Emergency network replenishment technology based on perception-decision closed-loop cycle

Abstract

This invention provides an emergency network replenishment technology based on a perception-decision closed-loop cycle, comprising: sensing unprocessed emergency events occurring in the topological physical network of measurement, control, and operations resources; invoking a dual-drive decision engine to determine the recommended emergency network replenishment strategy corresponding to the unprocessed emergency event; performing multi-dimensional quantitative evaluation of the recommended emergency network replenishment strategy; and outputting the evaluated recommended emergency network replenishment strategy. When this solution senses an unprocessed emergency event occurring in the topological physical network of measurement, control, and operations resources, it determines the recommended emergency network replenishment strategy corresponding to the unprocessed emergency event through a dual-drive decision engine. It performs multi-dimensional quantitative evaluation of the recommended emergency network replenishment strategy and outputs the evaluated recommended emergency network replenishment strategy, eliminating reliance on manual analysis and decision-making. This addresses the challenges of widely distributed and dynamically changing spatial networks, ensuring the needs of emergency scenarios are met.

Description

Technical Field

[0001] This invention relates to the field of emergency network replenishment technology, specifically to an emergency network replenishment technology based on a perception-decision closed-loop cycle. Background Technology

[0002] Emergency network restoration refers to the process of rapidly reconstructing the network topology and reallocating resources to restore and maintain critical business capabilities after some network nodes or links fail due to natural interference or equipment malfunction. Currently, emergency network restoration typically relies on manual analysis and decision-making, which is insufficient to handle widely distributed and dynamically changing spatial networks. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide an emergency network replenishment technology based on a perception-decision closed-loop cycle to solve the problem that manual analysis and decision-making cannot cope with widely distributed and dynamically changing spatial networks.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0005] The first aspect of this invention discloses an emergency network replenishment technology based on a perception-decision closed-loop cycle, the emergency network replenishment technology comprising:

[0006] Unresolved emergency events occurring in the topological physical network of sensing, measurement, operation, and control resources;

[0007] The dual-drive decision engine is invoked to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed.

[0008] The proposed emergency network replenishment strategies are evaluated quantitatively from multiple dimensions.

[0009] Output the recommended emergency network replenishment strategy that has passed the evaluation.

[0010] Preferably, the dual-drive decision engine includes at least a case library;

[0011] The dual-drive decision engine is invoked to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed, including:

[0012] Calculate the similarity between the emergency event to be processed and each emergency event case in the case library;

[0013] Obtain the response plan corresponding to the emergency event case with the highest similarity to the emergency event to be processed and the similarity is greater than a threshold, so as to obtain the emergency network replenishment strategy to be recommended for the emergency event to be processed.

[0014] Preferably, the dual-drive decision engine further includes a model library;

[0015] After calculating the similarity between the emergency event to be processed and each emergency event case in the case library, the method further includes:

[0016] If the similarity between the emergency event to be processed and each of the emergency event cases is less than a threshold, the artificial intelligence model in the model library is invoked to generate the recommended emergency network replenishment strategy corresponding to the emergency event to be processed.

[0017] Preferred options also include:

[0018] The operation research optimization algorithm in the model library is invoked to optimize the emergency network replenishment strategy to be recommended.

[0019] Preferably, the recommended emergency network replenishment strategy is subjected to a multi-dimensional quantitative evaluation, including:

[0020] The execution effect of the proposed emergency network replenishment strategy was simulated using a digital twin platform, and the corresponding simulation results were obtained.

[0021] The simulation results are subjected to multi-dimensional quantitative evaluation to obtain corresponding evaluation results, which are used to characterize whether the recommended emergency network replenishment strategy passes the evaluation.

[0022] Preferably, after obtaining the corresponding evaluation results, the following are also included:

[0023] If the evaluation result indicates that the recommended emergency network replenishment strategy has failed the evaluation, the step of calling the dual-drive decision engine to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed is executed, so as to re-determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed, until a recommended emergency network replenishment strategy that has passed the evaluation is obtained.

[0024] Preferably, the execution effect of the recommended emergency network replenishment strategy is simulated using a digital twin platform to obtain the corresponding simulation results, including:

[0025] The recommended emergency network replenishment strategy is executed by driving a virtual model in a simulation environment using a digital twin platform, so as to deduce the deduction results reflecting the execution effect of the recommended emergency network replenishment strategy.

[0026] Preferably, pending emergency events occurring in the topological physical network of sensing, measurement, and control resources include:

[0027] Using digital twin technology, we can sense and process emergency events occurring in the topological physical network of measurement, control, and operation resources.

[0028] A second aspect of the present invention discloses a computer device, comprising: a processor and a memory, the processor and the memory being connected via a bus; wherein, the processor is used to call and execute a program stored in the memory; the memory is used to store the program, the program being used to implement the emergency network replenishment technology based on perception-decision closed-loop rotation disclosed in the first aspect of the present invention.

[0029] The third aspect of this invention discloses a storage medium storing computer-executable instructions for executing the emergency network replenishment technology based on perception-decision closed-loop rotation disclosed in the first aspect of this invention.

[0030] An emergency network replenishment technology based on a perception-decision closed-loop cycle, provided by the above embodiments of the present invention, includes: sensing an unprocessed emergency event occurring in the topological physical network of measurement, control, and telecommunications resources; invoking a dual-drive decision engine to determine a recommended emergency network replenishment strategy corresponding to the unprocessed emergency event; performing multi-dimensional quantitative evaluation of the recommended emergency network replenishment strategy; and outputting the evaluated recommended emergency network replenishment strategy. When this solution senses an unprocessed emergency event occurring in the topological physical network of measurement, control, and telecommunications resources, it determines a recommended emergency network replenishment strategy corresponding to the unprocessed emergency event through a dual-drive decision engine. It performs multi-dimensional quantitative evaluation of the recommended emergency network replenishment strategy and outputs the evaluated recommended emergency network replenishment strategy, eliminating reliance on manual analysis and decision-making. This addresses the challenges of wide-area distribution and dynamic changes in spatial networks, ensuring the needs of emergency scenarios are met. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0032] Figure 1 A flowchart illustrating an emergency network replenishment technology based on a perception-decision closed-loop cycle, provided as an embodiment of the present invention;

[0033] Figure 2 A flowchart for determining the recommended emergency network replenishment strategy provided in this embodiment of the invention;

[0034] Figure 3 An example diagram of the overall architecture of an emergency network replenishment technology based on a perception-decision closed-loop cycle provided in an embodiment of the present invention;

[0035] Figure 4 This is a structural block diagram of an emergency network repair device provided in an embodiment of the present invention;

[0036] Figure 5 Another structural block diagram of an emergency network replenishment device provided in an embodiment of the present invention;

[0037] Figure 6 This is another structural block diagram of an emergency network repair device provided in an embodiment of the present invention;

[0038] Figure 7 This is another structural block diagram of an emergency network repair device provided in an embodiment of the present invention. Detailed Implementation

[0039] 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.

[0040] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0041] In a large-scale network system within a high-dimensional space, emergency network replenishment refers to the process of rapidly reconstructing the network topology and reallocating resources to restore and maintain critical business capabilities after some network nodes or links fail due to natural interference or equipment malfunction. The core challenges of emergency network replenishment lie in the timeliness of the response and the scientific nature of the decision-making.

[0042] Currently, emergency network replenishment typically relies on manual analysis and decision-making, which is insufficient to handle widely distributed and dynamically changing spatial networks. There is an urgent need to introduce an automated closed-loop management paradigm capable of real-time perception, intelligent decision-making, and forward-looking verification.

[0043] To address the challenge of dynamically adjusting measurement, control, and operations (PCA) resources to handle unforeseen tasks, this invention proposes an emergency network replenishment technology based on a perception-decision closed-loop cycle. When an unresolved emergency event is detected in the topological physical network of PCA resources, a dual-drive decision engine determines a recommended emergency network replenishment strategy corresponding to the event. The recommended emergency network replenishment strategy undergoes multi-dimensional quantitative evaluation, and a strategy that passes the evaluation is output. This eliminates reliance on manual analysis and decision-making, enabling the technology to cope with widely distributed and dynamically changing spatial networks and ensuring the needs of emergency scenarios are met.

[0044] It should be noted that the emergency network replenishment technology based on perception-decision closed-loop cycle proposed in this solution is equivalent to an emergency network replenishment method and related equipment based on perception-decision closed-loop cycle, as well as an emergency network replenishment method, system, electronic equipment and storage medium.

[0045] See Figure 1 The diagram illustrates a flowchart of an emergency network replenishment technology based on a perception-decision closed-loop cycle provided by an embodiment of the present invention. Figure 1 Includes the following steps:

[0046] Step S101: Emergency events to be processed occurring in the topological physical network of sensing, measurement, operation and control resources.

[0047] In the specific implementation of step S101, in response to the need for emergency network replenishment, digital twin technology is used to sense and process emergency events occurring in the topological physical network of measurement, operation and control resources.

[0048] In practice, a scheduling resource situational representation system is constructed using digital twin technology. This system reveals "which measurement, control, and operations resources can work collaboratively for a common task at the current moment." Based on this system, the state of the topological physical network of measurement, control, and operations resources is accurately and in real-time perceived, emergency network replenishment needs are located, potential bottlenecks and affected critical business flows are quickly identified, and thus, pending emergency events are perceived.

[0049] Step S102: Call the dual-drive decision engine to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed.

[0050] In the specific implementation step S102, after sensing the occurrence of an emergency event to be processed, the dual-drive decision engine is invoked to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed.

[0051] Step S103: Conduct a multi-dimensional quantitative evaluation of the recommended emergency network replenishment strategy.

[0052] In the specific implementation step S103, after obtaining the emergency network replenishment strategy to be recommended, the strategy is not directly sent to the resource scheduling platform for execution. Instead, the strategy is first injected into the digital twin platform, and the execution effect of the strategy is simulated using the digital twin platform to obtain the corresponding simulation results.

[0053] Specifically, after receiving the emergency network replenishment strategy to be recommended, the digital twin platform uses the digital twin platform to drive the virtual model to execute the recommended emergency network replenishment strategy in a simulation environment (high-fidelity simulation environment) in order to deduce the deduction results reflecting the execution effect of the recommended emergency network replenishment strategy.

[0054] It should be noted that during the execution of the recommended emergency network replenishment strategy by the virtual model, the execution effect, such as the scheduling target behavior and the scheduling of measurement, operation and control resources, is predicted based on discrete event simulation, thereby obtaining the simulation results.

[0055] After obtaining simulation results that reflect the implementation effect of the recommended emergency network replenishment strategy, the simulation results are quantitatively evaluated in multiple dimensions to obtain the corresponding evaluation results. These evaluation results are used to characterize whether the recommended emergency network replenishment strategy passes the evaluation.

[0056] The multi-dimensional quantitative evaluation includes at least the following evaluation contents: determining whether the simulation results meet the constraints, and determining whether the simulation results are energy-efficient.

[0057] If the evaluation results indicate that the recommended emergency network replenishment strategy passes the evaluation, proceed to step S104.

[0058] If the evaluation results indicate that the recommended emergency network replenishment strategy has failed the evaluation, step S102 is executed to re-determine the recommended emergency network replenishment strategy corresponding to the emergency event to be handled, until a recommended emergency network replenishment strategy that has passed the evaluation is obtained.

[0059] Step S104: Output the recommended emergency network replenishment strategy that has passed the evaluation.

[0060] It should be noted that the emergency network replenishment strategy that has passed the evaluation is the emergency network replenishment strategy that has been verified in the digital world to meet all constraints and has the best performance.

[0061] In the specific implementation of step S104, if the evaluation result indicates that the recommended emergency network replenishment strategy has passed the evaluation, the recommended emergency network replenishment strategy that has passed the evaluation will be output to the resource scheduling platform for execution.

[0062] In this embodiment of the invention, when an emergency event is detected in the topological physical network of measurement, control, and operation resources, a dual-drive decision engine determines the recommended emergency network replenishment strategy corresponding to the emergency event. The recommended emergency network replenishment strategy is then subjected to multi-dimensional quantitative evaluation, and the evaluated recommended emergency network replenishment strategy is output. This eliminates reliance on manual analysis and decision-making, enabling the response to widely distributed and dynamically changing spatial networks and ensuring the needs of emergency scenarios are met.

[0063] In some specific embodiments, the dual-drive decision engine includes a case-based reasoning (CBR) library and a model-based reasoning (MBR) library, which takes into account both the timeliness and scientific nature of decision-making.

[0064] The case library stores a large number of emergency incident cases (i.e., historical emergency incidents), as well as the emergency response plans and their implementation results.

[0065] Regarding the above embodiments of the present invention Figure 1 The content of "calling the dual-drive decision engine to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed" in step S102, please refer to [link / reference]. Figure 2 This document illustrates a flowchart illustrating the decision-making process for recommending emergency network replenishment strategies, as provided in an embodiment of the present invention. Figure 2 Includes the following steps:

[0066] Step S201: Calculate the similarity between the emergency event to be processed and each emergency event case in the case library.

[0067] In the specific implementation step S201, when an emergency event to be processed is detected, the similarity between the emergency event to be processed and each emergency event case in the case library is calculated. That is, the emergency event to be processed is matched with each emergency event case in the case library to obtain the similarity between the emergency event to be processed and each emergency event case.

[0068] Step S202: Obtain the emergency response plan corresponding to the emergency event case with the highest similarity to the emergency event to be handled and the similarity is greater than the threshold, so as to obtain the emergency network replenishment strategy to be recommended for the emergency event to be handled.

[0069] In the specific implementation step S202, based on the similarity between the emergency event to be processed and each emergency event case, the emergency response plan corresponding to the emergency event case with the highest similarity to the emergency event to be processed and the similarity is greater than the threshold is obtained, thereby obtaining the emergency network replenishment strategy to be recommended for the emergency event to be processed.

[0070] In other words, the recommended emergency network replenishment strategy is the response plan corresponding to the emergency event case with the highest similarity to the emergency event to be handled and the similarity is greater than a threshold.

[0071] By leveraging a case study database to identify recommended emergency network replenishment strategies, decision-making time can be significantly reduced, providing a validated and reliable strategic foundation for emergency response and ensuring the timeliness of decision-making.

[0072] In practical applications, the following situation may occur: the similarity between the emergency event to be processed and each emergency event case is less than the threshold.

[0073] The situation where "the similarity between the emergency event to be processed and each emergency event case is less than the threshold" indicates that there are no emergency event cases to refer to.

[0074] In some specific embodiments, the model library integrates various operations research optimization algorithms and artificial intelligence models, such as integer programming and deep reinforcement learning models. If the similarity between the emergency event to be processed and each emergency event case is less than a threshold, the artificial intelligence model in the model library is invoked to generate a recommended emergency network replenishment strategy corresponding to the emergency event to be processed.

[0075] After obtaining the emergency network replenishment strategy to be recommended, the strategy can be optimized. In some specific embodiments, the operations research optimization algorithm in the model library is called to optimize the emergency network replenishment strategy to be recommended.

[0076] The above embodiments of the present invention Figure 2 This is an explanation of how to decide on the recommended emergency network replenishment strategy.

[0077] In summary, the overall architecture of the emergency network replenishment technology based on perception-decision closed-loop cycle proposed in this solution is as follows: Figure 3 As shown, the emergency network replenishment technology based on the perception-decision closed-loop cycle is mainly divided into the following three parts: perception level, decision level, and closed-loop cycle.

[0078] Perception level: In response to emergency network replenishment needs, digital twin technology is used to detect the occurrence of emergency events to be addressed.

[0079] At the decision-making level: A case library and a model library are used to determine the recommended emergency response strategy. Specifically, the response plan corresponding to an emergency event case highly similar to the emergency event to be addressed (with the highest similarity exceeding a threshold) is prioritized from the case library and used as the recommended emergency response strategy. If no recommended emergency response strategy can be found from the case library, it is then generated from the model library.

[0080] Closed-loop cycle: The proposed emergency network replenishment strategy is injected into the digital twin platform for millisecond-level simulation and evaluation. If the evaluation result indicates that the proposed emergency network replenishment strategy passes the evaluation, it is output to the resource scheduling platform for execution. If the evaluation result indicates that the proposed emergency network replenishment strategy fails the evaluation, the process returns to the decision-making level to re-determine the proposed emergency network replenishment strategy, until a recommended emergency network replenishment strategy that passes the evaluation is obtained. Through high-speed iteration of the closed-loop system of "perception-decision-simulation-evaluation-re-decision," an emergency network replenishment strategy that is verified in the digital world to meet all constraints and has optimal performance (i.e., the final output proposed emergency network replenishment strategy) is generated.

[0081] It should be noted that the core paradigm of cybernetics is the perception-decision closed loop, also known as the perception-action cycle or the OODA loop (Observe-Orient-Decide-Act), which is the basic model in cybernetics for describing the interaction between intelligent systems and their environment to achieve their goals.

[0082] In summary, this solution is based on the core paradigm of cybernetics to implement an emergency network replenishment technology based on a closed-loop perception-decision cycle. It utilizes digital twin technology to perceive the real-time status of scheduled resources, pinpoint network replenishment needs, and quickly identify potential bottlenecks and affected critical business flows. A dual-driven intelligent decision-making approach, combining a "case library + model library," balances the timeliness and scientific rigor of decision-making. Finally, the proposed emergency network replenishment strategy is injected into the digital twin platform for millisecond-level simulation and evaluation. Based on the evaluation results, feedback is used to optimize various technologies and models, forming a spiraling research path to solve the intelligent scheduling problem of measurement, operation, and control resources in multiple scenarios (general scenarios, hotspot scenarios, and emergency scenarios), achieving intelligent autonomy.

[0083] Therefore, this solution has the following advantages: the final output of the recommended emergency network replenishment strategy has been repeatedly simulated in the digital twin, and its execution success rate and reliability are greatly guaranteed. This transforms the emergency response from a passive "post-event remediation" mode to an active "prior optimization" mode, significantly improving the resilience, autonomy, and mission support capabilities of the measurement, operation, and control network.

[0084] Corresponding to the emergency network replenishment technology based on perception-decision closed-loop rotation provided in the above embodiments of the present invention, see also... Figure 4 The present invention also provides a structural block diagram of an emergency network replenishment device, which includes: a sensing unit 401, a decision-making unit 402, an evaluation unit 403, and an output unit 404;

[0085] The sensing unit 401 is used to sense pending emergency events occurring in the topological physical network of measurement, operation and control resources.

[0086] In specific implementation, the sensing unit 401 is used to: use digital twin technology to sense pending emergency events occurring in the topological physical network of measurement, control and operation resources.

[0087] Decision unit 402 is used to call the dual-drive decision engine to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed.

[0088] Evaluation unit 403 is used to conduct multi-dimensional quantitative evaluation of the recommended emergency network replenishment strategy.

[0089] The output unit is used to output the recommended emergency network replenishment strategy that has passed the evaluation.

[0090] Preferred, combined Figure 4 See Figure 5 This diagram illustrates another structural block diagram of an emergency network replenishment device provided in an embodiment of the present invention. The dual-drive decision engine includes at least a case library and a model library, and the decision unit 402 includes:

[0091] The calculation module 4021 is used to calculate the similarity between the emergency event to be processed and each emergency event case in the case library.

[0092] The acquisition module 4022 is used to acquire the emergency response plan corresponding to the emergency event case with the highest similarity to the emergency event to be handled and the similarity is greater than a threshold, so as to obtain the emergency network replenishment strategy to be recommended for the emergency event to be handled.

[0093] Preferred, combined Figure 5 See Figure 6 This diagram illustrates another structural block diagram of an emergency network replenishment device provided in an embodiment of the present invention. The decision unit 402 further includes:

[0094] The generation module 4023 is used to generate a recommended emergency network replenishment strategy for the emergency event to be processed if the similarity between the emergency event to be processed and each emergency event case is less than a threshold.

[0095] Optimization module 4024 is used to call the operations research optimization algorithm in the model library to optimize the emergency network replenishment strategy to be recommended.

[0096] Preferred, combined Figure 4 See Figure 7 This diagram illustrates another structural block diagram of an emergency network replenishment device provided in an embodiment of the present invention. The evaluation unit 403 includes:

[0097] The simulation module 4031 is used to simulate the execution effect of the recommended emergency network replenishment strategy using the digital twin platform and obtain the corresponding simulation results.

[0098] In specific implementation, the deduction module 4031 is used to: drive the virtual model to execute the recommended emergency network replenishment strategy in the simulation environment using the digital twin platform, so as to deduce the deduction results that reflect the execution effect of the recommended emergency network replenishment strategy.

[0099] The evaluation module 4032 is used to perform multi-dimensional quantitative evaluation of the simulation results and obtain the corresponding evaluation results. The evaluation results are used to characterize whether the emergency network replenishment strategy to be recommended has passed the evaluation.

[0100] Preferably, the evaluation module 4032 is further configured to: if the evaluation result indicates that the recommended emergency network replenishment strategy has failed the evaluation, execute the decision-making unit 402 to re-determine the recommended emergency network replenishment strategy corresponding to the emergency event to be handled, until a recommended emergency network replenishment strategy that has passed the evaluation is obtained.

[0101] Preferably, the present invention also provides a computer device, including: a processor and a memory, the processor and the memory being connected via a bus; wherein, the processor is used to call and execute a program stored in the memory; the memory is used to store the program, the program being used to implement the emergency network replenishment technology based on perception-decision closed-loop rotation disclosed in the above embodiments.

[0102] Preferably, the present invention also provides a storage medium storing computer-executable instructions for executing the emergency network replenishment technology based on perception-decision closed-loop rotation disclosed in the above embodiments.

[0103] In summary, this invention provides an emergency network replenishment technology based on a perception-decision closed-loop cycle. When an emergency event awaiting processing is detected in the topological physical network of measurement, control, and operation resources, a dual-drive decision engine determines the recommended emergency network replenishment strategy corresponding to the event. The recommended emergency network replenishment strategy undergoes multi-dimensional quantitative evaluation and outputs the evaluated recommended emergency network replenishment strategy, eliminating reliance on manual analysis and decision-making. This addresses the challenges of wide-area distribution and dynamic changes in spatial networks, ensuring the needs of emergency scenarios are met.

[0104] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0105] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0106] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An emergency network replenishment technology based on a perception-decision closed-loop cycle, characterized in that, The emergency network repair technology includes: Unresolved emergency events occurring in the topological physical network of sensing, measurement, operation, and control resources; The dual-drive decision engine is invoked to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed. The proposed emergency network replenishment strategies are evaluated quantitatively from multiple dimensions. Output the recommended emergency network replenishment strategy that has passed the evaluation.

2. The emergency network replenishment technology according to claim 1, characterized in that, The dual-drive decision engine includes at least a case library; The dual-drive decision engine is invoked to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed, including: Calculate the similarity between the emergency event to be processed and each emergency event case in the case library; Obtain the response plan corresponding to the emergency event case with the highest similarity to the emergency event to be processed and the similarity is greater than a threshold, so as to obtain the emergency network replenishment strategy to be recommended for the emergency event to be processed.

3. The emergency network replenishment technology according to claim 2, characterized in that, The dual-drive decision engine also includes a model library; After calculating the similarity between the emergency event to be processed and each emergency event case in the case library, the method further includes: If the similarity between the emergency event to be processed and each of the emergency event cases is less than a threshold, the artificial intelligence model in the model library is invoked to generate the recommended emergency network replenishment strategy corresponding to the emergency event to be processed.

4. The emergency network replenishment technology according to claim 3, characterized in that, Also includes: The operation research optimization algorithm in the model library is invoked to optimize the emergency network replenishment strategy to be recommended.

5. The emergency network replenishment technology according to claim 1, characterized in that, The proposed emergency network replenishment strategies are evaluated quantitatively across multiple dimensions, including: The execution effect of the proposed emergency network replenishment strategy was simulated using a digital twin platform, and the corresponding simulation results were obtained. The simulation results are subjected to multi-dimensional quantitative evaluation to obtain corresponding evaluation results, which are used to characterize whether the recommended emergency network replenishment strategy passes the evaluation.

6. The emergency network replenishment technology according to claim 5, characterized in that, After obtaining the corresponding evaluation results, it also includes: If the evaluation result indicates that the recommended emergency network replenishment strategy has failed the evaluation, the step of calling the dual-drive decision engine to determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed is executed, so as to re-determine the recommended emergency network replenishment strategy corresponding to the emergency event to be processed, until a recommended emergency network replenishment strategy that has passed the evaluation is obtained.

7. The emergency network replenishment technology according to claim 5, characterized in that, The execution effect of the proposed emergency network replenishment strategy is simulated using a digital twin platform, and the corresponding simulation results are obtained, including: The recommended emergency network replenishment strategy is executed by driving a virtual model in a simulation environment using a digital twin platform, so as to deduce the deduction results reflecting the execution effect of the recommended emergency network replenishment strategy.

8. The emergency network replenishment technology according to claim 1, characterized in that, Unresolved emergency events occurring in the topological physical network of sensing, measurement, operation, and control resources include: Using digital twin technology, we can sense and process emergency events occurring in the topological physical network of measurement, control, and operation resources.

9. A computer device, characterized in that, include: A processor and a memory are connected via a bus; wherein the processor is used to call and execute a program stored in the memory; The memory is used to store a program for implementing the emergency network replenishment technology based on perception-decision closed-loop cycle as described in any one of claims 1-8.

10. A storage medium, characterized in that, The storage medium stores computer-executable instructions for executing the emergency network replenishment technology based on perception-decision closed-loop rotation as described in any one of claims 1-8.

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