamming aircraft will provide cover and jam airborne fire control radars, deep target indication radars, and guidance radars. Airborne anti-radiation attacks will be used to hard-kill key electromagnetic threats, protecting the air formation in completing the air corridor establishment mission. During air transport, long-range electronic jamming aircraft should remain on standby in the jamming area to jam targets that suddenly appear with electromagnetic threats. The accompanying electronic jamming aircraft, airborne anti-radiation jamming forces, and fighter jets will form a cover formation to jam and suppress reinforcing airborne fire control radars and ground-based guidance radars as needed. During ground operations, reconnaissance forces accompanying ground operations will conduct close-range reconnaissance around the target, and communication jamming forces will jam command and communication systems.

[0158] Step Nine: Electromagnetic System Reorganization Assessment and Decision-Making:

[0159] Due to differences in mission scenarios, mission objectives, and mission environments, the establishment of evaluation index systems varies. Taking the mission system for mastering the electromagnetic situation in an airborne area as an example, the electromagnetic system can be evaluated from the following four aspects: early warning and detection capabilities, intelligence processing capabilities, decision support capabilities, and collaborative mission capabilities. Figure 11 As shown.

[0160] Early warning and detection capability: This capability primarily focuses on evaluating the early warning and detection effectiveness of a single node. Reconnaissance and early warning are among the tasks of an electromagnetic system and are fundamental capabilities that platforms and component nodes must possess. During missions, tasks such as reconnaissance and surveillance, electronic jamming, and communication relay need to be performed in the mission area to ensure the smooth implementation of activities; therefore, target detection, location, and tracking are required.

[0161] Intelligence processing capabilities: The defining characteristic of full-domain missions is the realization of data sharing, situational awareness, and intelligent decision-making, enabling flexible responses to unforeseen circumstances. To ensure the smooth transmission of intelligence and data, transmission and computing capabilities are indispensable.

[0162] Decision Support Capability: In real-world mission environments, reliable information transmission between the command center and nodes cannot be guaranteed at all times. Therefore, after receiving massive amounts of situational information, each node first needs to share the situation within the system. Secondly, the system should process and learn from the shared information to respond promptly to the rapidly changing mission environment.

[0163] Collaborative task capability: In new intelligent collaborative joint tasks, in order to improve survivability and economy, a large number of nodes with relatively low economic costs are used to carry a large number of various types of task modules to form a forward formation. Therefore, when faced with threats, each module needs to be quickly reorganized into a new work chain to ensure the module's resilience.

[0164] The method of the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for modeling the rapid reconfiguration and operation mechanism of an electromagnetic system, characterized in that, include: Based on model description files at different levels, multiple component-level models, part-level models, and platform-level models are used to build multiple component-based digital meta-models. The component-level model is the smallest model unit with basic functions. The component-level model is assembled based on the component-level model to obtain the part-level model, and the platform-level model is assembled based on the part-level model. Feature modeling is performed on the digital meta-model, and the physical response features of the digital meta-model are configured. Then, multi-resolution modeling and behavioral modeling are performed to obtain the configured digital meta-model; The configured digital meta-model is verified, validated, and iteratively optimized to obtain the optimized digital meta-model. The optimized digital meta-model is configured according to the task scenario to obtain the electromagnetic system model; and the electromagnetic system model is managed. Based on the mission objectives, an electromagnetic system model is selected, and then the selected electromagnetic system model is rapidly recombined based on the optimized digital meta-model to obtain a recombined electromagnetic system model. The performance of the recombined electromagnetic system model is evaluated until it meets the mission objective requirements.

2. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 1, characterized in that, The component-level model includes a transmitter model, a receiver model, an antenna model, and a processor model; The component-level models include radar models, communication models, electronic countermeasures models, optoelectronic models, and motion models; The platform-level models include land-based platform models, sea-based platform models, air-based platform models, space-based platform models, and weapon platform models; The digital meta-model possesses task capabilities through the interaction between the processor models of each level of the model and the data bus.

3. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 1, characterized in that, The physical response characteristics include the radar cross-section, infrared radiation intensity, and optical contrast of the digital meta-model, which are obtained through typical model calculation, table lookup, or simulation table lookup.

4. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 1, characterized in that, Multi-resolution modeling of the digital meta-model includes coarse-grained system parameter model, medium-grained pulse data model, fine-grained baseband signal model, and fine-grained intermediate frequency signal model; the four granularities of the model are automatically adapted to the functional level simulation of different granularities according to the simulation step size.

5. The method for modeling the rapid reconfiguration mechanism of an electromagnetic system according to claim 1, characterized in that, Behavioral modeling of digital meta-models includes configuring behavioral processing components for the digital meta-models to receive decision instructions, decompose them into action sequences of component-level models and component-level models, and distribute them to the action components of component-level models and component-level models. The action components then control the corresponding component-level models and component-level models to complete the decision instructions.

6. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 5, characterized in that, The decision instructions include instructions for the task planning initialization phase of the digital meta-model, instructions for automatic action planning during the operation of the digital meta-model, and instructions for task action planning during the operation of the digital meta-model. The digital meta-model executes decision instructions based on a first-come, first-served principle.

7. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 1, characterized in that, Verification, validation, and iterative optimization of the configured digital meta-model include confirmation, validation, and identification of the configured digital meta-model. The verification includes verification of the validity of the theoretical model, verification of the validity of the data, and verification of the validity of the operation; by comparing the consistency between the output of the simulated theoretical model and the configured digital meta-model under the same input conditions and operating environment, the credibility and usability of the configured digital meta-model are evaluated. The verification includes implementing the configured digital meta-model using a computer program, and verifying the consistency between the computer program implementation result and the configured digital meta-model. The determination is based on the confirmation and verification of the digital meta-model after the current configuration.

8. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 7, characterized in that, Methods for confirming, verifying, and identifying the configured digital meta-model include subjective methods, graphical methods, and mathematical statistical methods; The subjective method involves domain experts distinguishing the output of the configured digital meta-model from the output of the corresponding real model until they can no longer be distinguished, thus determining the final configured digital meta-model. The graphical method lists the output of the configured digital meta-model and the output of the corresponding real model in a one-to-one correspondence form as a table or a characteristic curve until the error of the corresponding table or the corresponding characteristic curve is within a set threshold range, and then determines the final configured digital meta-model. The mathematical statistics method uses confidence interval method, hypothesis testing method or window spectrum estimation method to compare the consistency of the configured digital meta-model with the parameters, sample distribution and sample statistics of the corresponding actual equipment until the set target is met, and the final configured digital meta-model is determined. For the configured digital meta-model determined by the subjective method or the graphical method, the mathematical statistics method is used to continue to judge until the set target is met and the required fidelity of the configured digital meta-model is achieved, thus obtaining the optimized digital meta-model.

9. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 1, characterized in that, Managing the electromagnetic system model includes adding, deleting, modifying, querying, importing or exporting the optimized digital meta-model, as well as performing compliance checks on the optimized digital meta-model.

10. The method for modeling the rapid reconfiguration operation mechanism of an electromagnetic system according to claim 1, characterized in that, The performance evaluation of the recombined electromagnetic system model includes evaluation of early warning and detection capabilities, intelligence processing capabilities, decision support capabilities, and collaborative task capabilities. The early warning and detection capability assessment is an effectiveness evaluation of the target detection capability, target localization capability, and target tracking capability of the recombined electromagnetic system model for single nodes. The intelligence processing capability assessment is an evaluation of the intelligence transmission capability and distributed computing capability of the recombined electromagnetic system model for single nodes. The assessment of auxiliary decision-making capabilities includes evaluating the situation sharing and reinforcement learning capabilities of the recombined electromagnetic system model. The collaborative task capability assessment involves evaluating the reaction reorganization capability and module survivability of each optimized digital element model in the recombined electromagnetic system model.

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