A simulation method and system for battlefield joint interaction of a light center architecture

Abstract

The application provides a simulation method and system for battlefield joint interaction of light center architecture. The simulation method comprises establishing a dynamic service cluster election mechanism, periodically electing a cluster head node through a dynamic performance weight adjustment strategy; constructing a hierarchical topology discovery mechanism based on space-time correlation, and establishing a heartbeat message transmission mechanism between nodes; based on battlefield situation awareness data, constructing a deep reinforcement learning model to dynamically select a transmission path, and implementing a multi-modal routing optimization strategy; and establishing a digital twin collaborative environment of a cross-agency simulation system through a remote digital matching protocol. The method introduces a two-stage election process and an improved Byzantine fault tolerance algorithm to ensure efficient election and consensus of nodes; adopts a compressed bitmap encoding technology and a space-time graph convolution network to improve the transmission efficiency of node state information and the topology change prediction capability; and the application improves the flexibility and security of battlefield information interaction.

Description

Technical Field

[0001] The present invention relates to the field of military communication and simulation technology, and in particular to a simulation method and system for battlefield joint interaction with a light-center architecture. Background Art

[0002] In existing battlefield joint interaction technologies, the traditional central node forwarding mode has a central node load rate exceeding 80% when the node scale exceeds 300, resulting in a sharp increase in response delay; at the same time, the decentralized architecture relies on a flooding node discovery mechanism, and the bandwidth occupancy rate exceeds 40% at a scale of 200 nodes, making it difficult to support high-definition situation data synchronization; due to the heterogeneity of protocols in simulation systems of different military services (such as DIS, HLA, DDS), the protocol conversion efficiency is less than 70%, and cross-domain authentication takes up to 1.2 seconds, which poses obstacles to cross-institutional interconnection; the traditional AODV protocol only uses the number of hops as the optimization target, and the path survival time is less than 20 seconds in complex electromagnetic environments, which cannot adapt to dynamic battlefield environments.

[0003] At the same time, while existing research has adopted dynamic cluster structures, there are no methods to address the dynamic adjustment of weights and the prediction of spatiotemporal topology. Furthermore, research and application of protocol conversion and digital twin collaboration are relatively lacking. Therefore, a new method for battlefield joint interaction that balances node scale, transmission efficiency, and security collaboration is urgently needed. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a simulation method and system for battlefield joint interaction with a light center architecture.

[0005] The present invention specifically provides the following technical solutions:

[0006] A simulation method for battlefield joint interaction with a light-center architecture is provided. The following steps are performed for a node cluster deployed with a cross-organization simulation system:

[0007] Establish a dynamic service cluster election mechanism in the cross-institutional simulation system, and periodically elect a cluster head node by setting a dynamic performance weight adjustment strategy. The cluster head node is responsible for coordinating data interaction between multiple cross-institutional simulation nodes.

[0008] Build a hierarchical topology discovery mechanism based on spatiotemporal correlation, maintain dynamic topological relationships between cross-institutional simulation nodes, and establish a heartbeat message transmission mechanism between cross-institutional simulation nodes;

[0009] Based on battlefield situational awareness data from a cross-institutional simulation system, a deep reinforcement learning model is constructed to dynamically select data transmission paths between cross-institutional simulation nodes. The transmission paths are used to synchronize tactical instructions and exchange simulation data between cross-institutional simulation nodes, implementing a multimodal routing optimization strategy. The inputs to the deep reinforcement learning model include: a link quality matrix between cross-institutional simulation nodes, a load state vector of each cross-institutional simulation node, and a threat assessment index for the cross-institutional simulation node.

[0010] Through remote digital testing protocols, a digital twin collaborative environment for cross-institutional simulation systems is established to support virtual-reality mapping and joint deduction of multi-institutional simulation resources.

[0011] Optionally, the service cluster election mechanism includes introducing a two-stage election process, specifically:

[0012] In the pre-selection stage, the candidate nodes are quickly screened through the Bloom filter, and the filtering condition is that the false positive rate is less than 0.1%;

[0013] In the final election phase: consensus is reached using the improved Byzantine Fault Tolerance algorithm BFT-PoS;

[0014] Dynamic performance weight :

[0015]

[0016] in, is the current computational load, is the upper limit of load, is the available bandwidth, is the total bandwidth, is the historical average transmission delay, is the delay attenuation factor, + + =1, 、 、 All are weight coefficients;

[0017] The dynamic efficiency weight adjustment strategy is as follows: when the node failure rate is ≥5%, the topology stability weight coefficient Increase to 0.6≤ ≤0.8.

[0018] Optionally, the hierarchical topology discovery mechanism includes:

[0019] In the basic topology layer, compressed bitmap encoding technology is used to compress the status information of cross-institutional simulation nodes to 12 bytes per node. At the same time, based on the improved Kademlia protocol, cross-institutional simulation nodes can be quickly located with a response time of less than 50ms.

[0020] In the dynamic perception layer, a spatiotemporal graph convolutional network (ST-GCN) is constructed to predict the impact of battlefield situation changes on topology. The input feature dimension is: time step × number of nodes × 8, and the output is: probability distribution of topology changes;

[0021] In the emergency reconstruction layer, set the topology reorganization trigger threshold in emergency scenarios: packet loss rate > 15% or latency > 200ms.

[0022] Optionally, the reward function of the multimodal routing optimization strategy is for:

[0023]

[0024] in, For delayed rewards, is the front path end-to-end transmission delay, The maximum tolerable delay allowed by the system is set to 200ms. For safety rewards, is the security level of the i-th path, K is the candidate path, For load balancing rewards, and denote the standard deviation and mean of the node load vector, For cost penalty, is the energy consumption of a single data transmission, Calculate the cost for the path, is the maximum allowable energy consumption of a single task, , β, and δ are weights.

[0025] Optionally, the remote digital test protocol is specifically RDTP, and the RDTP adopts a triplet protocol conversion engine and a certificateless authentication mechanism;

[0026] The digital twin collaborative environment includes five core modules: virtual-reality fusion interface, distributed sandbox verification environment, cross-institutional resource mapping engine, security audit and traceability module, and collaborative deduction engine.

[0027] The present invention also provides a battlefield joint interactive simulation system with a light center architecture, the system comprising:

[0028] Dynamic cluster management subsystem, which integrates a performance evaluation model and resource scheduling engine based on federated learning;

[0029] Intelligent routing subsystem, including a multi-scale feature extraction module and a path decision matrix generator;

[0030] Digital testing platform, equipped with virtual-real integration interface and cross-domain security tunnel;

[0031] The performance monitoring center is deployed with a time series database and a three-dimensional situation visualization engine.

[0032] Optionally, the dynamic cluster management subsystem further includes:

[0033] The node profiling module is used to collect node characteristics, including computing power, storage capacity, and security level;

[0034] The fault prediction unit uses a time series prediction model based on the Transformer architecture, with a prediction window of ≥30 minutes;

[0035] The elastic scaling component is used to support dynamic adjustment of the service cluster size according to the battlefield situation and adjust the response time to <2s.

[0036] Optionally, the intelligent routing subsystem adopts a multipath transmission controller that supports 6 parallel transmission paths; the intelligent routing subsystem further adopts an adaptive coding strategy to dynamically select a coding scheme based on channel quality, including:

[0037] In high signal-to-noise ratio scenarios, select Turbo encoding with a bit rate of 0.8;

[0038] For medium signal-to-noise ratio scenarios, select LDPC code with a code rate of 0.6.

[0039] In low signal-to-noise ratio scenarios, select polar coding with a code rate of 0.4.

[0040] Optionally, the digital testing platform further includes:

[0041] Model fusion engine to support a smooth transition from LVC to digital twins;

[0042] Protocol sandbox environment, providing a set of protocol simulation test tools;

[0043] The security audit module is used to record complete operation logs and support blockchain evidence storage.

[0044] Optionally, the performance monitoring center further includes:

[0045] Multi-dimensional evaluation indicator system, including transmission efficiency, resource utilization, and security compliance;

[0046] Real-time alarm module, with a three-level warning mechanism, including general warning, serious alarm, and emergency response;

[0047] Retrospective analysis tool that locates performance bottlenecks based on causal reasoning models.

[0048] The present invention has the following beneficial technical effects: The present invention provides a simulation method and system for battlefield joint interaction with a light-center architecture. Through the light-center architecture design, the contradiction between the high load of the centralized architecture and the high bandwidth occupancy of the decentralized architecture is balanced for the first time, and the measured bandwidth utilization is greatly improved. The advantages of multi-field algorithms such as federated learning, spatiotemporal graph convolutional networks, and national secret algorithms are deeply analyzed, and they are organically integrated across fields to form an autonomous and controllable technical system. At the same time, a coupling design of a dynamic performance weight model and multi-objective reinforcement learning routing is proposed to solve the system imbalance problem caused by a single optimization goal. This solution supports real-time interaction among multiple entities in joint exercises, greatly reduces the delay in command transmission, and improves the flexibility and security of battlefield information interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 A flowchart of a simulation method for battlefield joint interaction in a light-center architecture provided by an embodiment of the present invention;

[0051] Figure 2 A block diagram of a battlefield joint interaction simulation system with a light center architecture provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0053] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0054] The purpose of the present invention is to provide a simulation method and system for battlefield joint interaction with a light center architecture, aiming to improve the accuracy and immediacy of battlefield joint interaction.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Reference Figure 1 , showing a simulation method for battlefield joint interaction with a light-center architecture according to an embodiment of the present application, for a node cluster deployed with a cross-organization simulation system, performing the following steps:

[0057] A dynamic service cluster election mechanism is established in the cross-institutional simulation system. A cluster head node is periodically elected by setting a dynamic performance weight adjustment strategy. The cluster head node is responsible for coordinating data interaction among multiple cross-institutional simulation nodes.

[0058] The service cluster election mechanism includes introducing a two-stage election process, specifically:

[0059] In the pre-selection stage, the candidate nodes are quickly screened through the Bloom filter, and the filtering condition is that the false positive rate is less than 0.1%;

[0060] In the final election stage: the improved Byzantine Fault Tolerance algorithm BFT-PoS is used to reach consensus.

[0061] The traditional PBFT algorithm faces two major bottlenecks in the dynamic battlefield environment: 1) High communication complexity: When the number of nodes is N, the message complexity is O(N²), which makes it difficult to scale to 2000+ nodes; 2) Static committee mechanism: The verification nodes are fixed and vulnerable to targeted attacks.

[0062] Specifically, the improved BFT-PoS employs a dynamic staking mechanism, whereby staked amounts are updated during each election round based on real-time node performance. Malicious nodes are penalized and their stakes are forfeited. It also employs a two-tiered verification committee architecture and a liveness guarantee mechanism. It replaces ECDSA / SHA256 with SM2 / SM3, increasing signature speed by 40%. Integration of a TEE (such as Intel SGX) protects committee members' private keys from theft, further enhancing security. Table 1 compares the performance of the improved BFT-PoS implementation with that of the traditional PBFT algorithm.

[0063] Table 1. Performance comparison results

[0064]

[0065] Specifically, the dynamic performance weight :

[0066]

[0067] in, is the current computational load, is the upper limit of load, is the available bandwidth, is the total bandwidth, is the historical average transmission delay, is the delay attenuation factor, + + =1;

[0068] The dynamic efficiency weight adjustment strategy is as follows: when the node failure rate is ≥5%, the topology stability weight coefficient Increase to 0.6≤ ≤0.8.

[0069] about 、 、 The value of is carefully designed in combination with the battlefield scene characteristics and system optimization goals. The constraints are + + =1 and 、 、 The values ​​of are all in the range [0,1].

[0070] For example, strategy 1: based on battlefield scenarios 、 、 The value of is statically configured, as shown in Table 2.

[0071] Table 2. Static configuration parameter comparison table

[0072]

[0073] Test 1: In a 200-node simulation environment, when using weights (0.2, 0.3, 0.5) for real-time communication, end-to-end latency was reduced by 42% compared to the balanced weighting scenario, and the command transmission success rate increased to 98.7%.

[0074] Strategy 2: Dynamic Adaptive Adjustment Mechanism

[0075] A weight adjustment function is designed for the highly dynamic and highly interfering battlefield environment. The performance weight coefficient is dynamically adjusted by real-time sensing of computing load, bandwidth status, and latency changes. 、 、 , the weight adjustment function is specifically:

[0076]

[0077] in, The current cluster computing load, that is, the comprehensive utilization of CPU and memory, ranges from 0 to 1; The cluster load warning threshold, which is the critical value that triggers emergency adjustment, is generally set at 0.7-0.8. It is the load sensitivity adjustment factor, which controls the steepness of the S-curve and is generally set at 2.5-3.5. The load emergency coefficient is the nonlinear amplification factor when the load exceeds the limit, which is generally 0.8-1.2; The minimum bandwidth required for the current task, which is dynamically calculated based on the data type; is the current available bandwidth, which is a real-time monitoring value; is the total channel bandwidth, which is a fixed value of 100MHz; The bandwidth redundancy coefficient is the ratio of reserved bandwidth to prevent sudden traffic congestion. The value is generally 0.1-0.3.

[0078] Test 2: When sudden electromagnetic interference causes a surge in latency

[0079] The initial state values ​​are: =0.65, =55Mbps, Tdelay=120ms; ( , , ) = (0.3, 0.4, 0.3).

[0080] After the interference occurs, the electromagnetic pulse causes Tdelay to increase sharply to 280ms, triggering the delay intervention mechanism and starting to adjust ( , , ) value, the adjustment process is:

[0081]

[0082] get( , , ) = (0.30, 0.22, 0.56). At this time, in the emergency intervention mode, electromagnetic interference causes a sudden increase in delay, Tdelay increases from 120ms to 280ms, which is an extreme event and requires a quick response; therefore, in an emergency, the system prioritizes , i.e., the latency stability weight, sacrifices some normalization constraints in exchange for system survival. That is, at this time, the system allows temporary breakthrough of normalization constraints to prioritize key performance indicators, i.e., latency stability. This is a fault-tolerant mechanism in the design. Constraints are gradually restored through subsequent steps (such as weight redistribution). For example, the system performs weight rebalancing in the next control cycle Δt=50ms, and finally obtains ( / 1.08, / 1.08, / 1.08)=(0.278,0.204,0.518).

[0083] Ultimately, latency fluctuations dropped from ±50ms to ±21ms, a 58% reduction, and topology reconstruction times dropped from 12 / minute to 5 / minute, improving stability by 42%.

[0084] Strategy 3: The staged weight planning is shown in Table 3.

[0085] Table 3. Stages ( , , ) Value table

[0086]

[0087] In the performance comparison experiment, the phased strategy increased the task completion rate by 37% and reduced the resource waste rate by 29% compared with the fixed weight scheme.

[0088] Among them, reinforcement learning is used to optimize the parameters. After 100 iterations of evolution, the Pareto optimal solution set is obtained, and the typical solution is ( , , ) = (0.35, 0.28, 0.37).

[0089] Build a hierarchical topology discovery mechanism based on spatiotemporal correlation, maintain dynamic topological relationships between cross-institutional simulation nodes, and establish a heartbeat message transmission mechanism between cross-institutional simulation nodes.

[0090] The hierarchical topology discovery mechanism includes:

[0091] In the basic topology layer, compressed bitmap encoding technology is used to compress the status information of cross-institutional simulation nodes to 12 bytes / node. At the same time, based on the improved Kademlia protocol, rapid positioning of cross-institutional simulation nodes is achieved, with a response time of <50ms.

[0092] The improved Kademlia protocol specifically includes: 1) automatic adjustment based on node density, reducing the query hop count from O(logN) to O(1), achieving dynamic k-bucket adjustment; 2) random number challenge based on SM3 hash, with verification time <3ms; 3) application of spatiotemporal coding ID = Hash (geographic location + timestamp + device fingerprint), supporting dynamic topology rapid reconstruction; 4) compared with the traditional scheme of 64-byte encoding, the bitmap encoding is compressed to 12 bytes / node, reducing bandwidth usage by 81%.

[0093] In the dynamic perception layer, a spatiotemporal graph convolutional network (ST-GCN) is constructed to predict the impact of battlefield situation changes on topology. The input feature dimension is: time step × number of nodes × 8, and the output is: probability distribution of topology changes.

[0094] The loss function of the ST-GCN model is a multi-fusion loss that uses cross entropy loss as the dominant classification task combined with KL divergence to constrain the distribution consistency of spatiotemporal attention weights.

[0095] In the experiment, we collected data from multiple exercises, including scenarios such as electromagnetic interference, node failures, and emergency tasks, totaling 500,000 spatiotemporal sequences. We also labeled topology change events. We also added 20% adversarial examples to improve the robustness of the ST-GCN model in interference environments. The test results are shown in Table 4.

[0096] Table 4. Comparison of measured results of ST-GCN model

[0097]

[0098] In the emergency reconstruction layer, set the topology reorganization trigger threshold in emergency scenarios: packet loss rate > 15% or latency > 200ms.

[0099] The hierarchical heartbeat message transmission mechanism specifically includes: setting up a three-level heartbeat message structure, including: basic heartbeat layer: sending 5-byte simplified messages at a fixed period; extended status layer: dynamically triggering the sending of enhanced messages containing resource status matrices; emergency notification layer: event-driven sending of priority messages carrying battlefield situation warnings.

[0100] Based on the battlefield situation awareness data of the cross-institutional simulation system, a deep reinforcement learning model is constructed to dynamically select the data transmission path between cross-institutional simulation nodes. The transmission path is used for tactical command synchronization and simulation data interaction between cross-institutional simulation nodes, and implements a multimodal routing optimization strategy. The input of the deep reinforcement learning model includes: the link quality matrix between cross-institutional simulation nodes, the load state vector of each cross-institutional simulation node, and the threat assessment index of the cross-institutional simulation node.

[0101] Building a deep reinforcement learning model is as follows:

[0102] State space modeling: Define a multidimensional state vector S t = [Q, L, T, H], where the link quality matrix Q∈R N×N , element q ij =SNR ij -P ij loss / P max, N is the number of nodes, SNR is the signal-to-noise ratio, PlossPloss is the packet loss rate; the node load vector L[l1,l2,...,l N ], the product of computing and storage resource utilization; threat assessment index T=[t1,t2,...,t N ], , where D1 is the electromagnetic interference intensity, D2 is the geographical exposure, and D3 is the number of historical attacks, with weights w1=0.5, w2=0.3, and w3=0.2; the historical path state H is a sliding window that records the delay and security level of the most recent 5-hop path.

[0103] Define action space: Construct a hierarchical action space A={A path ,A encode ,A priority}; where A path For path selection, specifically select K paths from the candidate path set P, K = 3, and use Gumbel-Softmax sampling; A encode As the coding strategy, a discrete action set {Turbo code, LDPC code, polar code} is used to dynamically adjust the code rate; A priority Set the DSCP field value of the packet for priority marking.

[0104] Reward function for multimodal routing optimization strategy design:

[0105]

[0106] in, For delayed rewards, is the front path end-to-end transmission delay, The maximum tolerable delay allowed by the system is set to 200ms. For safety rewards, is the security level of the i-th path, K is the candidate path, For load balancing rewards, and denote the standard deviation and mean of the node load vector, For cost penalty, is the energy consumption of a single data transmission, Calculate the cost for the path, is the maximum allowable energy consumption for a single task. Based on multiple experiments, the weights are set to α = 0.4, β = 0.3, γ = 0.2, and δ = 0.1. The experimental results are shown in Table 5.

[0107] Table 5. Test results

[0108]

[0109] Through multi-objective coupling design, the problem of "optimizing a single indicator leading to system imbalance" in traditional solutions is solved.

[0110] Through remote digital testing protocols, a digital twin collaborative environment for cross-institutional simulation systems is established to support virtual-reality mapping and joint deduction of multi-institutional simulation resources.

[0111] The Remote Digital Testing Protocol (RDTP) utilizes a triplet protocol conversion engine and a certificateless authentication mechanism. RDTP implements lossless bidirectional conversion between DIS 2.0.4, HLA 1516-2010, and DDS 1.4 protocols. It also employs a pipelined processing architecture, including parsing, semantic mapping, and encapsulation, with a conversion latency of ≤3ms. The protocol also supports context-aware conversion. The certificateless authentication mechanism includes an identification cryptography system based on the SM9 algorithm.

[0112] The digital twin collaborative environment includes five core modules: virtual-reality fusion interface, distributed sandbox verification environment, cross-institutional resource mapping engine, security audit and traceability module, and collaborative deduction engine.

[0113] In the virtual-reality fusion interface, the multi-resolution model adapter supports five-level granularity mapping from LVC (real-virtual-construction) to digital twins: entity level (1:1) → unit level (1:N) → system level (N:M) → battlefield level (regional mapping) → strategic level (global deduction); at the same time, the model resolution can be automatically switched according to bandwidth conditions.

[0114] The distributed sandbox verification environment has three dimensions, including spatial dimension, temporal dimension and logical dimension.

[0115] In the cross-institutional resource mapping engine, cosine similarity is used to calculate resource matching, and the ant colony algorithm is used to optimize the resource scheduling path to achieve dynamic load balancing.

[0116] A four-layer protection mechanism is designed in the security audit and traceability module, including an operation audit mechanism using blockchain evidence storage technology, a data integrity verification mechanism based on full-link verification of the Merkle tree, a privacy protection mechanism using the Paillier algorithm, and an anomaly detection mechanism using isolation forest combined with time series prediction technology.

[0117] The collaborative deduction engine can realize the decision-making of multiple intelligent agents including command, combat, and support, and can also realize real-time confrontation deduction.

[0118] The problems in cross-institutional simulation systems are solved through the design of lightweight protocols, resource virtualization, and intelligent decision-making.

[0119] In one embodiment of the present application, Figure 2 As shown, a battlefield joint interaction simulation system with a light center architecture is also provided, and the system includes:

[0120] The dynamic cluster management subsystem integrates a performance evaluation model and resource scheduling engine based on federated learning. This subsystem also includes a node profiling module for collecting characteristics of cross-organizational simulation nodes, including computing power, storage capacity, and security level; a fault prediction unit using a Transformer-based time series prediction model with a prediction window of 30 minutes or longer; and an elastic scaling component for dynamically adjusting the size of service clusters based on battlefield dynamics, ensuring a response time of less than 2 seconds.

[0121] The intelligent routing subsystem includes a multi-scale feature extraction module and a path decision matrix generator. The intelligent routing subsystem uses a multipath transmission controller that supports six parallel transmission paths. It also employs an adaptive coding strategy that dynamically selects a coding scheme based on channel quality. For high signal-to-noise ratio scenarios, Turbo coding with a code rate of 0.8 is selected; for medium signal-to-noise ratio scenarios, LDPC coding with a code rate of 0.6 is selected; and for low signal-to-noise ratio scenarios, polar coding with a code rate of 0.4 is selected.

[0122] The digital testing platform features a virtual-reality fusion interface and cross-domain secure tunnels. It also includes a model fusion engine to support a smooth transition from LVC to digital twins; a protocol sandbox environment that provides a DIS / HLA / DDS protocol simulation testing toolset; and a security audit module that records complete operation logs and supports blockchain-based evidence storage.

[0123] The performance monitoring center, equipped with a time-series database and a three-dimensional situation visualization engine, also includes a multi-dimensional evaluation indicator system, including transmission efficiency, resource utilization, and security compliance; a real-time alert module with a three-level warning mechanism: general warning, severe warning, and emergency response; and a retrospective analysis tool that locates performance bottlenecks based on causal reasoning models.

[0124] Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a computer device, including:

[0125] at least one processor; and

[0126] The memory stores a computer program that can be run on the processor, and the processor executes the steps of any one of the above simulation methods when executing the program.

[0127] Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer program instructions. When the computer program instructions are executed by a processor, the steps of any of the above simulation methods are performed.

[0128] Finally, it should be noted that those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM). The above-mentioned computer program embodiments can achieve the same or similar effects as the corresponding embodiments of any of the above-mentioned methods.

[0129] Furthermore, the apparatuses and devices disclosed in the embodiments of the present invention may typically be various electronic terminal devices, such as mobile phones, personal digital assistants (PDAs), tablet computers (PADs), smart televisions, etc., or large terminal devices, such as servers. Therefore, the scope of protection disclosed in the embodiments of the present invention should not be limited to a specific type of apparatus or device. The client disclosed in the embodiments of the present invention may be implemented in any of the above-mentioned electronic terminal devices in the form of electronic hardware, computer software, or a combination of both.

[0130] In addition, the method disclosed in the embodiment of the present invention can also be implemented as a computer program executed by a CPU, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the CPU, the above functions defined in the method disclosed in the embodiment of the present invention are performed.

[0131] In addition, the above method steps and system units can also be implemented using a controller and a computer-readable storage medium for storing a computer program that enables the controller to implement the above steps or unit functions.

[0132] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any marked order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless expressly limited to the singular.

[0133] In this specification, the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and the relevant parts can be referred to the partial description of the previous embodiments.

[0134] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A simulation method for battlefield joint interaction with a light center architecture, characterized in that: For a node cluster with a cross-organizational simulation system deployed, perform the following steps: exist A dynamic service cluster election mechanism is established in the cross-institutional simulation system. A dynamic performance weight adjustment strategy is set to periodically elect a cluster head node. The cluster head node is responsible for coordinating data interaction between multiple cross-institutional simulation nodes. The service cluster election mechanism includes a two-stage election process, specifically: In the pre-selection stage, the candidate nodes are quickly screened through the Bloom filter, and the filtering condition is that the false positive rate is less than 0.1%; In the final election phase: consensus is reached using the improved Byzantine Fault Tolerance algorithm BFT-PoS; Dynamic performance weight : in, is the current computational load, is the upper limit of load, is the available bandwidth, is the total bandwidth, is the historical average transmission delay, is the delay attenuation factor, + + =1, 、 、 All are weight coefficients; The dynamic efficiency weight adjustment strategy is as follows: when the node failure rate is ≥5%, the topology stability weight coefficient Increase to 0.6≤ ≤0.8; A hierarchical topology discovery mechanism based on spatiotemporal correlation is constructed to maintain dynamic topological relationships between cross-institutional simulation nodes and establish a heartbeat message transmission mechanism between cross-institutional simulation nodes. The hierarchical topology includes a basic topology layer, a dynamic perception layer, and an emergency reconstruction layer. The hierarchical topology discovery mechanism includes using compressed bitmap encoding technology in the basic topology layer to compress the state information of cross-institutional simulation nodes to 12 bytes per node, and simultaneously achieving rapid positioning of cross-institutional simulation nodes based on the improved Kademlia protocol with a response time of <50ms. In the dynamic perception layer, a spatiotemporal graph convolutional network (ST-GCN) is constructed to predict the impact of battlefield situation changes on topology. The input feature dimension is: time step × number of nodes × 8, and the output is: probability distribution of topology changes. In the emergency reconstruction layer, a topology reorganization trigger threshold is set in emergency scenarios: packet loss rate >15% or delay >200ms. Based on battlefield situational awareness data from a cross-institutional simulation system, a deep reinforcement learning model is constructed to dynamically select data transmission paths between cross-institutional simulation nodes. The transmission paths are used to synchronize tactical instructions and exchange simulation data between cross-institutional simulation nodes, implementing a multimodal routing optimization strategy. The inputs to the deep reinforcement learning model include: a link quality matrix between cross-institutional simulation nodes, a load state vector of each cross-institutional simulation node, and a threat assessment index for the cross-institutional simulation node. Through remote digital testing protocols, a digital twin collaborative environment for cross-institutional simulation systems is established to support virtual-reality mapping and joint deduction of multi-institutional simulation resources.

2. The simulation method for battlefield joint interaction with a light center architecture according to claim 1 is characterized in that: The reward function of the multimodal routing optimization strategy for: in, For delayed rewards, is the end-to-end transmission delay of the current path, The maximum tolerable delay allowed by the system is set to 200ms. For safety rewards, is the security level of the i-th path, K is the number of candidate paths, For load balancing rewards, and denote the standard deviation and mean of the node load vector, For cost penalty, is the energy consumption of a single data transmission, Calculate the cost for the path, is the maximum allowable energy consumption of a single task, , β, and δ are weights.

3. The simulation method for battlefield joint interaction with a light center architecture according to claim 1 is characterized in that: The remote digital test protocol is specifically RDTP, which adopts a triplet protocol conversion engine and a certificateless authentication mechanism; The digital twin collaborative environment includes five core modules: virtual-reality fusion interface, distributed sandbox verification environment, cross-institutional resource mapping engine, security audit and traceability module, and collaborative deduction engine.

4. A simulation system for battlefield joint interaction with a light center architecture, used to implement a simulation method for battlefield joint interaction with a light center architecture as claimed in any one of claims 1 to 3, characterized in that: The system comprises: Dynamic cluster management subsystem, which integrates a performance evaluation model and resource scheduling engine based on federated learning; Intelligent routing subsystem, including a multi-scale feature extraction module and a path decision matrix generator; Digital testing platform, equipped with virtual-real integration interface and cross-domain security tunnel; The performance monitoring center is deployed with a time series database and a three-dimensional situation visualization engine.

5. The battlefield joint interactive simulation system with a light center architecture according to claim 4 is characterized in that: The dynamic cluster management subsystem further includes: The node profiling module is used to collect the characteristics of cross-institutional simulation nodes, including computing power, storage capacity, and security level; The fault prediction unit uses a time series prediction model based on the Transformer architecture, with a prediction window of ≥30 minutes; The elastic scaling component is used to support dynamic adjustment of the service cluster size according to the battlefield situation and adjust the response time to <2s.

6. The battlefield joint interactive simulation system with a light center architecture according to claim 4 is characterized in that: The intelligent routing subsystem adopts a multipath transmission controller that supports 6 parallel transmission paths; The intelligent routing subsystem also adopts an adaptive coding strategy to dynamically select a coding scheme based on channel quality, including: In high signal-to-noise ratio scenarios, select Turbo encoding with a bit rate of 0.8; For medium signal-to-noise ratio scenarios, select LDPC code with a code rate of 0.

6. In low signal-to-noise ratio scenarios, select polar coding with a code rate of 0.

4.

7. The battlefield joint interactive simulation system with a light center architecture according to claim 4 is characterized in that: The digital testing platform further includes: Model fusion engine to support a smooth transition from LVC to digital twins; Protocol sandbox environment, providing a set of protocol simulation test tools; The security audit module is used to record complete operation logs and support blockchain evidence storage.

8. The battlefield joint interactive simulation system with a light center architecture according to claim 4 is characterized in that: The performance monitoring center further includes: Multi-dimensional evaluation indicator system, including transmission efficiency, resource utilization, and security compliance; Real-time alarm module, with a three-level warning mechanism, including general warning, serious alarm, and emergency response; Retrospective analysis tool that locates performance bottlenecks based on causal reasoning models.

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