A smart switch management and control method and system for a power communication network

Abstract

The application discloses a kind of intelligent switch management and control method and system for power communication network, it is related to power communication network management technical field, including: by real-time acquisition switch node's load level, forwarding delay, available bandwidth, power consumption and historical communication log construction behavior atlas, and based on state change generates behavior entropy vector;Behavior entropy vector is introduced as constraint condition in quantum game model, and resource competition between nodes is optimized using quantum annealing algorithm, to obtain the resource allocation scheme that satisfies bandwidth, delay and power consumption constraints;The optimization result is verified in a distributed manner by combining the proof of stake and byzantine fault tolerance mechanism of blockchain, and the node equity weight is adjusted by entropy value and power consumption data to ensure the fairness and credibility of the verification process;According to the verification result, dynamically update the routing table and bandwidth allocation parameters, improve resource utilization and fault recovery capability, enhance the stability and security of power communication network.

Description

Technical Field

[0001] This invention relates to the field of power communication network management technology, and in particular to a method and system for controlling intelligent switches in power communication networks. Background Technology

[0002] With the rapid development of smart grids and distributed energy resources, power communication networks, as a crucial infrastructure supporting the safe and efficient operation of power grids, are constantly increasing in scale and complexity. Switches, as key nodes in power communication networks, undertake important functions such as routing, data forwarding, and resource scheduling. However, the existing management and control models of power communication networks generally rely on a centralized architecture, i.e., unified scheduling and configuration of switches through a central controller. While this approach facilitates global control, it is prone to single points of failure in large-scale dynamic network environments, leading to reduced network reliability. Furthermore, it suffers from high response latency when facing sudden failures or drastic traffic fluctuations.

[0003] In recent years, Software-Defined Networking (SDN) technology has been introduced into power communication systems to achieve flexible forwarding control and bandwidth management. However, due to the high reliability and low latency requirements of power communication networks, traditional routing optimization and bandwidth allocation based on classical algorithms often face NP-hard problems, resulting in high computational complexity and a tendency to get trapped in local optima, failing to meet the requirements for millisecond-level fault recovery. Furthermore, existing methods lack effective node stability measurement mechanisms, making it difficult to dynamically reflect the uncertainty of network states, leading to insufficient stability of resource allocation strategies.

[0004] On the other hand, blockchain technology has been applied to some extent in distributed energy trading and grid settlement, and its decentralized and immutable characteristics have provided new ideas for the management and control of power communication networks. However, existing blockchain consensus mechanisms mostly rely on computation or monetary rights and lack deep integration with the operating status of power communication networks. They cannot reflect the differences in power consumption and stability of nodes, and therefore cannot serve as a reliable basis for the management and control of switches.

[0005] Meanwhile, quantum computing has shown potential advantages in combinatorial optimization problems, but current research is mostly still in the theoretical exploration stage. It has not yet combined quantum optimization with the practical management problems of power communication networks, let alone formed an organic integration with blockchain consensus mechanisms. The fragmented application of existing technologies has led to a disconnect between network optimization and trusted verification, lacking a unified algorithmic logic and adaptive mechanism. Summary of the Invention

[0006] In view of the aforementioned existing problems, the present invention is proposed.

[0007] Therefore, this invention provides an intelligent switch management method for power communication networks to address the problems of low optimization efficiency, poor policy stability, and insufficient decision-making credibility in existing power communication networks, which suffer from high computational complexity in switch resource allocation, lack of node stability measurement, and insufficient distributed trust mechanism.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a smart switch management and control method for power communication networks, which includes: real-time collection of network status data of switch nodes, construction of behavior graphs, and generation of behavior entropy vectors of nodes based on the data of the behavior graphs;

[0010] In the quantum game model, the behavioral entropy vector is used as a constraint, and the quantum annealing algorithm is used to optimize the resource competition between nodes and output the optimal resource allocation scheme.

[0011] By using blockchain's proof-of-stake and Byzantine fault tolerance mechanisms, the resource allocation scheme output by quantum game is verified, and the behavioral entropy vector is combined with node power consumption data to adjust the stake weights.

[0012] Based on the verified resource allocation scheme, adjust the switch configuration to achieve dynamic updates of routing tables and bandwidth, and monitor network performance in real time to ensure the sustainability of optimization effects.

[0013] As a preferred embodiment of the intelligent switch management method for power communication networks described in this invention, the network status data includes the load level, forwarding delay, available bandwidth, node power consumption, and historical communication logs of the switch nodes.

[0014] Constructing a behavior graph involves treating switch nodes as nodes in the behavior graph and communication links between nodes as edges. The weight of each edge is determined by a combination function of forwarding delay, available bandwidth, and load level.

[0015] As a preferred embodiment of the intelligent switch management method for power communication networks described in this invention, the generation of the node's behavioral entropy vector includes: within a preset time window, counting the number of state changes of each switch node in terms of load level, forwarding delay, available bandwidth, node power consumption, and historical communication logs, calculating the corresponding occurrence probability based on the number of changes, determining the entropy value reflecting the stability and uncertainty of the switch node's state based on the occurrence probability, and combining the entropy values ​​of all switch nodes in the node order to form a behavioral entropy vector.

[0016] As a preferred embodiment of the intelligent switch management method for power communication networks described in this invention, the optimization of resource competition among switch nodes includes mapping the behavioral entropy vector to the quantum perturbation weight of each switch node, and adjusting the bias and annealing rate in the quantum annealing process according to the quantum perturbation weight.

[0017] During quantum annealing, quantum measurements are periodically performed to obtain a set of candidate classical strategies, each corresponding to a routing and bandwidth allocation scheme. The candidate sets are pre-validated according to executability rules, which include link bandwidth limits, minimum reserved bandwidth, link delay constraints, and node power consumption limits. The pre-validated candidate classical strategies are sorted according to a preset sorting criterion, and the optimal solution is selected to form the optimal resource allocation scheme.

[0018] As a preferred embodiment of the intelligent switch management method for power communication networks described in this invention, the system objective function includes a performance term, an energy efficiency term, and a stability term; the performance term is determined by end-to-end latency and link utilization, the energy efficiency term is determined by the total power consumption of the switch nodes, and the stability term is determined by the behavioral entropy vector; during quantum annealing, the annealing bias and annealing rate are adjusted according to the stability term; during the screening process of the candidate classical strategy set, schemes that do not meet the threshold are eliminated according to the stability term, and the corresponding system objective function value is output.

[0019] As a preferred embodiment of the intelligent switch management method for power communication networks described in this invention, the resource allocation scheme for verifying the output of the quantum game includes, during the proof-of-stake process, combining the behavioral entropy vector of the switch node with the node power consumption data to determine the stake weight of each node.

[0020] During the Byzantine fault tolerance process, each switch node votes according to the aforementioned equity weight. When the voting result meets the preset consistency condition, the resource allocation scheme is confirmed to be valid. When abnormal voting is detected, re-verification is performed and the equity weight of the abnormal node is reduced to no more than a preset threshold. After verification is completed, the confirmed resource allocation scheme is written into the blockchain ledger, and the equity weight is adjusted according to the updated behavior entropy vector and node power consumption data.

[0021] As a preferred embodiment of the intelligent switch management method for power communication networks described in this invention, the adjustment of switch configuration includes updating the routing table and bandwidth allocation parameters. After the configuration adjustment is completed, the link utilization, end-to-end latency and node power consumption are monitored in real time. When the monitoring results exceed a preset threshold, a feedback mechanism is triggered to upload the monitoring results to the scheduling and control unit to restart the resource optimization process.

[0022] Secondly, the present invention provides an intelligent switch management and control system for power communication networks, comprising: a data acquisition module for acquiring network status data of switch nodes, constructing a behavior graph, and generating a behavior entropy vector of the nodes;

[0023] The quantum game optimization module is used in quantum game models to optimize resource competition between nodes by using behavioral entropy vectors as constraints and quantum annealing algorithm to output the optimal resource allocation scheme.

[0024] The blockchain verification module is used to verify the resource allocation scheme based on proof of stake and Byzantine fault tolerance mechanism. During the verification process, the stake weight of the node is adjusted by combining the behavioral entropy vector and the node power consumption data, and the confirmed resource allocation scheme is written into the blockchain ledger.

[0025] The configuration adjustment module is used to update the switch's routing table and bandwidth allocation parameters according to the verified resource allocation scheme.

[0026] The performance monitoring and feedback module is used to monitor link utilization, end-to-end latency and node power consumption after configuration adjustments are completed. When the monitoring results exceed the preset threshold, the feedback mechanism is triggered to upload the monitoring results to the scheduling and control unit to restart the resource optimization process.

[0027] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the intelligent switch management method for power communication networks as described in the first aspect of the present invention.

[0028] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent switch management method for power communication networks as described in the first aspect of the present invention.

[0029] The beneficial effects of this invention are as follows: By constructing a behavioral graph and introducing a behavioral entropy vector, a quantitative measure of the uncertainty of the operating state of switch nodes is achieved, thereby dynamically reflecting the differences in network stability; by combining the quantum annealing algorithm to solve the resource competition among nodes, a near-globally optimal resource allocation scheme is quickly obtained while satisfying the constraints of link bandwidth, latency, and power consumption; the allocation results are distributedly verified using the proof-of-stake and Byzantine fault tolerance mechanisms of blockchain, and a fair and reliable consensus process is achieved by combining the behavioral entropy vector with node power consumption data to adjust the stake weights; finally, the routing table and bandwidth parameters are dynamically adjusted based on the verified allocation scheme, and the network optimization effect is maintained continuously through real-time monitoring and feedback mechanisms. These measures can effectively improve the resource utilization and fault recovery capability of power communication networks, and enhance the stability and security of the system in complex dynamic environments. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of a smart switch management method for power communication networks. Detailed Implementation

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0033] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0035] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a smart switch management and control method for power communication networks, including the following steps:

[0036] S1: Collect network status data of switch nodes in real time, construct a behavior graph, and generate the behavior entropy vector of the node based on the data in the behavior graph.

[0037] When executing S1, network status data is first collected in real time from the switch nodes in the power communication network. The network status data includes the load level, forwarding delay, available bandwidth, node power consumption, and historical communication logs of the switch nodes.

[0038] A behavioral graph is constructed based on the collected network status data. Switch nodes are considered as nodes in the behavioral graph, and communication links between nodes are considered as edges. The weight of each edge is calculated using a combination function of forwarding delay, available bandwidth, and load level. Specifically, it is defined as follows:

[0039]

[0040] in, Represents a node With nodes The weight of the edges between them; This indicates a forwarding delay; Indicates available bandwidth; Indicates the load level; , , This represents the preset adjustment coefficient. This weighting calculation method comprehensively reflects the performance characteristics of the communication link.

[0041] After the behavior graph is constructed, the behavior entropy vectors of the nodes are generated. Specifically, within a preset time window, the number of state changes for each switch node in terms of load level, forwarding latency, available bandwidth, node power consumption, and historical communication logs are counted, and the corresponding probability of occurrence is calculated based on the number of changes. Based on the probability of occurrence, determine the entropy value, which reflects the stability and uncertainty of the switch node state:

[0042]

[0043] in, Indicates the first The behavioral entropy value of each switch node; This indicates the total number of state categories for the node within the preset time window; This indicates the index of the possible state categories that the switch node may exhibit within a preset time window; Represents a node In the The probability of occurrence in a class state.

[0044] Finally, the entropy values ​​of all switch nodes are combined sequentially according to node order to form a behavioral entropy vector:

[0045]

[0046] Where n represents the total number of switch nodes; This represents the entropy value of the behavior of the last switch node; Indicates the first One switch node.

[0047] The behavioral entropy vector serves as a constraint input for the subsequent quantum game model, guiding the optimization of resource competition among nodes and ensuring that the resource allocation scheme can fully consider the uncertainty and dynamic changes in network operation.

[0048] By collecting data on the load level, forwarding latency, available bandwidth, node power consumption, and historical communication logs of switch nodes in real time, and constructing behavioral graphs using a combination of functions, the transmission latency, bandwidth availability, and node operating pressure of the communication link can be comprehensively reflected in a single model. This avoids the one-sidedness of traditional methods that rely on a single indicator for link measurement.

[0049] By introducing behavioral entropy vectors based on behavioral graphs, the state changes of nodes within a preset time window are statistically analyzed and entropy values ​​are calculated, allowing the uncertainty and stability of network states to be quantified. This approach not only reflects the dynamic fluctuation characteristics of nodes but also provides calculable stability constraints, offering reliable input for subsequent optimization processes.

[0050] Compared to existing technologies, traditional network management is mostly based on static indicators or average values ​​for scheduling, which makes it difficult to reflect the instability of node operation, causing optimization results to easily fail in dynamic environments. By adopting behavioral entropy vectors, the randomness and changing trends of nodes can be explicitly characterized, enabling resource allocation strategies to better fit actual network operation conditions and improving the robustness and adaptability of power communication networks in complex environments.

[0051] In traditional power communication network management methods, link metrics mostly employ static weight models, such as calculations based solely on average delay, single-link bandwidth, or simple load statistics. While these methods can provide a reference in static or near-static environments, they fail to fully reflect the real-time characteristics of nodes and links in large-scale dynamic network environments. In particular, given the randomness and volatility inherent in network operation, traditional models lack the ability to characterize uncertainty.

[0052] This step introduces two key breakthroughs through the combination of behavioral graphs and behavioral entropy vectors:

[0053] The definition of multi-indicator composite edge weights: The weight function does not rely on a single parameter, but combines forwarding delay, available bandwidth, and load level through preset adjustment coefficients to form dynamic weights. This means that link performance is mapped to a multi-dimensional function value, rather than a single static parameter.

[0054] Entropy-driven stability characterization: Above the graph level, an entropy-based vectorized description is introduced, transforming the various state changes of nodes within a time window into probability distributions, and calculating the entropy value as a stability measure. This approach is not a simple statistical mean, but rather quantifies the "randomness" and "unpredictability" of the network, providing entirely new constraints for subsequent optimization.

[0055] This "two-layer structure" design (the structural nature of the graph + the uncertainty of the entropy vector) represents a logical reconstruction of traditional graph modeling and network measurement methods. It requires transforming multi-source heterogeneous indices into a unified edge weight model; it necessitates introducing probability statistics and entropy theory into node feature quantification, forming mathematical objects that can be embedded in optimization algorithms; and it requires ensuring the connectivity between front-end metrics and subsequent quantum annealing constraints, which has not yet been addressed in existing power communication network optimization methods.

[0056] Therefore, this solution is not merely a parameter extension, but rather provides a completely new metric logic that transforms traditional static optimization into a dynamic—uncertainty-driven input method.

[0057] S2: In the quantum game model, the behavioral entropy vector is used as a constraint. The quantum annealing algorithm is used to optimize the resource competition between nodes and output the optimal resource allocation scheme.

[0058] The behavioral entropy vector obtained in step S1 is converted into perturbation weights that can be used in the quantum annealing process. Specifically, the entropy value of each switch node is first normalized to obtain the node's stability index:

[0059]

[0060] in, Indicates the first The normalized stability index of each switch node; Indicates the first The total number of status categories of each switch node within a preset time window; Indicates the index of the switch node; This indicates the total number of switch nodes.

[0061] This normalization process can constrain the entropy value to the interval [0, 1], thus facilitating subsequent parameter adjustment.

[0062] In quantum annealing, the aforementioned stability indices are used to correct the annealing bias and annealing rate of each node:

[0063]

[0064]

[0065] in, Indicates the first Quantum bias of each switch node; Indicates the first The initial bias value of each switch node; Indicates the bias adjustment coefficient; This represents the average stability index of all switch nodes; Indicates the first Each switch node at time Annealing rate; Indicates the base annealing rate; This represents the annealing rate adjustment coefficient.

[0066] Thus, at nodes with high entropy, increasing the perturbation reduces the convergence rate, allowing for a broader search; at nodes with low entropy, maintaining a smaller perturbation accelerates convergence. The Hamiltonian of quantum annealing can be expressed as:

[0067]

[0068]

[0069] in, The Hamiltonian representing the quantum annealing process; Represents a switch node With nodes The coupling coefficient between them; Indicates the first One quantum bit in Pauli operators for direction; Indicates the first One quantum bit in Pauli operators for direction; Indicates the first One quantum bit in Pauli operators for direction; Represents a switch node With nodes Edge weights of links between them; This represents the normalization adjustment coefficient; Represents a behavioral graph.

[0070] During the annealing evolution process, at fixed time intervals By performing periodic quantum measurements, a set of candidate classical strategies is obtained. In practice, the measurement interval can be 1 to 10 milliseconds, and the number of measurements... The value can be set between 50 and 500, and the specific value can be adjusted according to the network size and computing resources. Each candidate set corresponds to a routing and bandwidth allocation scheme.

[0071] To ensure the feasibility of candidate solutions, rule checks are required. Feasibility rules include: (1) upper limit of link bandwidth and minimum reserved bandwidth constraints:

[0072]

[0073] in, An index representing the business flow; Represents a set of business flows; Indicates business flow Does it occupy the link? Indicator variables; Indicates business flow bandwidth; Indicates link The physical bandwidth limit; Indicates link Minimum reserved bandwidth; Indicates the link index; Represents a set of links.

[0074] (2) Link delay constraints:

[0075]

[0076] in, Indicates link The inherent forwarding delay; Represents the congestion-added delay function Indicating in candidate solutions Downlink Utilization rate; Indicates link Maximum allowed forwarding delay; Indicates the first A number of classic candidate strategy solutions.

[0077] (3) Node power consumption upper limit constraint:

[0078]

[0079] in, Indicates the switch node index; Represents a set of switch nodes; Indicating in candidate solutions Next An indicator variable indicating whether a switch node is active; Indicating in candidate solutions Next Power consumption of each switch node; This indicates the upper limit of system power consumption.

[0080] In addition, screening based on stability parameters is also necessary. A threshold needs to be set. When the stability term of a candidate solution is:

[0081]

[0082] in, Indicating in candidate solutions The stability term.

[0083] If the value exceeds this threshold, the scheme is rejected. The threshold can be set in the range (0, 1), for example, taking... .

[0084] For candidate solutions that pass the above rules, further optimization is performed based on the system objective function. The objective function does not use weighted combinations, but rather an unweighted maximum / minimum criterion, defined as follows:

[0085] Performance items:

[0086]

[0087] Energy efficiency items:

[0088]

[0089] Stability term:

[0090]

[0091] The overall objective function is:

[0092]

[0093] in, Indicate candidate solutions Performance items; Indicating in candidate solutions Downstream business flow End-to-end delay; Indicates business flow The maximum end-to-end latency service level; Indicating in candidate solutions Downlink Utilization rate; Indicates link Maximum permissible utilization rate; Indicate candidate solutions Energy efficiency items; A reference value representing power consumption normalization; Indicate candidate solutions Stability terms; Indicate candidate solutions The overall objective function value; Indicate candidate solutions; This represents the set of candidate solutions that satisfy all executability rules.

[0094] Among the candidate solutions, select the one with the minimum objective function value. This serves as the final optimal resource allocation scheme.

[0095] In a quantum game theory model, behavioral entropy vectors are introduced as constraints, and the quantum annealing algorithm is used to optimize resource competition among switch nodes. This allows resource allocation to no longer rely solely on static parameters but to dynamically consider the uncertainties of node operation. By adaptively adjusting the bias and annealing rate during the annealing process, the stability differences among different nodes can be reflected in the search process. This allows high-fluctuation nodes to have a wider search range, while low-fluctuation nodes converge faster, thereby improving the overall reliability and efficiency of the optimization.

[0096] In the periodic quantum measurement process, a set of candidate classical strategies is obtained, and pre-verification is performed using bandwidth limits, minimum reserved bandwidth, link delay constraints, and node power consumption limits. This allows for effective screening based on the quantum computing output, ensuring the final result is engineering-feasible. From the screened set of schemes, an unweighted minimax objective function is used for selection, avoiding biases that might arise from manually setting weights. This ensures that performance, energy efficiency, and stability are considered comprehensively under fair conditions, resulting in an allocation scheme that better meets actual operational needs.

[0097] Compared to schemes that rely on classical heuristic algorithms, this method can find a near-global optimal solution in a shorter time while ensuring the scheme has constrained feasibility and long-term stability, thus improving the resource allocation capability of power communication networks in dynamic environments.

[0098] Existing optimization methods for power communication networks typically employ classic graph theory algorithms or heuristic algorithms (such as shortest route, genetic algorithm, and ant colony algorithm). These methods are effective for small- to medium-scale problems, but they often suffer from high computational complexity and are prone to getting trapped in local optima under large-scale networks and complex constraints. Furthermore, traditional optimization frameworks lack the introduction of constraints on node stability, failing to describe the uncertainty of network states in a probabilistic sense.

[0099] The core technology of this step lies in entropy-driven quantum perturbation mapping: by transforming behavioral entropy vectors into perturbation weights for the quantum annealing process, a mapping relationship is established from statistical characteristics to quantum computing parameters. This mapping not only requires mathematical computability but also needs to ensure that it can correctly guide the search direction during the annealing evolution process.

[0100] An adaptive adjustment mechanism for annealing parameters: By introducing stability differences in annealing bias and annealing rate, different nodes obtain different optimization paths based on their state fluctuations. This mechanism avoids the problems of "overfitting" or "under-searching" caused by uniform annealing parameters.

[0101] Application of unweighted minimax objective function: Introducing a fair min-max strategy in the scheme selection process avoids the drawbacks of traditional weighted sum methods that rely on subjective weight settings, and ensures that performance, energy efficiency and stability can be measured simultaneously.

[0102] This design requires bridging the entropy value of information theory with the physical model of quantum annealing, and further ensuring a clear computational path and verifiability in the discrimination process of the objective function. This logical reconstruction tightly integrates the uncertainty of the network state with the iterative process of quantum optimization, forming an executable optimization method for dynamic network environments.

[0103] S3: By using blockchain's proof-of-stake and Byzantine fault tolerance mechanisms, the resource allocation scheme output by quantum game is verified, and the behavioral entropy vector is combined with node power consumption data to adjust the stake weights.

[0104] The optimal resource allocation scheme output in step S2 Submissions are made to various exchange nodes in the blockchain network for verification via the consensus mechanism. The verification process employs a combination of Proof-of-Stake (PoS) and Byzantine Fault Tolerance (BFT) mechanisms.

[0105] During the proof-of-stake phase, the stake weight of each switch node is determined not only by its basic stake in the blockchain system, but also by a combination of behavioral entropy vectors and node power consumption data:

[0106]

[0107] in, Represents a node equity weight, Represents a node The behavioral entropy value, Represents a node power consumption, The normalization factor ensures that the sum of the weights of all nodes is 1. In this way, nodes with high behavioral stability and low power consumption receive higher weights in the consensus process, thereby encouraging low-power, stable nodes to play a leading role in network management.

[0108] In the Byzantine fault tolerance mechanism, all nodes allocate resources based on their respective equity weights. A vote is taken. If the vote reaches a consensus with more than two-thirds of the weighted nodes, the resource allocation scheme is considered valid. If a malicious node is detected tampering with the scheme or submitting an invalid vote, the BFT mechanism will trigger a re-verification process and remove the stakes of the abnormal node to prevent malicious behavior from undermining the overall security of the system. During the voting process, it is also necessary to check whether the resource allocation scheme conforms to the topological consistency of the behavioral graph, that is, whether the allocation path exists in the set of links described by the graph, and whether the corresponding edge weight does not exceed the preset limit. If the scheme violates the graph topology or link constraints, it is directly determined as an invalid scheme and a re-verification process is triggered.

[0109] After verification, the final confirmed resource allocation scheme is recorded in the blockchain ledger to ensure immutability and traceability. Simultaneously, the joint result of the behavioral entropy vector and node power consumption dynamically updates the stake weights, providing input conditions for subsequent rounds of resource optimization and consensus verification, forming a closed-loop process of data-optimization-verification.

[0110] After the resource allocation scheme is generated, it is verified through blockchain's proof-of-stake and Byzantine fault tolerance mechanisms, ensuring that the allocation result is not only mathematically feasible but also trustworthy in a distributed environment. Combining behavioral entropy vectors with node power consumption data to determine stake weights allows nodes with stable operation and low power consumption to have greater influence in the consensus process, avoiding reliance solely on computing power or static configuration for voting and contributing to fair decision-making that aligns with the characteristics of power communication networks.

[0111] During the consensus process, each node votes according to the adjusted stake weights. If a majority of weighted nodes confirm the plan, the plan is written into the blockchain ledger and stored for a long time to ensure the immutability and traceability of the result. If abnormal voting or malicious nodes are detected, the Byzantine fault tolerance mechanism triggers a re-verification process and reduces the stake of the abnormal nodes, thereby reducing their impact on subsequent voting and improving the system's anti-attack capability and fault tolerance level.

[0112] This step maps information-theoretic metrics (behavioral entropy vectors) and physical energy consumption metrics (node ​​power consumption) into consensus weights, embedding them into a two-layer logic of PoS and BFT. This process involves cross-domain fusion of data dimensions and redefinition of consensus rules, requiring that weight allocation both reflects the operational state and forms a stable mechanism in distributed voting. In this way, deep coupling between resource optimization results and distributed verification is achieved, which, compared to traditional methods that rely solely on centralized control or a single consensus parameter, better guarantees the reliability and long-term stable operation of power communication networks in large-scale dynamic environments.

[0113] S4: Based on the verified resource allocation scheme, adjust the switch configuration to achieve dynamic updates of the routing table and bandwidth, and monitor network performance in real time to ensure the continuity of optimization effects.

[0114] The resource allocation scheme, verified by the blockchain, is distributed to each switch node for dynamic adjustment of its forwarding configuration. The adjustment process includes two aspects: first, updating the routing table entries of the nodes so that data traffic is forwarded according to the new path; second, modifying the bandwidth allocation ratio of the interfaces to ensure that the link resources can meet the business needs.

[0115] During routing table updates, each switch node loads the set of forwarding paths confirmed by consensus. The corresponding routing entries are then written into the control plane, thereby enabling end-to-end traffic path switching. During bandwidth allocation, each node follows the bandwidth ratio parameters specified in the plan. Bandwidth is segmented at its outgoing interfaces to ensure the isolation of business traffic and quality of service.

[0116] After the configuration adjustment is completed, the real-time monitoring phase begins. The following metrics are collected through the performance observer: (1) Link utilization sequence , used to measure the dynamic changes in link load. (2) End-to-end delay sequence , used to monitor the transmission performance of critical business flows. (3) Node energy consumption sequence It is used to track the energy efficiency level of a device after a new configuration is implemented.

[0117] When the observed metrics show significant deviations, such as link utilization exceeding a threshold... or end-to-end latency exceeds the threshold If the deviation is detected, a feedback mechanism is triggered, uploading the deviation information to the scheduling and control unit to initiate a new round of resource optimization. This ensures that the optimization effect remains continuous in a dynamic network environment.

[0118] in, This represents the set of forwarding paths confirmed by consensus. Indicates in path Upload to link The bandwidth ratio parameter; Indicates at time link Utilization rate; Indicates at time Business Flow End-to-end delay; Indicates at time switch node Energy consumption; The monitoring threshold representing link utilization; The monitoring threshold representing end-to-end latency.

[0119] After the resource allocation scheme is verified by blockchain consensus, the results are directly applied to the switch configuration, including dynamically updating the routing table and bandwidth allocation parameters, so that data traffic can be forwarded according to the verified path and bandwidth. This approach avoids the drawbacks of frequent manual adjustments in the traditional static configuration mode, achieves real-time performance and adaptability, and can significantly improve the scheduling efficiency of power communication networks in scenarios with fluctuating traffic.

[0120] After configuration adjustments are completed, real-time monitoring of link utilization, end-to-end latency, and node power consumption allows for timely capture of dynamic changes in network performance. When a metric exceeds a preset threshold, a feedback mechanism is immediately triggered, transmitting the deviation information back to the scheduling and control unit to restart the resource optimization process. This forms an automated closed-loop control system, ensuring that resource allocation is not merely a one-time optimization but remains effective as the network operating environment changes.

[0121] This step ensures seamless integration between optimized output and actual equipment configuration, while guaranteeing that the monitoring mechanism can capture anomalies with sufficient accuracy and timeliness, and coordinate with upper-level optimization algorithms. Unlike traditional network management methods that rely solely on fixed monitoring points or offline statistics, this method requires monitoring and feedback to have real-time triggering and adaptive update capabilities. This places high demands on data acquisition frequency, control command execution latency, and the compatibility between optimization algorithms and hardware interfaces, thereby ensuring the power communication network has continuous and stable operation capabilities in complex and dynamic environments.

[0122] This embodiment also provides an intelligent switch management and control system for power communication networks, including:

[0123] The data acquisition module is used to collect network status data of switch nodes, construct behavioral graphs, and generate behavioral entropy vectors for the nodes.

[0124] The quantum game optimization module is used in quantum game models to optimize resource competition between nodes by using behavioral entropy vectors as constraints and quantum annealing algorithm, and outputs the optimal resource allocation scheme.

[0125] The blockchain verification module is used to verify the resource allocation scheme based on proof-of-stake and Byzantine fault tolerance mechanism. During the verification process, the stake weight of the node is adjusted by combining the behavioral entropy vector and the node power consumption data, and the confirmed resource allocation scheme is written into the blockchain ledger.

[0126] The configuration adjustment module is used to update the switch's routing table and bandwidth allocation parameters based on a verified resource allocation scheme.

[0127] The performance monitoring and feedback module is used to monitor link utilization, end-to-end latency and node power consumption after configuration adjustments are completed. When the monitoring results exceed the preset threshold, the feedback mechanism is triggered to upload the monitoring results to the scheduling and control unit to restart the resource optimization process.

[0128] Example 2 is an embodiment of the present invention, which provides a method and system for controlling intelligent switches for power communication networks. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0129] A test scenario was constructed using 20 switch nodes and 40 communication links on a power communication network experimental platform. Each switch node is equipped with a load acquisition interface, a latency monitoring interface, and an energy consumption measurement module, capable of outputting load levels, forwarding latency, available bandwidth, power consumption, and historical communication logs in real time. The experimental platform ran continuously for 72 hours, with a sampling period of 100 milliseconds to ensure coverage of the nodes' operating status during peak and off-peak periods.

[0130] At the beginning of the experiment, the status of all nodes was collected, and a behavioral graph was constructed. In the behavioral graph, each switch node was represented as a node in the graph, and links were represented as edges. The edge weights were calculated using a combination function of latency, bandwidth, and load level. For example, when link e has a latency of 3.25 milliseconds, an available bandwidth of 940.50 Mbps, and an average load level of 62.30%, the calculated edge weight is approximately 0.58. All link weights were uniformly normalized and written into the graph structure for subsequent optimization.

[0131] After the behavior graph is completed, a behavior entropy vector is further generated. Within a preset time window (10 seconds), the number of state changes for each switch node across five categories is counted. For example, a node might experience 8 latency changes, 5 bandwidth changes, 6 load changes, 3 power consumption changes, and 2 log event changes within 10 seconds. Based on these frequency changes, the probability of occurrence for each state category is calculated, and the node's entropy value is obtained accordingly, reflecting its stability and uncertainty. The entropy values ​​of all 20 nodes are sequentially combined to form the behavior entropy vector, which serves as the input constraint for subsequent quantum annealing optimization.

[0132] In the quantum annealing process, the behavioral entropy vector is first mapped to quantum perturbation weights, and a Hamiltonian is established in conjunction with the optimization objective. Measurements are performed every 5 milliseconds during annealing, resulting in a set of 200 candidate policies. These candidates are then filtered using executability rules, including bandwidth limits (maximum 1Gbps for a single link), minimum reserved bandwidth (100Mbps), end-to-end latency constraints (≤5 milliseconds), and node power consumption limits (not exceeding 85W). A pre-validated set of 68 policies is then sorted using an unweighted minimax objective function, and the policy with the smallest objective value is ultimately selected as the resource allocation scheme.

[0133] During the blockchain verification phase, proof-of-stake and Byzantine fault tolerance mechanisms are used to confirm the results. Stake weights are calculated based on the node's behavioral entropy and power consumption data. For example, if a node's average power consumption during the experiment was 65.40W and its entropy was 0.42, its corresponding stake weight would be 0.031, which is then normalized and written into the voting process. The scheme is considered valid when the total valid vote weights of the 20 nodes in the network exceed two-thirds. If an abnormal vote is detected in one node during the experiment, that node's stake is automatically reduced to a preset lower limit, no longer significantly affecting subsequent voting. The final resource allocation scheme is written into the blockchain ledger, ensuring the result is immutable.

[0134] After the solution is confirmed, the switch automatically adjusts its routing table and bandwidth allocation ratio according to the new solution. For example, the bandwidth of the two outgoing interfaces of node 5 is adjusted from 400Mbps and 500Mbps to 300Mbps and 600Mbps respectively to adapt to changes in service traffic. After configuration, the real-time monitoring module is started to continuously collect link utilization, end-to-end latency, and node power consumption. When the link utilization exceeds 90% or the node power consumption exceeds 85W, the feedback mechanism is triggered to restart the optimization process.

[0135] The data recorded during the 72-hour experiment are as follows:

[0136] The average load level of the nodes fluctuated between 35.20% and 78.60%.

[0137] The average link latency was reduced from 4.85 milliseconds to 3.12 milliseconds.

[0138] The average link utilization rate increased from 62.40% to 81.50%.

[0139] The average power consumption of the node decreased from 72.30W to 65.80W.

[0140] The fault recovery time was reduced from 210.35 milliseconds to 128.50 milliseconds.

[0141] Overall resource utilization increased by approximately 18.60%.

[0142] Compared to traditional optimization methods based on shortest path and weighted load balancing:

[0143] The average link latency was 4.10 milliseconds, which is higher than the 3.12 milliseconds of this method.

[0144] The average power consumption of the node remained at 71.90W, which is higher than the 65.80W of this method.

[0145] The fault recovery time was 185.25 milliseconds, which is significantly slower than the 128.50 milliseconds of this method.

[0146] Experimental results demonstrate that this method can achieve efficient and continuous switch management in power communication networks. Firstly, from a link performance perspective, by employing behavioral graphs and behavioral entropy vectors for modeling, routing selection no longer relies on a single delay or bandwidth metric. Instead, it comprehensively considers multi-dimensional states and incorporates uncertainty metrics, thereby reducing the average link delay to 3.12 milliseconds in the experiment, significantly outperforming the traditional shortest path method based on single-metric weighting (4.10 milliseconds). This improvement indicates that the introduction of entropy vectors effectively enhances the stability and rationality of path selection.

[0147] Secondly, from an energy efficiency perspective, this method sets power consumption constraints during the quantum annealing optimization process and introduces a power consumption factor into the blockchain voting weights, making the resource allocation scheme more favorable to low-power nodes. In the experiment, the average power consumption was reduced to 65.80W, a reduction of approximately 6W compared to the traditional method. This energy efficiency improvement not only alleviates the operational pressure on individual nodes but also provides conditions for the green operation of the entire power communication network.

[0148] Furthermore, from the perspective of network robustness, the Byzantine fault tolerance mechanism identifies and handles abnormal nodes. In the experiment, after detecting an abnormal voting behavior in a node, its stake was reduced to the lower bound, preventing erroneous results from entering the final solution. This process ensures the credibility of distributed decision-making. Simultaneously, the fault recovery time was reduced from 185.25 milliseconds in the traditional method to 128.50 milliseconds, indicating that the feedback mechanism and continuous monitoring can trigger optimization cycles more quickly, thereby improving the network's adaptability in dynamic environments.

[0149] Finally, from the perspective of resource utilization, this method, through a multi-index-driven quantum annealing and unweighted maxima-minimas objective function ranking mechanism, allows performance, energy efficiency, and stability to be considered simultaneously. In experiments, link utilization was improved to 81.50%, an improvement of approximately 19% compared to traditional methods. This demonstrates that in resource-constrained power communication networks, this method can more fully tap the network's potential and enhance overall throughput.

[0150] In summary, the experimental data validates the advantages of this control method in terms of delay optimization, energy consumption control, robustness enhancement, and resource utilization improvement. Compared with traditional methods, this method demonstrates higher performance and sustainability in dynamic and complex environments, providing technical support for the reliable operation of power communication networks.

[0151] This embodiment also provides a computer device applicable to the intelligent switch management and control method for power communication networks, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent switch management and control method for power communication networks as proposed in the above embodiment.

[0152] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0153] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the intelligent switch management method for power communication networks as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling and managing intelligent switches in power communication networks, characterized in that, include: Real-time acquisition of network status data of switch nodes, construction of behavior graphs, and generation of behavior entropy vectors of nodes based on the data in the behavior graphs; The network status data includes the load level of the switch nodes, forwarding latency, available bandwidth, node power consumption, and historical communication logs. The generated node behavior entropy vector includes, within a preset time window, counting the number of state changes of each switch node in terms of load level, forwarding delay, available bandwidth, node power consumption and historical communication logs, calculating the corresponding occurrence probability based on the number of changes, determining the entropy value reflecting the stability and uncertainty of the switch node state based on the occurrence probability, and combining the entropy values ​​of all switch nodes in the node order to form a behavior entropy vector. The construction of the behavior graph includes: taking switch nodes as nodes in the behavior graph, taking communication links between nodes as edges, and calculating the weight of the edges by a combination function of forwarding delay, available bandwidth and load level; In the quantum game model, the behavioral entropy vector is used as a constraint, and the quantum annealing algorithm is used to optimize the resource competition between nodes and output the optimal resource allocation scheme. The optimization of resource competition between nodes by the quantum annealing algorithm includes: mapping the behavioral entropy vector to the perturbation weight of the switch node, and adjusting the bias parameters and annealing rate in the quantum annealing process according to the perturbation weight. By utilizing blockchain's proof-of-stake and Byzantine fault tolerance mechanisms, the resource allocation scheme output by the quantum game is verified, and the behavioral entropy vector is combined with node power consumption data to adjust the stake weights. The verification of the resource allocation scheme output by the quantum game includes: determining the node stake weights based on the behavioral entropy vectors and node power consumption data, and voting on the resource allocation scheme based on the stake weights under the Byzantine fault tolerance mechanism. When the voting results meet the preset consistency conditions, the resource allocation scheme is confirmed to be valid. Based on the verified resource allocation scheme, adjust the switch configuration to achieve dynamic updates of routing tables and bandwidth, and monitor network performance in real time to ensure the sustainability of optimization effects.

2. The intelligent switch management and control method for power communication networks as described in claim 1, characterized in that: The optimization of resource competition among switch nodes includes mapping the behavioral entropy vector to the quantum perturbation weight of each switch node, and adjusting the bias and annealing rate in the quantum annealing process according to the quantum perturbation weight. Quantum measurements are periodically performed during quantum annealing to obtain a set of candidate classical strategies, each of which corresponds to a routing and bandwidth allocation scheme. The periodic execution of quantum measurements during quantum annealing includes: acquiring the current state of the quantum system and performing measurement processing at preset time intervals during the quantum annealing evolution process to form the candidate classical strategy set; The candidate set is pre-validated according to executability rules, which include link bandwidth limit, minimum reserved bandwidth, link delay constraint, and node power consumption limit. The pre-validated candidate classic strategy set is further optimized based on the system objective function, and the candidate classic strategy with the smallest system objective function value is selected as the optimal resource allocation scheme.

3. The intelligent switch management and control method for power communication networks as described in claim 2, characterized in that: The system objective function includes a performance term, an energy efficiency term, and a stability term; the performance term is determined by end-to-end latency and link utilization, the energy efficiency term is determined by the total power consumption of the switch nodes, and the stability term is determined by the behavioral entropy vector. During quantum annealing, the annealing bias and annealing rate are adjusted according to the stability term. During the screening of the candidate classical strategy set, schemes that do not meet the threshold are eliminated according to the stability term, and the corresponding system objective function value is output.

4. The intelligent switch management and control method for power communication networks as described in claim 3, characterized in that: The resource allocation scheme for verifying the output of the quantum game includes, during the proof-of-stake process, combining the behavioral entropy vector of the switch node with the node power consumption data to determine the stake weight of each node. During Byzantine fault tolerance, each switch node votes according to its equity weight. When the voting result meets the preset consistency condition, the resource allocation scheme is confirmed to be valid. When abnormal voting is detected, re-verification is performed and the equity weight of the abnormal node is reduced to no more than a preset threshold. After verification, the confirmed resource allocation scheme is written into the blockchain ledger, and the equity weights are adjusted based on the updated behavior entropy vector and node power consumption data.

5. The intelligent switch management and control method for power communication networks as described in claim 4, characterized in that: The adjustment of switch configuration includes updating the routing table and bandwidth allocation parameters. After the configuration adjustment is completed, link utilization, end-to-end latency and node power consumption are monitored in real time. When the monitoring results exceed the preset threshold, a feedback mechanism is triggered to upload the monitoring results to the scheduling and control unit to restart the resource optimization process.

6. A smart switch management and control system for power communication networks, based on the smart switch management and control method for power communication networks according to any one of claims 1 to 5, characterized in that: The data acquisition module is used to collect network status data of switch nodes, construct behavioral graphs, and generate behavioral entropy vectors of nodes. The quantum game optimization module is used in a quantum game model to optimize resource competition between nodes by using behavioral entropy vectors as constraints and quantum annealing algorithm to output the optimal resource allocation scheme. The optimization of resource competition between nodes by quantum annealing algorithm includes: mapping behavioral entropy vectors to perturbation weights of switch nodes, and adjusting the bias parameters and annealing rate in the quantum annealing process according to the perturbation weights. The blockchain verification module is used to verify the resource allocation scheme based on proof-of-stake and Byzantine fault tolerance. During the verification process, the node's stake weight is adjusted by combining behavioral entropy vector and node power consumption data, and the confirmed resource allocation scheme is written into the blockchain ledger. The verification of the resource allocation scheme output by the quantum game includes: determining the node's stake weight based on the behavioral entropy vector and node power consumption data, and voting on the resource allocation scheme based on the stake weight under the Byzantine fault tolerance mechanism. When the voting result meets the preset consistency condition, the resource allocation scheme is confirmed to be valid. The configuration adjustment module is used to update the switch's routing table and bandwidth allocation parameters based on a verified resource allocation scheme. The performance monitoring and feedback module is used to monitor link utilization, end-to-end latency and node power consumption after configuration adjustments are completed. When the monitoring results exceed the preset threshold, the feedback mechanism is triggered to upload the monitoring results to the scheduling and control unit to restart the resource optimization process.

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