A method and device for cognitive network routing reconstruction with adaptive energy consumption rate weight

Abstract

The application discloses a cognitive network routing reconstruction method and device with adaptive energy consumption rate weight, and relates to the technical field of mobile communication. The method comprises the following steps: initializing the accumulated energy consumption rate weight value of each relay node; calculating the distance between a source node and a relay node; calculating the current state of each relay node; calculating the residual energy of each relay node; calculating the communication rate of the data packet transmitted by each relay node; determining the instantaneous energy consumption rate function of each relay node; determining the accumulated energy consumption rate weight value of each relay node; selecting the relay node of the next hop; judging whether the accumulated energy consumption rate weight value of each relay node converges; if the accumulated energy consumption rate weight value of each relay node does not converge, the residual energy of the relay node at the current time is determined as the residual energy at the last time, and the cycle is executed; otherwise, the cycle is stopped, and the network routing reconstruction is completed. By adopting the application, the quality of service requirement of the communication rate can be met while the energy consumption is optimized.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a cognitive network routing reconstruction method and device with adaptive energy consumption rate weight. Background Art

[0002] In modern communication networks, with the rapid development of technologies such as the Internet of Things and artificial intelligence, network heterogeneity and intelligence are becoming increasingly prominent. These characteristics are particularly important in complex battlefield network environments, where massive amounts of heterogeneous terminal devices are present. The dynamic and uncertain nature of battlefield environments requires networks to rapidly adapt to environmental changes, achieve energy balance, and optimize routing decisions. Cognitive networks are intelligent networks that can perceive environmental changes and automatically adjust their configuration to accommodate them. However, nodes in cognitive networks are often energy-constrained. Extending network life and improving energy efficiency within these limited energy constraints is a key issue. Summary of the Invention

[0003] To address the existing technical problem that nodes in cognitive networks are often energy-constrained, the present invention provides a method and apparatus for cognitive network routing reconstruction with adaptive energy consumption rate weighting. The technical solution is as follows:

[0004] On the one hand, a method for cognitive network routing reconstruction with adaptive energy consumption rate weight is provided. The method is implemented by a cognitive network routing reconstruction device with adaptive energy consumption rate weight, and the method includes:

[0005] S01, determining a source node, multiple relay nodes, and a target node;

[0006] S02. The source node obtains the status information of all relay nodes and initializes the cumulative energy consumption rate trade-off value of each relay node. The status information includes the energy consumption unit, the remaining energy at the previous moment, the transmission consumption energy, and the interference information of the malicious node;

[0007] S03. Calculate the distance between the source node and each relay node based on the node coordinates of the source node and the node coordinates of each relay node;

[0008] S04. Calculate the current state of each relay node based on the distance between the source node and each relay node and the consumed energy unit;

[0009] S05. Calculate the consumed energy of each relay node at the current moment according to the current state of each relay node, and calculate the remaining energy according to the consumed energy and the remaining energy at the previous moment;

[0010] S06. Calculate the communication rate of the transmission data packet of each relay node based on the transmission energy consumption and the interference information of the malicious node;

[0011] S07. Determine an instantaneous energy consumption rate distribution function of each relay node based on the residual energy of each relay node at the current moment and the communication rate of the transmitted data packet, and determine an instantaneous energy consumption rate function of each relay node based on the instantaneous energy consumption rate distribution function of each relay node;

[0012] S08. Determine a cumulative energy consumption rate trade-off value of each relay node according to the instantaneous energy consumption rate function of each relay node;

[0013] S09. Select the next-hop relay node based on the cumulative energy consumption rate trade-off value of each relay node;

[0014] S10. Determine whether the cumulative energy consumption rate trade-off value of each relay node converges. If not, determine the selected relay node as the new source node, determine the residual energy of the relay node at the current moment as the residual energy at the previous moment, and jump to S02 for loop execution; if all have converged, stop the loop execution and complete the network routing reconstruction.

[0015] On the other hand, a cognitive network routing reconstruction device with adaptive energy consumption rate weight is provided, which is applied to a cognitive network routing reconstruction method with adaptive energy consumption rate weight. The device includes:

[0016] A first determination module, configured to determine a source node, a plurality of relay nodes, and a target node;

[0017] An initialization module is used for the source node to obtain the status information of all relay nodes and initialize the cumulative energy consumption rate trade-off value of each relay node. The status information includes the energy consumption unit, the remaining energy at the previous moment, the transmission consumption energy, and the interference information of the malicious node;

[0018] A first calculation module is used to calculate the distance between the source node and each relay node according to the node coordinates of the source node and the node coordinates of each relay node;

[0019] A second calculation module is used to calculate the current state of each relay node according to the distance between the source node and each relay node and the consumed energy unit;

[0020] a third calculation module, configured to calculate the consumed energy of each relay node at a current moment according to a current state of each relay node, and calculate the remaining energy according to the consumed energy and the remaining energy at a previous moment;

[0021] a fourth calculation module, configured to calculate a communication rate of a transmission data packet of each relay node based on transmission consumption energy and interference information of malicious nodes;

[0022] A second determining module is used to determine the instantaneous energy consumption rate distribution function of each relay node according to the residual energy of each relay node at the current moment and the communication rate of the transmitted data packet, and determine the instantaneous energy consumption rate function of each relay node according to the instantaneous energy consumption rate distribution function of each relay node;

[0023] a third determining module, configured to determine a cumulative energy consumption rate trade-off value of each relay node according to the instantaneous energy consumption rate function of each relay node;

[0024] A selection module is used to select the next-hop relay node based on the cumulative energy consumption rate trade-off value of each relay node;

[0025] The loop execution module is used to determine whether the cumulative energy consumption rate trade-off value of each relay node converges. If not, the selected relay node is determined as the new source node, the residual energy of the relay node at the current moment is determined as the residual energy at the previous moment, and jump to S02 for loop execution; if all have converged, the loop execution is stopped and the network routing reconstruction is completed.

[0026] On the other hand, a cognitive network routing reconstruction device with adaptive energy consumption rate weight is provided, and the cognitive network routing reconstruction device with adaptive energy consumption rate weight includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned cognitive network routing reconstruction methods with adaptive energy consumption rate weight is implemented.

[0027] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned energy consumption rate weight adaptive cognitive network routing reconstruction methods.

[0028] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0029] The embodiment of the present invention models the routing problem as a Partially Observable Markov Decision Process (POMDP) ​​and designs an energy consumption rate function based on the node's remaining energy and communication rate. Using energy consumption rate trade-offs, nodes in the network can predict the energy status of neighboring nodes and select the node with the largest cumulative energy consumption rate trade-off value as the next hop to achieve a routing strategy that optimizes energy efficiency. In a decentralized network structure, each node learns and makes decisions independently. Each node makes routing choices based on its own local information and learned strategies, reducing dependence on a single central node and improving the network's resilience and flexibility. Each node utilizes local information and the CERT values ​​of neighboring nodes to make autonomous routing choices, enhancing the network's adaptability. In a centralized network structure, a central node is set up to collect status information from all nodes, maintain and update the CERT values ​​of all nodes, and make routing decisions based on global information, improving network coordination and decision-making efficiency. In a multi-center network structure, multiple regional central nodes are set up. Each central node manages the nodes within its region and exchanges information with other regional central nodes, achieving refined management within the region and effective coordination between regions. This is suitable for collaborative decision-making in cross-domain integrated operations. In addition, communication latency is taken into account to ensure that the service quality requirements of communication rate are met while optimizing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 This is a flow chart of a cognitive network routing reconstruction method with adaptive energy consumption rate weight provided by an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of a cognitive node routing problem scenario provided by an embodiment of the present invention;

[0033] Figure 3 This is a block diagram of a cognitive network routing reconstruction device with adaptive energy consumption rate weight provided by an embodiment of the present invention;

[0034] Figure 4 This is a structural diagram of a cognitive network routing reconstruction device with adaptive energy consumption rate weight provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0036] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0037] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0038] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0039] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0040] Embodiments of the present invention provide a method for cognitive network routing reconfiguration with adaptive energy rate weighting. This method can be implemented by a cognitive network routing reconfiguration device with adaptive energy rate weighting, which can be a terminal or a server. To address the energy consumption issues of cognitive networks in complex environments, a cognitive network energy-optimized routing algorithm based on energy rate trade-offs is proposed. This algorithm utilizes the self-learning mechanism of intelligent agents to optimize the energy consumption of cognitive networks. This algorithm dynamically adjusts routing decisions based on node behavior in the cognitive network, optimizing network energy consumption while maintaining a certain communication rate.

[0041] The routing problem of cognitive network is modeled as POMDP (Partially Observable Markov Decision Process), and the energy consumption rate trade-off cognitive network routing optimization algorithm is used to solve this process. In the embodiment of the present invention, each cognitive network node acts as an agent, continuously updates its cognition of the network environment through the learning process, and makes routing decisions based on this. Figure 1The flowchart of the method for cognitive network routing reconstruction with adaptive energy consumption rate weight is shown. The processing flow of the method may include the following steps:

[0042] S01. Determine a source node, multiple relay nodes, and a target node.

[0043] In a feasible implementation, in the initial stage, all cognitive nodes in the battlefield network interact with the battlefield network environment, learn the current state (including information such as consumed energy, remaining energy, distance to enemy interference nodes, and friendly neighbor nodes), build a routing table, and initialize the CERT value to 0, that is, CERT (s i,0 ,a i,0 )=0, and the node sends status information to neighboring nodes to achieve real-time perception and information sharing of battlefield situation.

[0044] like Figure 2 Figure 1 shows a scenario diagram of a cognitive node routing problem. In this example, there are 6 cognitive nodes and 1 enemy interference node. The source node SN1 wants to establish a communication link with the destination node SN6 to transmit data packets. The path contains one or more relay nodes.

[0045] S02. The source node obtains the status information of all relay nodes and initializes the cumulative energy consumption rate trade-off value of each relay node. The status information includes the energy consumption unit, the remaining energy at the previous moment, the transmission consumption energy, and the interference information of the malicious node.

[0046] In a feasible implementation, for a decentralized network structure, starting from the source node, each relay node learns from the status of its neighboring nodes, including their residual energy, the distance between nodes, and the node transmission data rate; for a centralized and multi-center network structure, the central node collects the status information of all nodes in its jurisdiction.

[0047] S03. Calculate the distance between the source node and each relay node according to the node coordinates of the source node and the node coordinates of each relay node.

[0048] In a feasible implementation, the distance between two communicable SNs is as follows (1):

[0049]

[0050] Where d(i,j)≤D sn , D sn is the communication radiation radius of SN.

[0051] S04. Calculate the current state of each relay node according to the distance between the source node and each relay node and the consumed energy unit.

[0052] The routing decision process is abstracted as a finite-time POMDP. This model models cognitive networks in dynamic environments and transforms the energy balance problem into a POMDP. Data packets forwarded by cognitive nodes are considered agents, and each node is considered a state. In the POMDP-based routing decision process, agents do not directly observe the network environment. Instead, they observe it through interaction with the environment, updating the probability distribution of the current state to update their confidence in the true state, thereby enabling them to make better decisions about future states.

[0053] Use a five-tuple {S,A, γ,ER} describes the problem model:

[0054] S represents the finite set of all possible states that the Agent can take in the network, that is, all cognitive nodes SN in the network. The state of the node is represented by a sub-state set That is, the state of the i-th SN at time t, is the energy consumed by the node at time t, is the remaining energy of the node at time t.

[0055] A is a set of optional actions, that is, the set of all neighbor nodes of SN, which can also be used Represents the set of neighbor nodes of the i-th SN at time t. The agent selects an action a from the action set i,t ∈A means selecting one node from all neighboring nodes as its next hop.

[0056] It represents the transition probability of selecting action a in state s to reach state s'.

[0057] γ∈[0,1] is the discount factor, which indicates the degree of attention paid by the agent to the value of future states during the learning process.

[0058] ER = ER (s, a) represents the expected instantaneous energy consumption rate trade-off when action a is selected in state s. In the model of the present invention, the energy consumption rate trade-off value is related to the remaining energy of the next-hop node and the transmission rate of the node.

[0059] Optionally, S04 calculates the current state of each relay node according to the distance between the source node and each relay node and the consumed energy unit, including:

[0060] According to the distance between the source node and each relay node, the energy consumption unit and the following formula (2), the current state E(d, l) of each relay node is calculated:

[0061] E(d,l)=l·(E T +E R +Eb d δ )(2)

[0062] Where d represents the distance between the source node and the relay node, l represents the bit data, and E T It represents the energy consumed to send one bit of data, E R Indicates the energy consumed in receiving one bit of data, E b represents the energy consumed to transmit one bit of data, and δ represents the path loss exponent.

[0063] In a feasible implementation, in order to minimize the cumulative energy consumption of the network, the energy consumption of the cognitive node at the current moment and its remaining energy, ie, the current state of the node, are calculated based on the cognitive node energy consumption model.

[0064] S05. Calculate the consumed energy of each relay node at the current moment according to the current state of each relay node, and calculate the remaining energy according to the consumed energy and the remaining energy at the previous moment.

[0065] Optionally, S05 calculates the consumed energy of each relay node at the current moment according to the current state of each relay node, and calculates the remaining energy according to the consumed energy and the remaining energy at the previous moment, including:

[0066] According to the current state of the i-th relay node and the following formula (3), the consumed energy of the i-th relay node at the current time t is calculated as

[0067]

[0068] Among them, E e represents the energy cost of sending or receiving, ν s represents the node perception sampling rate, ν t represents the node sending and receiving rate, d(i,j) represents the distance between the i-th relay node and the j-th relay node, t represents the t time, Indicates the number of neighbor nodes;

[0069] According to the energy consumed by the i-th relay node at the current time t Remaining energy at the previous moment And the following formula (4) to calculate the remaining energy

[0070]

[0071] In a feasible implementation, the energy consumption and remaining energy of the node at time t are as shown in the above formula, where the SN's perception time and the communication time with the adjacent nodes are normalized to the integral of t.

[0072] S06. Calculate the communication rate of the transmission data packet of each relay node according to the transmission consumption energy and the interference information of the malicious node.

[0073] Optionally, S06 calculates the communication rate of the transmission data packet of each relay node according to the transmission consumption energy and the interference information of the malicious node, including:

[0074] According to the transmission energy consumption, the interference information of the malicious node and the following formula (5), the communication rate of the transmission data packet between the i-th relay node and the j-th relay node is calculated:

[0075]

[0076] Where d(i,mn) is the distance between the i-th relay node and the interference node, E mn is the electromagnetic interference energy of the malicious node on the i-th relay node.

[0077] In one feasible implementation, the impact of electromagnetic interference from malicious enemy nodes on the node communication rate in a battlefield communication environment needs to be considered when selecting routes. Taking into account the electromagnetic interference generated by malicious enemy nodes, according to the Shannon formula, the communication rate for transmitting data packets between the i-th SN node and the j-th SN node is as shown in equation (5).

[0078] S07. Determine the instantaneous energy consumption rate distribution function of each relay node based on the residual energy of each relay node at the current moment and the communication rate of the transmitted data packet. Determine the instantaneous energy consumption rate function of each relay node based on the instantaneous energy consumption rate distribution function of each relay node.

[0079] Optionally, S07 determines the instantaneous energy consumption rate distribution function of each relay node according to the residual energy of each relay node at the current moment and the communication rate of the transmitted data packet, and determines the instantaneous energy consumption rate function of each relay node according to the instantaneous energy consumption rate distribution function of each relay node, including:

[0080] According to the remaining energy of each relay node at the current moment, the communication rate of the transmitted data packet and the following formula (6), the instantaneous energy consumption rate distribution function of each relay node is determined:

[0081]

[0082] Where β and μ are weighting factors of energy and communication rate, respectively, β + μ = 1;

[0083] According to the instantaneous energy consumption rate distribution function of each relay node and the following formula (7), the instantaneous energy consumption rate function of each relay node is determined:

[0084] ER i,t =E[er i,t+1 ∣s i,t =s,a i,t =a](7)

[0085] Among them, s i,t represents the state of the i-th relay node at time t, a i,t Represents the action of the i-th relay node at time t.

[0086] In a feasible implementation, the instantaneous energy consumption rate distribution function er of the comprehensive energy consumption and communication rate is i,t As shown in the above formula (6). Where β and μ are the weighting factors of energy and communication rate respectively (β + μ = 1), indicating the weight of each corresponding indicator in the energy consumption rate value. The larger the weighting factor, the greater the importance attached to the indicator. It is a given residual energy threshold. When the residual energy of the neighboring node is lower than the given threshold, the energy consumption rate value is 0.

[0087] S08. Determine a cumulative energy consumption rate trade-off value of each relay node according to the instantaneous energy consumption rate function of each relay node.

[0088] Optionally, S08 determines the accumulated energy consumption rate trade-off value of each relay node according to the instantaneous energy consumption rate function of each relay node, including:

[0089] According to the instantaneous energy consumption rate function of each relay node and the following formula (8), the cumulative energy consumption rate trade-off value (CERT) of each relay node is determined:

[0090]

[0091] Where, γ represents the discount factor, T represents a time step, and er i,t+1 represents the instantaneous energy consumption rate distribution function of the i-th relay node at time t+1, s' represents another state, a' represents another action, It represents the transition probability of selecting action a in state s to reach state s'.

[0092] S09. Select the next-hop relay node according to the accumulated energy consumption rate trade-off value of each relay node.

[0093] Optionally, S09 selects a next-hop relay node according to the accumulated energy consumption rate trade-off value of each relay node, including:

[0094] According to the cumulative energy consumption rate trade-off value of each relay node, the optimal cumulative energy consumption rate trade-off value is converted into solving the Bellman equation, as shown in the following formula (9):

[0095]

[0096] in, represents the optimal cumulative energy consumption rate trade-off value, s represents the state, and a represents the action;

[0097] By solving the Bellman equation, the relay node with the largest cumulative energy consumption rate trade-off value is determined as the next-hop relay node.

[0098] In a feasible implementation, in order to obtain an energy-optimized routing path, the key is to solve the optimal CERT value. This routing path optimization problem can be transformed into solving the Bellman equation. From the routing table, the relay node with the largest cumulative energy consumption rate trade-off value is selected according to the ε-greedy strategy. The solution of the Bellman equation corresponds to the path composed of a series of nodes selected when the data packet arrives at the destination node from the source node, that is, the energy-optimized routing strategy π*. Starting from the source node, at each hop node state s i ∈S selects the next hop node with the largest CERT value as action a i ∈A.

[0099] S10. Determine whether the cumulative energy consumption rate trade-off value of each relay node converges. If not, determine the selected relay node as the new source node, determine the residual energy of the relay node at the current moment as the residual energy at the previous moment, and jump to S02 for loop execution; if all have converged, stop the loop execution and complete the network routing reconstruction.

[0100] In one feasible implementation, determining whether the cumulative energy consumption rate trade-off value of each relay node has converged refers to determining whether the CERT value of each relay node has changed significantly. If there is no significant change, convergence is determined. Specifically, the difference between the CERT value of each relay node and the CERT value in the previous cycle is determined to exceed a preset threshold. If not, there is no significant change and the CERT value is determined to have converged. Convergence of the CERT value indicates that the optimal routing path has been found, and the destination node has been reached.

[0101] The embodiments of the present invention can be applied to various network topology scenarios, three of which are described below:

[0102] (1) For a decentralized network structure, each node learns and makes decisions independently, without the need for coordination from a central node. Each node makes routing choices based on its own local information and the learned strategy. After selecting an action, the agent updates the CERT value based on the returned energy consumption rate trade-off value and the prediction of the energy consumption rate trade-off value for the future state, namely:

[0103]

[0104] In the process of updating the CERT value, each cognitive node gradually converges to the optimal CERT value. At this time, the solution of the Bellman equation corresponding to the target of optimization of the proposed routing algorithm is the path composed of a series of nodes selected when the data packet reaches the destination node from the source node, that is, the energy-optimized routing strategy π*.

[0105] (2) For a centralized network structure, the central node is responsible for collecting the status information of all nodes and is responsible for global learning and decision-making. The CERT value can be updated by the central node based on global information:

[0106]

[0107] in, It is the global state maintained by the central node Next select action a i,t CERT value, global status Includes information on the remaining energy and consumed energy of all nodes.

[0108] (3) For a multi-center network structure, there are multiple regional central nodes. Each central node is responsible for managing the nodes in its region and exchanging information with other regional central nodes. To this end, a CERT value update formula can be defined for each central node:

[0109]

[0110] in, It is the global state maintained by the central node Next select action a i,t CERT value;er i,t is the instantaneous energy consumption rate value of the i-th central node at time t; γ i is the discount factor of the i-th central node, weighing the importance of the current energy consumption rate value and the future energy consumption rate value; is the CERT value of the best action that the i-th central node may take at the next time point t+1; N(i) is the set of neighboring central nodes of the i-th central node; β is a weight factor used to adjust the impact of the CERT value of the central node in the adjacent area on the CERT value update of the current node; It is the sum of the CERT values ​​of the adjacent nodes of the i-th central node at the next time point t+1.

[0111] In order to achieve information exchange and coordination decision-making between central nodes, the information exchange formula can be used:

[0112]

[0113] in, is the updated CERT value of the i-th node, is the old CERT value of the i-th central node, N(i) is the set of neighboring central nodes of the i-th central node, and ω is a weight factor that balances the influence of its own CERT value and the CERT value of its neighbors.

[0114] The embodiment of the present invention combines cognitive networks to perform energy consumption rate trade-offs, and realizes optimized routing decisions with energy consumption balance in different network structures such as decentralized, centralized, and multi-centric. The cognitive nodes in the cognitive network can perceive the battlefield situation in real time, including key information such as consumed energy, remaining energy, distance from enemy interference nodes, and neighboring nodes of our army. The routing problem of the cognitive network is modeled as a partially observable Markov decision process, and an energy consumption rate function based on the remaining energy and communication rate of the cognitive nodes is designed to enable nodes in the network to predict the energy status of neighboring nodes. At the same time, considering the information exchange of central nodes in each battlefield area, a routing optimization scheme that supports decentralized, centralized, and multi-centric network topologies is designed to adapt to different combat styles and formation requirements in the battlefield environment.

[0115] This embodiment of the present invention models the routing problem as a POMDP and designs an energy consumption rate function based on node residual energy and communication rate. Using energy consumption rate tradeoffs, network nodes can predict the energy status of neighboring nodes and select the node with the highest cumulative energy consumption rate tradeoff as the next hop, achieving a routing strategy that optimizes energy efficiency. In a decentralized network structure, each node independently learns and makes decisions, selecting routes based on its own local information and learned strategies, reducing reliance on a single central node and improving network resilience and flexibility. Each node utilizes local information and the CERT values ​​of neighboring nodes to autonomously select routes, enhancing the network's adaptability. In a centralized network structure, a central node is deployed to collect status information from all nodes, maintain and update their CERT values, and make routing decisions based on global information, improving network coordination and decision-making efficiency. In a multi-center network structure, multiple regional central nodes are deployed. Each central node manages nodes within its region and exchanges information with other regional central nodes, enabling refined management within a region and effective collaboration between regions. This approach is suitable for collaborative decision-making in cross-domain integrated operations. In addition, communication delay is also taken into account to ensure that the quality of service requirements of the communication rate are met while optimizing energy consumption.

[0116] Figure 3 This is a block diagram of a cognitive network routing reconstruction device with adaptive energy consumption rate weight according to an exemplary embodiment, which is used in a cognitive network routing reconstruction method with adaptive energy consumption rate weight. Figure 3 The device includes a first determination module 3010, an initialization module 3020, a first calculation module 3030, a second calculation module 3040, a third calculation module 3050, a fourth calculation module 3060, a second determination module 3070, a third determination module 3080, a selection module 3090 and a loop execution module 3100.

[0117] in:

[0118] A first determining module 3010 is configured to determine a source node, multiple relay nodes, and a target node;

[0119] Initialization module 3020, configured for the source node to obtain status information of all relay nodes and initialize the cumulative energy consumption rate trade-off value of each relay node. The status information includes energy consumption units, remaining energy at the previous moment, transmission energy consumption, and interference information of malicious nodes;

[0120] A first calculation module 3030 is configured to calculate the distance between the source node and each relay node based on the node coordinates of the source node and the node coordinates of each relay node;

[0121] A second calculation module 3040 is configured to calculate the current state of each relay node based on the distance between the source node and each relay node and the consumed energy unit;

[0122] The third calculation module 3050 is used to calculate the consumed energy of each relay node at the current moment according to the current state of each relay node, and calculate the remaining energy according to the consumed energy and the remaining energy at the previous moment;

[0123] A fourth calculation module 3060 is configured to calculate a communication rate of a transmission data packet of each relay node based on transmission energy consumption and interference information of malicious nodes;

[0124] The second determining module 3070 is configured to determine an instantaneous energy consumption rate distribution function of each relay node based on the residual energy of each relay node at a current moment and the communication rate of the transmitted data packet, and determine an instantaneous energy consumption rate function of each relay node based on the instantaneous energy consumption rate distribution function of each relay node;

[0125] A third determining module 3080 is configured to determine a cumulative energy consumption rate trade-off value of each relay node based on the instantaneous energy consumption rate function of each relay node;

[0126] A selection module 3090 is configured to select a next-hop relay node based on a weighted value of the accumulated energy consumption rate of each relay node;

[0127] The loop execution module 3100 is used to determine whether the cumulative energy consumption rate trade-off value of each relay node converges. If not, the selected relay node is determined as the new source node, the remaining energy of the relay node at the current moment is determined as the remaining energy at the previous moment, and jump to S02 for loop execution; if all have converged, the loop execution is stopped and the network routing reconstruction is completed.

[0128] Optionally, the second calculation module 3040 is configured to:

[0129] According to the distance between the source node and each relay node, the energy consumption unit and the following formula (1), the current state E(d, l) of each relay node is calculated:

[0130] E(d,l)=l·(E T +E R +E b d δ )(1)

[0131] Where d represents the distance between the source node and the relay node, l represents the bit data, and E T It represents the energy consumed to send one bit of data, E R Indicates the energy consumed in receiving one bit of data, E brepresents the energy consumed to transmit one bit of data, and δ represents the path loss exponent.

[0132] Optionally, the third calculation module 3050 is configured to:

[0133] According to the current state of the i-th relay node and the following formula (2), the consumed energy of the i-th relay node at the current time t is calculated as

[0134]

[0135] Among them, E e represents the energy cost of sending or receiving, ν s represents the node perception sampling rate, ν t represents the node sending and receiving rate, d(i,j) represents the distance between the i-th relay node and the j-th relay node, t represents the t time, Indicates the number of neighbor nodes;

[0136] According to the energy consumed by the i-th relay node at the current time t Remaining energy at the previous moment And the following formula (3) to calculate the remaining energy

[0137]

[0138] Optionally, the fourth calculation module 3060 is configured to:

[0139] According to the transmission energy consumption, the interference information of the malicious node and the following formula (4), the communication rate of the transmission data packet between the i-th relay node and the j-th relay node is calculated:

[0140]

[0141] Where d(i,mn) is the distance between the i-th relay node and the interference node, E mn is the electromagnetic interference energy of the malicious node on the i-th relay node.

[0142] Optionally, the second determining module 3070 is configured to:

[0143] According to the remaining energy of each relay node at the current moment, the communication rate of the transmitted data packet and the following formula (5), the instantaneous energy consumption rate distribution function of the i-th relay node at the t-th moment is determined:

[0144]

[0145] Where β and μ are weighting factors of energy and communication rate respectively, β+μ=1, Indicates the remaining energy threshold; D sn Indicates the communication distance of nodes;

[0146] According to the instantaneous energy consumption rate distribution function of each relay node and the following formula (6), the instantaneous energy consumption rate function of each relay node is determined:

[0147] ER i,t =E[er i,t+1 ∣s i,t =s,a i,t =a](6)

[0148] Among them, s i,t represents the state of the i-th relay node at time t, a i,t Represents the action of the i-th relay node at time t.

[0149] Optionally, the third determining module 3080 is configured to:

[0150] According to the instantaneous energy consumption rate function of each relay node and the following formula (7), the cumulative energy consumption rate trade-off value CERT (s i,t ,a i,t ):

[0151]

[0152] Where γ represents the discount factor, T represents a time step, and er i,t+1 represents the instantaneous energy consumption rate distribution function of the i-th relay node at time t+1, s' represents another state, a' represents another action, It represents the transition probability of selecting action a in state s to reach state s'.

[0153] Optionally, the selection module 3090 is configured to:

[0154] According to the cumulative energy consumption rate trade-off value of each relay node, the optimal cumulative energy consumption rate trade-off value is converted into solving the Bellman equation, as shown in the following formula (8):

[0155]

[0156] in, represents the optimal cumulative energy consumption rate trade-off value, s represents the state, and a represents the action;

[0157] By solving the Bellman equation, the relay node with the largest cumulative energy consumption rate trade-off value is determined as the next-hop relay node.

[0158] This embodiment of the present invention models the routing problem as a POMDP and designs an energy consumption rate function based on node residual energy and communication rate. Using energy consumption rate tradeoffs, network nodes can predict the energy status of neighboring nodes and select the node with the highest cumulative energy consumption rate tradeoff as the next hop, achieving a routing strategy that optimizes energy efficiency. In a decentralized network structure, each node independently learns and makes decisions, selecting routes based on its own local information and learned strategies, reducing reliance on a single central node and improving network resilience and flexibility. Each node utilizes local information and the CERT values ​​of neighboring nodes to autonomously select routes, enhancing the network's adaptability. In a centralized network structure, a central node is deployed to collect status information from all nodes, maintain and update their CERT values, and make routing decisions based on global information, improving network coordination and decision-making efficiency. In a multi-center network structure, multiple regional central nodes are deployed. Each central node manages nodes within its region and exchanges information with other regional central nodes, enabling refined management within a region and effective collaboration between regions. This approach is suitable for collaborative decision-making in cross-domain integrated operations. In addition, communication delay is also taken into account to ensure that the quality of service requirements of the communication rate are met while optimizing energy consumption.

[0159] Figure 4 Schematic diagram of a cognitive network routing reconstruction device with adaptive energy consumption rate weight provided by an embodiment of the present invention. Figure 4 As shown, the energy consumption rate weight adaptive cognitive network routing reconstruction device may include the above Figure 3 Optionally, the cognitive network routing reconstruction device 410 with adaptive energy consumption rate weight may include a first processor 2001 .

[0160] Optionally, the energy consumption rate weight adaptive cognitive network routing reconstruction device 410 may further include a memory 2002 and a transceiver 2003 .

[0161] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0162] The following combination Figure 4 The components of the energy consumption rate weight adaptive cognitive network routing reconstruction device 410 are described in detail:

[0163] The first processor 2001 is the control center of the energy consumption rate weight adaptive cognitive network routing reconstruction device 410, and can be a processor or a collective term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs) or one or more field programmable gate arrays (FPGAs).

[0164] Optionally, the first processor 2001 may execute various functions of the energy consumption rate weight adaptive cognitive network routing reconstruction device 410 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0165] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0166] In a specific implementation, as an embodiment, the energy consumption rate weight adaptive cognitive network routing reconstruction device 410 may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0167] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0168] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and be connected to the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0169] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0170] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 (not shown separately in the figure). The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0171] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and be configured to reconfigure the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0172] It should be noted that Figure 4 The structure of the energy consumption rate weight adaptive cognitive network routing reconstruction device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0173] In addition, the technical effects of the energy consumption rate weight adaptive cognitive network routing reconstruction device 410 can refer to the technical effects of the energy consumption rate weight adaptive cognitive network routing reconstruction method described in the above method embodiment, and will not be repeated here.

[0174] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0175] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0176] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0177] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0178] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0179] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0180] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0181] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0182] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0183] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0185] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0186] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A cognitive network routing reconstruction method with adaptive energy consumption rate weight, characterized in that: The method comprises: S01, determining a source node, multiple relay nodes, and a target node; S02. The source node obtains the status information of all relay nodes and initializes the cumulative energy consumption rate trade-off value of each relay node. The status information includes the energy consumption unit, the remaining energy at the previous moment, the transmission consumption energy, and the interference information of the malicious node; S03. Calculate the distance between the source node and each relay node based on the node coordinates of the source node and the node coordinates of each relay node; S04. Calculate the current state of each relay node based on the distance between the source node and each relay node and the consumed energy unit; S05. Calculate the consumed energy of each relay node at the current moment according to the current state of each relay node, and calculate the remaining energy according to the consumed energy and the remaining energy at the previous moment; S06. Calculate the communication rate of the transmission data packet of each relay node based on the transmission energy consumption and the interference information of the malicious node; S07. Determine an instantaneous energy consumption rate distribution function of each relay node based on the residual energy of each relay node at the current moment and the communication rate of the transmitted data packet, and determine an instantaneous energy consumption rate function of each relay node based on the instantaneous energy consumption rate distribution function of each relay node; S08. Determine a cumulative energy consumption rate trade-off value of each relay node according to the instantaneous energy consumption rate function of each relay node; S09. Select the next-hop relay node based on the cumulative energy consumption rate trade-off value of each relay node; S10. Determine whether the cumulative energy consumption rate trade-off value of each relay node converges. If not, determine the selected relay node as the new source node, determine the residual energy of the relay node at the current moment as the residual energy at the previous moment, and jump to S02 for loop execution; if all have converged, stop the loop execution and complete the network routing reconstruction.

2. The energy consumption rate weight adaptive cognitive network routing reconstruction method according to claim 1 is characterized in that: The step S04 calculates the current state of each relay node according to the distance between the source node and each relay node and the energy consumption unit, including: According to the distance between the source node and each relay node, the energy consumption unit and the following formula (1), the current state E(d, l) of each relay node is calculated: E(d,l)=l·(E T +E R +E b d δ )(1) Where d represents the distance between the source node and the relay node, l represents the bit data, and E T It represents the energy consumed to send one bit of data, E R Indicates the energy consumed in receiving one bit of data, E b represents the energy consumed to transmit one bit of data, and δ represents the path loss exponent.

3. The energy consumption rate weight adaptive cognitive network routing reconstruction method according to claim 1 is characterized in that: The step S05 of calculating the consumed energy of each relay node at the current moment according to the current state of each relay node, and calculating the remaining energy according to the consumed energy and the remaining energy at the previous moment, includes: According to the current state of the i-th relay node and the following formula (2), the consumed energy of the i-th relay node at the current time t is calculated as Among them, E e represents the energy cost of sending or receiving, ν s represents the node perception sampling rate, ν t represents the node sending and receiving rate, d(i,j) represents the distance between the i-th relay node and the j-th relay node, t represents the t time, Indicates the number of neighbor nodes; According to the energy consumed by the i-th relay node at the current time t Remaining energy at the previous moment And the following formula (3) to calculate the remaining energy 4. The energy consumption rate weight adaptive cognitive network routing reconstruction method according to claim 1 is characterized in that: The step S06 calculates the communication rate of the transmission data packet of each relay node according to the transmission consumption energy and the interference information of the malicious node, including: According to the transmission energy consumption, the interference information of the malicious node and the following formula (4), the communication rate of the transmission data packet between the i-th relay node and the j-th relay node is calculated: Where d(i,mn) is the distance between the i-th relay node and the interference node, E mn is the electromagnetic interference energy of the malicious node on the i-th relay node.

5. The energy consumption rate weight adaptive cognitive network routing reconstruction method according to claim 1 is characterized in that: The step S07 of determining the instantaneous energy consumption rate distribution function of each relay node according to the residual energy of each relay node at the current moment and the communication rate of the transmitted data packet, and determining the instantaneous energy consumption rate function of each relay node according to the instantaneous energy consumption rate distribution function of each relay node, includes: According to the remaining energy of each relay node at the current moment, the communication rate of the transmitted data packet and the following formula (5), the instantaneous energy consumption rate distribution function of the i-th relay node at the t-th moment is determined: Where β and μ are weighting factors of energy and communication rate respectively, β+μ=1, Indicates the remaining energy threshold; D sn Indicates the communication distance of nodes; According to the instantaneous energy consumption rate distribution function of each relay node and the following formula (6), the instantaneous energy consumption rate function of each relay node is determined: IS i,t =E[er i,t+1 ∣s i,t =s,a i,t =a](6) Among them, s i,t represents the state of the i-th relay node at time t, a i,t Represents the action of the i-th relay node at time t.

6. The energy consumption rate weight adaptive cognitive network routing reconstruction method according to claim 1 is characterized in that: The step S08 of determining the accumulated energy consumption rate trade-off value of each relay node according to the instantaneous energy consumption rate function of each relay node includes: According to the instantaneous energy consumption rate function of each relay node and the following formula (7), the cumulative energy consumption rate trade-off value CERT (s i,t ,a i,t ): Where γ represents the discount factor, T represents a time step, and er i,t+1 represents the instantaneous energy consumption rate distribution function of the i-th relay node at time t+1, s' represents another state, a' represents another action, It represents the transition probability of selecting action a in state s to reach state s'.

7. The energy consumption rate weight adaptive cognitive network routing reconstruction method according to claim 1 is characterized in that: The step S09 of selecting a next-hop relay node according to the accumulated energy consumption rate trade-off value of each relay node includes: According to the cumulative energy consumption rate trade-off value of each relay node, the optimal cumulative energy consumption rate trade-off value is converted into solving the Bellman equation, as shown in the following formula (8): in, represents the optimal cumulative energy consumption rate trade-off value, s represents the state, and a represents the action; By solving the Bellman equation, the relay node with the largest cumulative energy consumption rate trade-off value is determined as the next-hop relay node.

8. A cognitive network routing reconstruction device with adaptive energy consumption rate weight, wherein the cognitive network routing reconstruction device with adaptive energy consumption rate weight is used to implement the cognitive network routing reconstruction method with adaptive energy consumption rate weight as claimed in any one of claims 1 to 7, characterized in that: The device comprises: A first determination module, configured to determine a source node, a plurality of relay nodes, and a target node; An initialization module is used for the source node to obtain the status information of all relay nodes and initialize the cumulative energy consumption rate trade-off value of each relay node. The status information includes the energy consumption unit, the remaining energy at the previous moment, the transmission consumption energy, and the interference information of the malicious node; A first calculation module is used to calculate the distance between the source node and each relay node according to the node coordinates of the source node and the node coordinates of each relay node; A second calculation module is used to calculate the current state of each relay node according to the distance between the source node and each relay node and the consumed energy unit; a third calculation module, configured to calculate the consumed energy of each relay node at a current moment according to a current state of each relay node, and calculate the remaining energy according to the consumed energy and the remaining energy at a previous moment; a fourth calculation module, configured to calculate a communication rate of a transmission data packet of each relay node based on transmission consumption energy and interference information of malicious nodes; A second determining module is used to determine the instantaneous energy consumption rate distribution function of each relay node according to the residual energy of each relay node at the current moment and the communication rate of the transmitted data packet, and determine the instantaneous energy consumption rate function of each relay node according to the instantaneous energy consumption rate distribution function of each relay node; a third determining module, configured to determine a cumulative energy consumption rate trade-off value of each relay node according to the instantaneous energy consumption rate function of each relay node; A selection module is used to select the next-hop relay node based on the cumulative energy consumption rate trade-off value of each relay node; The loop execution module is used to determine whether the cumulative energy consumption rate trade-off value of each relay node converges. If not, the selected relay node is determined as the new source node, the residual energy of the relay node at the current moment is determined as the residual energy at the previous moment, and jump to S02 for loop execution; if all have converged, the loop execution is stopped and the network routing reconstruction is completed.

9. A cognitive network routing reconstruction device with adaptive energy consumption rate weight, characterized in that: The energy consumption rate weight adaptive cognitive network routing reconstruction device includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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