An agent digital twin driven whole life cycle management and collaboration method and system

Abstract

The application belongs to the technical field of artificial intelligence communication, and discloses a kind of intelligent body digital twin driving's whole life cycle management and control and collaborative method and system, in view of the specific technical problems that physical-virtual state mapping of communication intelligent body is not accurate, whole life cycle management and control lacks closed loop mechanism, cross-domain collaborative dynamic adaptability is insufficient, by constructing communication intelligent body digital twin mirror image and real-time mapping model, designing whole life cycle closed loop control algorithm, proposing dynamic game driven cross-domain collaborative adaptation mechanism, realize the technical upgrading of communication intelligent body from isolated physical operation, segmented control, static collaboration to virtual-real linkage management and control, whole cycle closed loop optimization, dynamic collaborative adaptation.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a method and system for full lifecycle management and collaboration driven by a digital twin of an intelligent agent. Background Technology

[0002] The full lifecycle management (deployment, operation, maintenance, and iteration) and cross-domain collaboration of communication intelligent agents are core guarantees for the large-scale application of modern communication networks. Digital twin technology provides a new path for the virtual-physical linkage management of intelligent agents and is widely used in scenarios such as 5G / 6G core network intelligent agents, industrial internet edge intelligent agents, and global communication scheduling intelligent agents. Existing digital twin application technologies for communication intelligent agents are mostly based on static image construction and simple data mapping, lacking in-depth algorithm design for precise physical-virtual synchronization, closed-loop management throughout the lifecycle, and dynamic adaptation for cross-domain collaboration. In practical applications, three specific and urgent technical problems need to be solved, as follows: Inaccurate physical-virtual state mapping and lagging dynamic synchronization: Existing digital twin mirrors of communication agents are mostly built based on offline parameters, which can only achieve coarse-grained mapping of physical states. They lack fine-grained perception and real-time synchronization mechanisms for the agent's operating state (such as decision logic execution, resource consumption, and fault precursors). When the physical agent's state changes dynamically (such as sudden load changes or policy adjustments), the virtual mirror synchronization lags and cannot accurately reflect the real state of the physical entity.

[0003] The lack of a closed-loop mechanism for full lifecycle management and the disconnect between segmented management: The lifecycle management of existing communication intelligent agents is divided into independent stages such as deployment, operation, maintenance, and iteration. Each stage adopts isolated management strategies and has not established a closed-loop optimization mechanism across stages. For example, fault data in the operation stage cannot directly guide the optimization of maintenance strategies, and maintenance records cannot support iterative upgrade decisions, resulting in low management efficiency and poor stability of full lifecycle operation.

[0004] The cross-domain collaboration lacks dynamic adaptability and relies on preset collaboration rules: the existing cross-domain collaboration of multiple communication agents is based on static information interaction of digital twin mirrors. The collaboration rules are preset fixed patterns and do not consider the impact of dynamic changes in the physical agent state and network environment fluctuations on the collaboration strategy. When cross-domain links are congested or agent loads are unbalanced, the collaboration strategy cannot be adaptively adjusted, resulting in decreased cross-domain collaboration efficiency and unbalanced resource allocation.

[0005] The above-mentioned problems are specific technical defects in existing communication intelligent agent digital twin application technologies. They are not macro-level issues, but directly result in the communication intelligent agent's state control accuracy, full life cycle operation quality, and cross-domain collaboration capabilities failing to meet the development needs of modern communication networks in terms of scale, dynamism, and cross-domain collaboration. Therefore, it is urgent to propose a targeted technical solution to address these problems. Summary of the Invention

[0006] The purpose of this invention is to overcome the aforementioned deficiencies of the prior art and provide a method and system for full lifecycle management and collaboration driven by digital twins of intelligent agents. It addresses the specific problems raised in the background art one by one: For the problem of inaccurate physical-virtual state mapping, it proposes a dynamic construction and real-time mapping algorithm for digital twin images to achieve accurate synchronization of virtual and physical states; for the problem of the lack of a closed-loop mechanism in full lifecycle management, it designs a segmented full lifecycle management and cross-stage closed-loop optimization algorithm to achieve full-cycle collaborative management; and for the problem of insufficient dynamic adaptability in cross-domain collaboration, it establishes a dynamic game-driven cross-domain collaborative adaptation algorithm to achieve adaptive adjustment of collaborative strategies.

[0007] The core technical solution of this invention includes six algorithms not reported in the prior art. All of them emphasize the modeling and solution process and belong to technical solutions rather than rules for intellectual activities. Specifically: A dynamic construction algorithm for digital twin mirrors of communication agents is proposed, which integrates physical perception data and operation logs, and constructs a high-fidelity virtual mirror based on generative adversarial networks (GANs) to solve the problem of poor adaptability of static mirrors; Design a real-time physical-virtual state mapping algorithm, which captures dynamic state changes based on a temporal attention mechanism to achieve low-latency and accurate synchronization of virtual and real states; Construct a segmented management and control modeling algorithm for the entire lifecycle of a communication intelligent agent, and design exclusive management and control strategies for each stage of deployment, operation, maintenance and iteration to achieve precise segmented management and control; A cross-stage closed-loop optimization algorithm is proposed, which realizes the coordinated optimization of control strategies at each stage based on the full-cycle data flow of digital twin mirrors. We design a dynamic game-driven cross-domain collaborative adaptation modeling algorithm that incorporates the dynamic state of physical agents into the game framework to achieve dynamic adjustment of collaborative strategies. A dynamic algorithm for resolving cross-domain collaborative conflicts is established, which, combined with the global state awareness of digital twin mirrors, enables real-time conflict resolution and collaborative optimization.

[0008] A first aspect of this invention provides a method for full lifecycle management and cross-domain collaboration of a communication intelligent agent driven by a digital twin, comprising the following three core steps: S1: Construction of Digital Twin Mirrors of Communication Agents and Real-Time Mapping Between Virtual and Real Worlds By using the digital twin image dynamic construction algorithm (1) and the virtual-real real-time mapping algorithm (2), a high-fidelity virtual image is constructed and the accurate synchronization of physical and virtual states is achieved, thus solving the problem of inaccurate physical-virtual state mapping.

[0009] S2: Digital Twin-Driven Closed-Loop Management of the Entire Lifecycle Based on digital twin mirroring, the system employs a segmented control algorithm (3) for the entire lifecycle and a cross-stage closed-loop optimization algorithm (4) to achieve precise control and cross-stage collaborative optimization of the intelligent agent throughout its entire lifecycle, thus solving the problem of the lack of a closed-loop mechanism in the entire lifecycle control.

[0010] S3: Dynamic game-driven cross-domain collaborative adaptation and conflict resolution For multi-agent cross-domain collaborative scenarios, a dynamic game-theoretic collaborative adaptation algorithm (5) and a dynamic conflict resolution algorithm (6) are used to achieve adaptive adjustment of collaborative strategies and real-time conflict resolution, thus solving the problem of insufficient dynamic adaptability in cross-domain collaboration.

[0011] A second aspect of this invention provides a digital twin-driven full lifecycle management and cross-domain collaboration system for communication intelligent agents, comprising three core units, each corresponding to one of the three steps of the above method. Each unit implements a corresponding innovative algorithm to collaboratively complete the virtual-real linkage management, full lifecycle optimization, and cross-domain dynamic collaboration of the communication intelligent agent.

[0012] A third aspect of the present invention provides an electronic device, including a processor and a memory, wherein the processor invokes instructions stored in the memory to execute the above-described method.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the above-described method.

[0014] The beneficial effects of this invention are as follows: This invention specifically addresses the problems of existing digital twin applications of communication intelligent agents, and realizes the technical upgrade of communication intelligent agents from "isolated physical operation, segmented control, and static collaboration" to "virtual and physical linkage control, full-cycle closed-loop optimization, and dynamic collaborative adaptation", adapting to the needs of large-scale applications in modern communication networks. The digital twin mirror construction and real-time virtual-real mapping algorithm improves the accuracy of physical-virtual state mapping, reduces synchronization latency by 65%, and realizes precise virtual-real linkage of agent state; The closed-loop management and control algorithm throughout the entire lifecycle reduces the failure rate of the intelligent agent throughout its lifecycle, lowers operation and maintenance costs, and significantly improves the synergy of management and control strategies at each stage. Dynamic game-theoretic collaborative adaptation and conflict resolution algorithms enhance the dynamic adaptability of cross-domain collaboration, improve resource utilization, and effectively cope with dynamic changes in the network environment and agent state. The technical solution of this invention is compatible with existing architectures such as 5G / 6G core networks and industrial internet edge networks, and can be seamlessly connected to the communication network full life cycle management platform. It has good compatibility, scalability and engineering practicality, and meets the development needs of the new generation of information technology industry. Attached Figure Description Figure 1 Flowchart illustrating the implementation principle of this invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to specific embodiments. These embodiments are only used to explain the present invention and are not intended to limit the present invention. Furthermore, the embodiments can be combined with each other, and the same or similar concepts will not be repeated.

[0016] Example 1: Real-time Physical-Virtual State Mapping Algorithm This embodiment addresses the issues of inaccurate physical-virtual state mapping and lagging dynamic synchronization by combining the temporal attention mechanism in step 2 to achieve low-latency and accurate synchronization between the physical agent and its digital twin mirror image. The specific steps are as follows: Step 1: Multi-dimensional state data acquisition and preprocessing of the physical agent Data Acquisition: Through the perception modules of the physical agent (sensors, operation log collectors, and decision-making process monitors), six dimensions of state data are collected: hardware state (CPU load, memory usage, power stability), decision state (decision strategy, execution actions, parameter configuration), resource state (bandwidth usage, computing resource consumption, storage usage), network state (link latency, signal strength, connection stability), task state (task execution progress, completion rate, latency compliance rate), and environmental state (temperature, humidity, electromagnetic interference, deployment location environment). This data forms the basis of a physical state dataset. ,in for The physical state vector at time t; Preprocessing: The time-series state data is segmented into frames using a sliding window, outliers are removed by outlier detection, feature values ​​are mapped to the [0,1] interval by normalization, and redundant features are removed by feature selection to obtain a standardized physical state sequence. .

[0017] Step 2: Construction of the state mapping model for the temporal attention mechanism A physical-virtual state mapping model based on a temporal attention mechanism is constructed to capture the dynamic changes in physical states and achieve precise synchronization of virtual mirror states. The core formula is as follows: in: The hidden state of the gated recurrent unit (GRU) is used to capture the temporal dependencies of physical states. for The hidden state at any given moment; This is the temporal attention weight vector, reflecting the contribution of historical states to the mapping at the current time step. , , These are query, key, and value weight matrices, respectively. The historical hidden state matrix, The dimension of the key vector. This refers to attention bias. This is a temporal attention context vector that integrates key information from historical and current states. for The synchronization state vector of the virtual image at any given time. It is the Sigmoid activation function. This is the output layer weight matrix. This is the output bias term.

[0018] Step 3: Mapping Model Training and Optimization Building the training dataset: standardizing the physical state sequence As input, the true labels of the physical state (precise state data verified by humans) are used as output, and the data is divided into training set, validation set and test set; Define the loss function: The mean squared error (MSE) loss function is used to measure the difference between the virtual synchronization state and the physical real state.

[0019] in for The real state vector of the physical agent at any given moment; Model optimization: The adaptive moment estimation (Adam) algorithm is used to iteratively optimize the model parameters and minimize the loss function. By monitoring the model's generalization ability through the validation set to prevent overfitting, the optimal state mapping model is obtained.

[0020] Step 4: Real-time synchronization and dynamic calibration of virtual and real states Real-time mapping: mapping the real-time state data of physical agents. Input the trained mapping model and output the virtual mirror synchronization status in real time. This enables low-latency synchronization between physical and virtual states. Dynamic calibration: Periodically calculate the deviation between the synchronization state and the actual physical state. If the deviation exceeds the preset threshold, an online learning algorithm is used to dynamically adjust the mapping model parameters and calibrate the synchronization error. Anomaly warning: By monitoring the trend of changes in the synchronization state through virtual mirror, if an abnormal increase or sudden change in the state deviation is detected, the potential failure of the physical intelligent agent can be predicted and the warning mechanism can be triggered.

[0021] Efficiency Enhancement Principle Existing virtual-real mapping technologies lack the ability to capture temporal dependencies and perform simple mapping based only on the current state, resulting in low synchronization accuracy. This embodiment innovates by modeling the GRU unit to capture the temporal evolution of the physical state, enabling the virtual image to predict the current state based on historical states, thereby improving the accuracy of dynamic synchronization. Existing state mapping technologies treat all historical data equally without distinguishing the contribution of key states. This embodiment uses a temporal attention mechanism to adaptively allocate attention weights to historical states, strengthen the influence of key states on the current mapping, and weaken the interference of irrelevant states, thus solving the problem of "mapping deviation caused by historical data redundancy". Existing mapping models are statically trained and cannot adapt to the long-term state drift of physical agents. This embodiment uses dynamic calibration and online learning to adjust model parameters in real time, ensuring the long-term stability of virtual-real mapping accuracy and solving the problem of "synchronization lag caused by state drift". Existing technologies only achieve state synchronization without abnormal early warning functions. This embodiment monitors the state trend of a virtual mirror and predicts faults based on changes in mapping deviation, thus upgrading from "passive synchronization" to "active early warning" and improving the foresight of intelligent agent state management.

[0022] Example 2: Algorithm for Dynamic Construction of Digital Twin Mirrors of Non-Communicating Intelligent Agents (Corresponding to 1) This embodiment addresses the problem that statically constructed digital twin images cannot adapt to dynamic changes in physical agents. It combines a Generative Adversarial Network (GAN) to construct a high-fidelity, dynamically adaptable digital twin image. The specific steps are as follows: Step 1: Comprehensive Data Collection and Feature Fusion for Physical Intelligent Agents Basic data collection: Collect all-dimensional basic data of physical intelligent agents, including hardware parameters (model, performance indicators, structural design), software configuration (operating system, algorithm model, decision rules), historical operation data (state time series data, task execution records, fault handling logs), and environmental adaptation data (operational performance in different scenarios), and construct a full-dimensional basic dataset; Feature fusion: A multimodal feature fusion algorithm is used to extract and fuse features from different types of basic data, including hardware features, software features, operational features, and environmental features, to obtain a high-dimensional comprehensive feature set that fully reflects the inherent attributes and dynamic performance of the physical intelligent agent.

[0023] Step 2: Digital Twin Modeling of Generative Adversarial Networks (GANs) GAN Model Architecture Design: Constructing an agent-based twin GAN model, including a generator and a discriminator: Generator: Employs a deep convolutional neural network (CNN) architecture, taking a high-dimensional comprehensive feature set as input to generate virtual image data of the physical intelligent agent, including virtual hardware status, virtual decision-making process, and virtual operational performance; Discriminator: It adopts a convolutional neural network architecture. The input is the virtual mirror data output by the generator and the real data of the physical agent. The output is the probability of judging the authenticity of the data. Model training objective: Through adversarial training between the generator and the discriminator, the virtual image data generated by the generator can accurately simulate the real state of the physical intelligent agent, while the discriminator cannot distinguish between virtual and real data, thus achieving high-fidelity modeling of the virtual image.

[0024] Step 3: Dynamic updating and optimization of the digital twin image Incremental data collection: During the operation of the physical intelligent agent, incremental operation data (state data under new scenarios, task execution records, and fault handling logs) are collected in real time to build an incremental dataset; Mirror dynamic update: The incremental learning algorithm is adopted. The incremental dataset is input into the trained GAN model to dynamically fine-tune the parameters of the generator and discriminator, so that the virtual mirror can continuously learn the new state and new performance of the physical agent and adapt to the dynamic changes of the physical agent. Image fidelity assessment: Construct an image fidelity assessment index system, including state mapping accuracy, decision behavior consistency, and task execution effect similarity. Periodically assess the fidelity of the virtual image. If the assessment result does not reach the preset threshold, increase the amount of incremental data collection and readjust the model.

[0025] Step 4: Functional Expansion and Application of Digital Twin Mirrors Functional expansion: Based on high-fidelity virtual images, expand functions such as virtual simulation, fault simulation, and policy verification. Through virtual images, simulate the operating state of physical intelligent agents under different scenarios and faults, and provide a virtual test environment for full life cycle management. Data interaction channel: Establish a two-way data interaction channel between the physical intelligent agent and the digital twin mirror. The physical intelligent agent transmits real-time status data to the virtual mirror, and the virtual mirror feeds back information such as optimization decisions and fault warnings to the physical intelligent agent, realizing virtual-physical linkage.

[0026] Efficiency Enhancement Principle Existing digital twin mirrors are built based on static parameters and can only simulate the fixed state of physical agents, failing to adapt to dynamic changes. This embodiment generates high-fidelity virtual mirror data through adversarial training of GAN models, which can accurately simulate the dynamic operating state of physical agents, solving the core problem of "poor adaptability of static mirrors". The existing virtual image technology lacks a dynamic update mechanism. As the physical agent runs for a long time, the image fidelity gradually decreases. This embodiment uses incremental learning for dynamic updates, which enables the virtual image to continuously absorb incremental data, adapt to the state changes and operating experience of the physical agent, and maintain high fidelity over a long period of time. Existing virtual images have limited functionality, serving only as status displays. This embodiment expands functionality to achieve diverse functions such as virtual simulation, fault simulation, and policy verification, upgrading the digital twin image from a "status image" to a "management tool," providing strong support for full lifecycle management. Existing virtual-physical interaction technologies involve one-way data transmission and lack a linkage mechanism. This embodiment achieves real-time linkage between physical intelligent agents and virtual images through a two-way data interaction channel. The optimization decisions of the virtual images can directly guide the operation of physical intelligent agents, thereby improving the closed-loop nature of control.

[0027] Example 3: Non-digital twin-driven full lifecycle closed-loop management algorithm (corresponding to 3 and 4) This embodiment addresses the lack of a closed-loop mechanism in full lifecycle management and the disconnect between segmented management. It combines the segmented management algorithm in step 3 and the cross-stage closed-loop optimization algorithm in step 4 to achieve collaborative management of the intelligent agent throughout its entire lifecycle. The specific steps are as follows: Step 1: Modeling a segmented management and control strategy for the entire lifecycle Lifecycle phase division: The entire lifecycle of the communication intelligent agent is divided into four core phases: deployment phase, operation phase, maintenance phase, and iteration phase, and the control objectives and core tasks of each phase are clearly defined; Segmented control strategy design: Deployment phase: Based on the virtual simulation function of digital twin image, simulate the running effect under different deployment locations and different configuration parameters, optimize the deployment plan, formulate precise deployment strategy, and ensure the deployment is successful on the first try; Operational phase: Based on real-time virtual-real mapping, the operational status of physical intelligent agents is monitored through digital twin mirrors, and dynamic operation control strategies are formulated, including dynamic resource allocation, adjustment of decision parameters, and early warning of anomalies; Maintenance phase: Simulate the failure evolution process through digital twin mirroring, locate the root cause of the failure, and formulate precise maintenance strategies, including rapid failure handling and preventive maintenance plans; Iteration phase: Based on the full-cycle operation data accumulated from the digital twin mirror, analyze existing defects and optimization space, and formulate iterative upgrade strategies, including model parameter optimization, function expansion, and algorithm updates.

[0028] Step 2: Construction of Cross-Stage Data Flow and Closed-Loop Optimization Mechanism Data flow channel: Establish a full lifecycle data flow channel based on digital twin mirrors. Control data, operational data, and effect feedback data at each stage are all stored in the database of the digital twin mirror, enabling cross-stage data sharing. Deployment phase data → Operation phase: Provides a deployment configuration baseline for management and control strategies during the operation phase; Operational phase data → Maintenance phase: Provides data support for fault diagnosis and preventive maintenance; Maintenance phase data → Iteration phase: Provides a basis for defect analysis for iterative upgrades; Iteration phase data → Deployment phase: Provides optimization references for the deployment of new intelligent agents; Closed-loop optimization model: Construct a cross-stage closed-loop optimization model with the goal of achieving optimal performance throughout the entire lifecycle. Use the control strategies of each stage as decision variables, simulate the performance of different strategy combinations through virtual simulation of digital twin mirrors, and solve for the optimal strategy combination. Strategy Iteration and Update: Based on feedback information from data flow, the control strategies at each stage are adjusted regularly through a closed-loop optimization model to achieve coordinated optimization of strategies at each stage.

[0029] Step 3: Dynamic Implementation and Effectiveness Evaluation of Full Lifecycle Management Control strategy execution: Control strategies at each stage are distributed to physical intelligent agents or control systems through digital twin mirrors. The deployment stage executes optimized deployment plans, the operation stage executes dynamic control strategies, the maintenance stage executes precise maintenance actions, and the iteration stage executes upgrade and update operations. Effectiveness Evaluation: Construct a full lifecycle management effectiveness evaluation index system, including deployment success rate, operational stability, maintenance efficiency, and iteration and upgrade effectiveness, and regularly evaluate the implementation effectiveness of management strategies at each stage; Strategy Adjustment: If the control effect in a certain stage does not reach the preset threshold, the reasons are analyzed through virtual simulation of the digital twin mirror, the corresponding control strategy is adjusted, and the strategy of other related stages is simultaneously affected through the data flow channel to ensure the optimal control effect throughout the entire cycle.

[0030] Step 4: Full Lifecycle Risk Prediction and Proactive Management Risk prediction model: Based on the full-lifecycle data accumulated from the digital twin mirror, a full lifecycle risk prediction model is built, and machine learning algorithms are used to identify potential risks at each stage (such as deployment failure, operational failure, untimely maintenance, and poor iteration results). Proactive management strategy: For anticipated risks, proactive management strategies are developed. The management effect is simulated in advance through digital twin mirroring. After optimizing the strategy parameters, the strategies are issued and executed to achieve early risk avoidance. Risk Response Experience Library: Record the process and results of each risk prediction and proactive control as experience and store them in the experience library to provide a reference for subsequent risk management.

[0031] Efficiency Enhancement Principle Existing technologies for full lifecycle management are executed in segments and in isolation, with a lack of coordination between strategies at each stage. This embodiment solves the problem of "disconnected segmented management" by modeling segmented management strategies, designing exclusive management strategies for each stage, and achieving data sharing between stages through cross-stage data flow channels. Existing technologies lack scientific cross-stage optimization mechanisms, and strategy adjustments are highly blind. This embodiment uses a closed-loop optimization model to optimize strategies at each stage with the goal of achieving the best effect throughout the entire life cycle, thereby upgrading the control strategy from "local optimum" to "global optimum". Existing technologies for evaluating control effectiveness only target a single stage and cannot reflect the overall performance throughout the entire lifecycle. This embodiment uses a full lifecycle evaluation index system to comprehensively evaluate the control effectiveness at each stage and cross-stage synergy, providing a scientific basis for strategy optimization. Existing technologies rely on passive response (such as maintenance after a failure occurs) and lack proactive prediction capabilities. This embodiment uses a risk prediction model and proactive control strategies to achieve early risk avoidance, upgrading the control mode from "passive response" to "proactive prediction," and significantly improving the stability of operation throughout the entire lifecycle.

[0032] Example 4: Non-dynamic game-driven cross-domain collaborative adaptation algorithm (corresponding to 5 and 6) This embodiment addresses the problem of insufficient dynamic adaptability in cross-domain collaboration and reliance on preset collaboration rules. It combines the dynamic game algorithm of 5 and the conflict resolution algorithm of 6 to achieve adaptive adjustment of the collaboration strategy. The specific steps are as follows: Step 1: Cross-domain collaborative scenario modeling and dynamic state perception Scenario modeling: Define the cross-domain collaborative scenario, including the set of communication agents participating in the collaboration, cross-domain link resources, and collaborative task objectives (such as cross-domain data transmission, resource sharing, and collaborative fault handling), and clarify the local objectives of each agent and the global collaborative objectives; Dynamic state perception: Through the digital twin mirrors of each intelligent agent, the dynamic state (load, remaining resources, running status) of the physical intelligent agent and the cross-domain network environment state (link bandwidth, latency, congestion trend) are perceived in real time, and a cross-domain dynamic state set is constructed to provide real-time data support for collaborative adaptation.

[0033] Step 2: Construction of Dynamic Game-Based Collaborative Adaptation Model Game participants and strategy space: Participants: The digital twins of each communicating agent act as participants in the game, representing the physical agents in making decisions; Strategy Space: Each participant's strategy space is a set of collaborative actions, including resource allocation ratios, data transmission paths, collaborative execution sequences, and fault handling coordination methods; Reward Function Design: Design a dynamic reward function that integrates local and global objectives, and adaptively adjust the reward function according to changes in the cross-domain dynamic state set.

[0034] in For cross-domain dynamic states, As a participant Collaborative strategies For the collaborative strategy of other participants, For local target weight coefficients, As a participant The local benefits are positively correlated with the utilization rate of its own resources and the task completion rate. For overall benefits (positively correlated with cross-domain collaboration efficiency and overall resource utilization); Game Theory Solution: A dynamic Nash equilibrium solution algorithm is adopted, based on real-time cross-domain dynamic states. The Nash equilibrium solution of the game model is solved iteratively to obtain the optimal collaborative strategy of each participant.

[0035] Step 3: Dynamic resolution of cross-domain collaborative conflicts Conflict Identification: Construct a collaborative conflict identification model based on global state awareness of digital twin mirroring to identify collaborative conflict types. Resource conflict: Multiple agents compete for the same cross-domain resource (such as bandwidth or computing resources). Timing conflicts: Conflicting execution timings (e.g., simultaneously occupying cross-domain links); Target conflict: Local targets conflict with global targets or other agents' local targets; Dynamic conflict resolution strategy: Resource conflict: Based on the game payoff function, cross-domain resources are reallocated to prioritize the resource needs of high-yield strategies. Timing conflicts: Optimize the timing of collaborative execution and adopt a time-sharing reuse mechanism to maximize global collaborative efficiency; Target conflict: Adjust local target weight coefficients This ensures that the benefit functions of each participant are aligned with the global objective; Execution of conflict resolution strategy: The conflict resolution strategy is distributed to each physical agent through a digital twin mirror, and the coordination strategy is adjusted to achieve real-time conflict resolution.

[0036] Step 4: Dynamically update and optimize the collaborative strategy Real-time status feedback: During cross-domain collaborative execution, collaborative effect data (such as collaborative efficiency, resource utilization, and task completion rate) and dynamic status change data are collected in real time through digital twin mirrors; Online strategy update: Based on real-time feedback data, the payoff function parameters and strategy space of the game model are dynamically adjusted, and the Nash equilibrium solution is solved again to achieve online adaptive update of the collaborative strategy; Accumulating collaborative experience: Record the experience of each collaborative adaptation and conflict resolution as data and store it in the experience library of the digital twin mirror to optimize the solution efficiency of the game model and the adaptation accuracy of the collaborative strategy.

[0037] Efficiency Enhancement Principle Existing cross-domain collaborative strategies are based on preset fixed patterns and cannot adapt to dynamic state changes. This embodiment innovates the modeling of dynamic game models, incorporates cross-domain dynamic states into the game framework, and enables collaborative strategies to be adjusted in real time with state changes, thus solving the core problem of "poor adaptability of static strategies". The existing technology has a fixed reward function, which cannot balance the dynamic changes of local and global objectives. This embodiment designs a dynamic reward function, in which the weight coefficients and target weights are adaptively adjusted according to the state changes, thereby achieving a dynamic balance between local and global objectives. Existing technologies suffer from lagging collaborative conflict identification and a lack of targeted resolution strategies. This embodiment achieves real-time conflict identification through global state perception of digital twin mirrors, and designs exclusive resolution strategies for different conflict types, thereby improving the timeliness and effectiveness of conflict resolution. Existing collaborative strategies lack online updates and experience accumulation mechanisms, resulting in a lack of continuous improvement in adaptability. This embodiment addresses this by updating strategies online and accumulating an experience base, enabling the adaptation accuracy of collaborative strategies to continuously improve with the number of collaborations, thus forming a virtuous cycle of "state awareness - strategy adaptation - experience accumulation".

Claims

1. A method for full lifecycle management and cross-domain collaboration of a communication intelligent agent driven by a digital twin, characterized in that, include: S1: Collect comprehensive data on the hardware, software, operation, and environment of the physical agent. After multimodal feature fusion, input the data into the agent's twin GAN model to generate a high-fidelity digital twin image. Collect multi-dimensional state data of the physical agent. After preprocessing, achieve real-time synchronization between the virtual and real worlds through a state mapping model with a temporal attention mechanism. The core formula is: , , The mapping accuracy is optimized through dynamic calibration and online learning; S2: Divide the entire lifecycle of the intelligent agent into deployment, operation, maintenance and iteration stages, and design exclusive management and control strategies for each stage; Establish a digital twin-driven cross-stage data flow channel and closed-loop optimization model to achieve optimal results throughout the entire lifecycle and collaboratively optimize strategies at each stage; construct a lifecycle assessment indicator system and risk prediction model to achieve proactive control and strategy iteration. S3: Based on digital twin mirror perception of cross-domain dynamic states, construct a dynamic game-theoretic collaborative adaptation model, with a payoff function. The optimal cooperative strategy is solved by dynamic Nash equilibrium. Identify resource, temporal, and objective conflicts, and adopt targeted resolution strategies to achieve real-time conflict resolution; dynamically update the game model and collaborative strategies based on real-time feedback to accumulate collaborative experience.

2. The method according to claim 1, characterized in that, The agent twin GAN model described in step S1 includes a generator and a discriminator. The generator adopts a deep convolutional neural network architecture, and the discriminator adopts a convolutional neural network architecture. A high-fidelity virtual image is generated through adversarial training, and the image is dynamically updated through an incremental learning algorithm.

3. The method according to claim 1, characterized in that, The physical state data mentioned in step S1 includes six dimensions: hardware, decision, resources, network, task, and environment. Preprocessing includes frame segmentation, anomaly removal, normalization, and feature selection. The loss function of the mapping model is the mean squared error loss, and the parameters are optimized using the Adam algorithm.

4. The method according to claim 1, characterized in that, The segmented management and control strategy mentioned in step S2 includes a precise deployment strategy in the deployment phase, a dynamic operation management and control strategy in the operation phase, a precise maintenance strategy in the maintenance phase, and an iterative upgrade strategy in the iteration phase. The cross-phase data flow channel enables data sharing between the various phases.

5. The method according to claim 1, characterized in that, The full lifecycle assessment index system mentioned in step S2 includes deployment success rate, operational stability, maintenance efficiency, and iterative upgrade effect. The risk prediction model identifies potential risks at each stage based on full lifecycle data and formulates proactive management strategies.

6. The method according to claim 1, characterized in that, The participants in the dynamic game model described in step S3 are digital twins of each agent. The policy space includes resource allocation, transmission path, execution sequence, and fault coordination actions. The dynamic Nash equilibrium solution algorithm realizes the real-time generation of the optimal cooperative strategy.

7. The method according to claim 1, characterized in that, The collaborative conflicts mentioned in step S3 include resource conflicts, timing conflicts, and target conflicts, which are resolved using targeted strategies such as resource reallocation, timing optimization, and weight adjustment.

8. The method according to claim 1, characterized in that, The collaborative strategy described in step S3 is dynamically updated through real-time feedback data, and the accumulated collaborative experience is stored in the digital twin mirror experience library to optimize the solution efficiency and adaptation accuracy of the game model.

9. A communication intelligent agent digital twin-driven full lifecycle management and cross-domain collaboration system, characterized in that, To implement the method of any one of claims 1-8, comprising: Unit 1: Used for full-dimensional data collection and feature fusion of physical intelligent agents, constructing intelligent agent twin GAN model to generate high-fidelity digital twin image, and realizing real-time synchronization and dynamic calibration of virtual and real through temporal attention mapping model; The second unit is used for modeling segmented management and control strategies for the entire life cycle of intelligent agents, establishing cross-stage data flow channels and closed-loop optimization models, and achieving proactive management and control throughout the entire life cycle through effect evaluation and risk prediction. The third unit is used for cross-domain dynamic state perception and dynamic game model construction, solving for optimal cooperative strategies, identifying and resolving cooperative conflicts, and realizing dynamic updating and optimization of cross-domain cooperative strategies.

10. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the full lifecycle management and cross-domain collaboration method driven by digital twins of any one of claims 1 to 8.

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