Network link dynamic construction method, device and equipment for charging link network

Abstract

Embodiments of the present disclosure disclose a network link dynamic construction method, device and equipment for a charging link network. A specific implementation of the method comprises: in response to receiving a charging service request, constructing a target agent link according to the charging service request and each agent; performing link verification processing on the charging service request according to the target agent link; in response to determining that a first link verification result indicates that the link verification is passed, sending a charging instruction to a target charging device through the target agent link; monitoring the state of the target agent link to update the target agent link in real time; in response to receiving a power-off service request issued for the target charging device, sending a power-off instruction to the target charging device according to the target agent link, and updating each agent. The implementation can reduce network delay through dynamic link optimization in the scenario of a charging link network, thereby improving network service stability.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to a method, apparatus, and device for dynamically constructing network links for charging link networks. Background Technology

[0002] A charging link network is a communication network composed of charging devices, user terminals, service platforms, and communication relay nodes. It is used to promptly respond to and handle network services such as charging / power-off requests from users. Currently, when constructing network links in a charging link network scenario, the common approach is to provide network services to users using a centralized architecture and static network paths.

[0003] However, when constructing network links in charging link network scenarios using the above method, the following technical problems often arise:

[0004] Static network paths are often difficult to adapt to real-time dynamic changes in node load and network status. Furthermore, when a node in the network path fails, it can directly lead to frequent occurrences of increased network service latency, abnormal disconnections, or network service interruptions.

[0005] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not form prior art known to those skilled in the art. Summary of the Invention

[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of this disclosure propose a method, apparatus, and device for dynamically constructing network links for charging link networks to address the technical problems mentioned in the background section above.

[0008] In a first aspect, some embodiments of this disclosure provide a method for dynamically constructing network links for a charging link network. The method includes: in response to receiving a charging service request for a target charging device, constructing a target intelligent agent link based on the charging service request and pre-built intelligent agents, wherein the intelligent agents are deployed at different network physical nodes in the charging link network; performing link verification processing on the charging service request based on the target intelligent agent link to generate a first link verification result; in response to determining that the first link verification result indicates successful link verification, sending a charging command to the target charging device through the target intelligent agent link; in response to determining that the target intelligent agent link is not interrupted, performing status monitoring on the target intelligent agent link to update the target intelligent agent link in real time; and in response to receiving a power outage service request for the target charging device, sending a power outage command to the target charging device based on the target intelligent agent link, and updating the intelligent agents.

[0009] Secondly, some embodiments of this disclosure provide a network link dynamic construction apparatus for a charging link network. The apparatus includes: a construction unit configured to, in response to receiving a charging service request for a target charging device, construct a target intelligent agent link based on the charging service request and pre-constructed intelligent agents, wherein the intelligent agents are deployed at different network physical nodes in the charging link network; a link verification unit configured to perform link verification processing on the charging service request based on the target intelligent agent link to generate a first link verification result; a first sending unit configured to, in response to determining that the first link verification result indicates that the link verification is successful, send a charging command to the target charging device through the target intelligent agent link; a status monitoring unit configured to, in response to determining that the target intelligent agent link is not interrupted, perform status monitoring on the target intelligent agent link to update the target intelligent agent link in real time; and a second sending unit configured to, in response to receiving a power-off service request for the target charging device, send a power-off command to the target charging device based on the target intelligent agent link and update the intelligent agents.

[0010] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0012] The above embodiments of this disclosure have the following beneficial effects: the dynamic network link construction method for charging link networks according to some embodiments of this disclosure can reduce network latency and improve network link stability in charging link network scenarios through dynamic link optimization. Specifically, the reasons for high network latency and unstable network links are that static network paths are usually difficult to adapt to real-time dynamic changes in node load and network status, and when a node in the network path fails, it will directly lead to increased network service latency, abnormal disconnection, or frequent network service interruptions. Based on this, the dynamic network link construction method for charging link networks according to some embodiments of this disclosure firstly, in response to receiving a charging service request for a target charging device, constructs a target intelligent agent link based on the charging service request and pre-built intelligent agents. The intelligent agents are deployed on different network physical nodes in the charging link network. Thus, by selecting network physical nodes where intelligent agents with low response time and high stability reside during the initial construction of the network link, the average link latency and failure rate can be reduced. Then, based on the target intelligent agent link, the charging service request is processed for link verification to generate a first link verification result. Subsequently, in response to the determination that the first link verification result indicates that the link verification is successful, a charging command is sent to the target charging device through the target agent link. This allows link verification to be performed between multiple nodes, reducing the risk of path errors or hijacking caused by tampering with a single node in the link. Secondly, in response to the determination that the target agent link is uninterrupted, the status of the target agent link is monitored to update it in real time. This allows for the determination of agent status based on real-time link data (bandwidth, latency, packet loss rate) and the dynamic replacement of abnormal agent nodes, thereby reducing network latency and improving link continuity and network communication stability. Finally, in response to receiving a power outage service request for the target charging device, a power outage command is sent to the target charging device according to the target agent link, and the various agents are updated. Therefore, after each network service ends, it is possible to promptly identify agents with high response latency and low health within the charging link network, and update or remove such agents, thereby maintaining the overall response speed of the charging link network. Furthermore, in scenarios oriented towards the charging link network, the network link construction method through dynamic link optimization can reduce network latency and improve network service stability. Attached Figure Description

[0013] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0014] Figure 1 This is a schematic diagram illustrating the construction of an intelligent agent in a method for dynamically constructing network links for charging link networks according to some embodiments of this disclosure;

[0015] Figure 2 This is a schematic diagram illustrating an application scenario of the dynamic network link construction method for charging link networks according to some embodiments of this disclosure;

[0016] Figure 3 This is a flowchart of some embodiments of the method for dynamically constructing network links for charging link networks according to this disclosure;

[0017] Figure 4 This is a schematic diagram of the structure of some embodiments of the network link dynamic construction device for charging link networks according to the present disclosure;

[0018] Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0019] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0020] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0024] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario of a dynamic network link construction method for charging link networks, which is one of the embodiments of this disclosure.

[0026] Figure 2 This is a schematic diagram illustrating an application scenario of a dynamic network link construction method for charging link networks, which is one of the embodiments of this disclosure.

[0027] exist Figure 1 and Figure 2 In the application scenario shown, firstly, for each physical node device (e.g., router 204, router 205, etc.) included in the charging link network 203, the computing device 201 can build and deploy differentiated intelligent agents in each physical node device according to the different operating systems and operating environments of each physical node device. Then, when the computing device 201 receives a charging service request from a user terminal for a target charging device 202 through the charging link network 203, it can construct a target intelligent agent link based on the charging service request and the pre-built intelligent agents. This target intelligent agent link, as shown by the red line in the figure, is formed by linking the intelligent agents deployed in network devices 204, 205, 206, and 207. Subsequently, the computing device 201 can provide network services such as charging and power-off to the user terminal through the target intelligent agent link. Finally, in response to determining that the target intelligent agent link is not interrupted, the computing device 201 can perform status monitoring on the target intelligent agent link to update it in real time.

[0028] It should be noted that the aforementioned computing device 201 can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed in the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here. It should be understood that... Figure 2 The number of computing devices in the system can be arbitrary, depending on the implementation requirements.

[0029] Continue to refer to Figure 3The diagram illustrates flow 300 of some embodiments of a method for dynamically constructing network links for charging link networks according to the present disclosure. This method for dynamically constructing network links for charging link networks includes the following steps:

[0030] Step 301: In response to receiving a charging service request for the target charging device, construct the target intelligent agent link based on the charging service request and the pre-built intelligent agents.

[0031] In some embodiments, the execution entity of the network link dynamic construction method for charging link networks (e.g., Figure 2 The computing device 201 shown can respond to receiving a charging service request for a target charging device via a wired or wireless connection. Based on the charging service request and pre-built intelligent agents, it constructs a target intelligent agent link, wherein the intelligent agents are deployed at different network physical nodes in the charging link network. The target charging device can be a charging device scanned by a user via a mobile terminal. The charging device can be a charging pile. The charging service request can be a data request message sent by a mobile terminal (e.g., a payment QR code) to initiate the charging process after the user scans the identification information (e.g., a payment QR code) on the target charging device. The charging service request can include, but is not limited to, charging device identification, charging device location information, and payment information. The charging device identification can be the device serial number of the target charging device. The charging device location information can be the MAC address of the target charging device. The payment information can be intermediate information generated by the user during the payment process (e.g., payment voucher, order number, etc.). The charging link network can be a network system for providing charging / disconnection services to electric vehicles, consisting of multiple network physical nodes. The aforementioned network physical nodes may include charging devices with power output capabilities and network devices for control, scheduling, and communication. These network physical nodes can be network devices and charging devices distributed throughout the aforementioned charging link network. These network physical nodes may be, but are not limited to, billing gateways, routers, repeaters, control cabinets, servers, and charging piles. The aforementioned Distributed Agents (DAs) can be small program entities deployed in different physical nodes of the charging link network. The aforementioned target agent link can be a logical link consisting of multiple network physical nodes with deployed agents, connecting the user's mobile terminal to the target charging device.

[0032] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0033] Optionally, before receiving the charging service request for the charging device, the executing entity may also perform the following steps:

[0034] The first step is to perform the following node preprocessing steps for the network physical nodes in the above charging link network:

[0035] The first sub-step involves collecting the node parameter information and node identifier of the aforementioned network physical nodes. The node identifier can be a unique string representing the network physical node (e.g., a device serial number). The node parameter information can include device type, CPU architecture, memory size, operating system type, available bandwidth, network latency, network packet loss rate, and MAC address. The network latency can be the average round-trip time per second for one ping session within the most recent sliding window (e.g., a 30-second sliding window). The network packet loss rate can be the ratio of the number of failed ping sessions to the total number of ping sessions within the most recent sliding window. In practice, the node parameter information and node identifier of the aforementioned network physical nodes can be queried and collected through local queries or device management protocols (e.g., MQTT or Modbus protocols).

[0036] It should be noted that, in practice, the aforementioned execution entity may randomly select network physical nodes in the aforementioned charging link network to perform the aforementioned node preprocessing steps. The number of network physical nodes selected may be a preset number or a random number based on a number greater than or equal to half the total number of network physical nodes in the charging link network.

[0037] The second sub-step involves constructing node profile labels based on the collected node parameter information. These node profile labels can be structured features describing the resource capabilities and status of the network physical nodes, constructed by vectorizing and extracting labels from the node parameter information. In practice, the node parameters included in the node parameter information can be categorized. Numerical parameters can be mapped to standard labels using quantitative indicators (e.g., memory greater than or equal to 16GB is mapped to the "high memory" label). Non-numerical parameters can be categorized using preset classification labels (e.g., determining the location of the network physical node in the charging link network based on the MAC address, dividing it into "edge node" and "non-edge node" labels) and mapped to corresponding standard labels to construct node profile labels. For example, the node profile labels could be "["router", "ARM", "high memory", "low latency", "high availability", "edge node"]".

[0038] The third sub-step involves encrypting and signing the aforementioned node profile label and node identifier to generate node signature information. In practice, the node profile label can be concatenated with the aforementioned node identifier, and then a digest can be generated using a hash algorithm (e.g., SHA-256 algorithm). Finally, a digital signature (e.g., ECDSA algorithm or RSA algorithm) can be performed using the preset private key of the aforementioned network physical node to generate node signature information.

[0039] It should be noted that the aforementioned executing entity has a pre-set public key, and each network physical node stores a private key corresponding to the public key.

[0040] The fourth sub-step involves deploying intelligent agents in the aforementioned network physical nodes based on the node parameter information.

[0041] In some optional implementations of certain embodiments, the aforementioned executing entity may deploy an intelligent agent in the aforementioned network physical node based on the aforementioned node parameter information through the following steps:

[0042] The first step is to obtain the agent template code corresponding to the node parameter information mentioned above. This agent template code can be a general source code framework for building distributed agent programs deployed on various network physical nodes in the charging link network. In practice, the executing entity can extract agent template code adapted to the aforementioned network physical nodes from a pre-set code template library according to the CPU architecture, memory size, operating system type, and bandwidth capabilities included in the node parameter information.

[0043] The second step involves using code obfuscation tools to perturb and replace the aforementioned agent template code, resulting in obfuscated agent template code. In practice, the aforementioned executor can use code obfuscation tools (e.g., Allatori, ProGuard) to perturb function names, variable names, comments, class structures, etc., in the template code to obtain obfuscated agent template code.

[0044] The third step involves logically refactoring the obfuscated agent template code to obtain the refactored agent template code. In practice, the executing entity can rewrite the logical structure of the obfuscated agent template code to obtain the refactored agent template code. This logical structure rewriting can include control flow flattening, function inlining / splitting, or using the CFG Rewriter tool to shuffle the order of code blocks.

[0045] The fourth step involves injecting policy plugins into the reconstructed agent template code based on the node profile tags mentioned above, thereby generating differentiated agent template code. In practice, the executing entity can use a policy plugin template library (e.g., RuleSet-A or ML-Decision-B) to selectively combine policy plugins according to the node profile tags of the network physical nodes, and embed the policy plugins into the specified interface positions of the reconstructed agent template code to generate differentiated agent template code. For example, if the node profile tag in the physical network node includes the "edge node" tag, then an AuthPolicy plugin (authentication plugin, used to determine whether a user request is legitimate) and a RoutePolicy plugin (routing plugin, controlling whether to allow data packets) can be injected.

[0046] The fifth step involves compiling the differentiated agent template code to construct an agent. The constructed agent includes agent parameter information, which includes agent health, agent confidence, and an online flag. Agent health can be a numerical value representing the agent's operational status. Agent confidence can be a numerical value calculated based on the agent's performance in historical tasks, used to characterize the reliability of the agent's behavior. The online flag can be an identifier indicating whether the corresponding agent is running (online). In practice, the executing entity can use a compiler to compile the differentiated agent template code to obtain an executable binary program for the network physical node, serving as the agent.

[0047] It should be noted that after constructing the agent and deploying it to the network physical node, the aforementioned executor can set the corresponding agent parameter information, including agent health and agent confidence, to the default initial value (e.g., the default initial value is 0).

[0048] The sixth step is to deploy the constructed intelligent agent to the aforementioned network physical nodes. In practice, the aforementioned executing entity can deploy and run the aforementioned intelligent agent (i.e., the executable binary program) on the aforementioned network physical nodes.

[0049] The content in steps one through six above constitutes an inventive point of this disclosure, solving the technical problem that "the construction method of intelligent agents in network link nodes is singular and the structure is homogeneous, making them easy for attackers to predict and reuse, thus leading to poor stability of the charging link network and reduced communication security." Factors leading to poor network stability and low communication security often include: the construction method of intelligent agents in network link nodes is singular and the structure is homogeneous, making them easy for attackers to predict and reuse. To achieve this effect, differentiated processing methods such as code perturbation (obfuscation), logic refactoring, and policy plugin injection are introduced during the intelligent agent construction stage to generate heterogeneous intelligent agents with different code structures, control logic paths, and policy behaviors. Therefore, differentiated intelligent agents can be generated based on the same intelligent agent template, effectively increasing the complexity of the intelligent agent execution paths and the diversity of each network physical node in the entire charging link network, thereby improving the stability and communication security of the charging link network.

[0050] In some optional implementations of certain embodiments, the aforementioned executing entity can construct the target intelligent agent link based on the aforementioned charging service request and the pre-built intelligent agents through the following steps:

[0051] The first step is to parse the charging service request to obtain the charging device identifier and location information. In practice, the executing entity can extract JSON attributes to parse the charging service request and obtain the charging device identifier and location information.

[0052] The second step involves performing node filtering on each agent based on their respective parameter information to determine candidate agents. In practice, the executing entity can identify candidate agents as those whose agent health and confidence scores are greater than or equal to the health and confidence thresholds, respectively, and whose online status indicates they are online (running).

[0053] The third step involves performing link simulations on each candidate agent based on the aforementioned charging device location information to generate target agent link information. This target agent link information characterizes the connection order of the selected agents. For example, the target agent link information could be ["DA_12", "DA_45", "DA_88"]. In practice, the executing agents can use node bandwidth and packet loss rate as path weights to construct adjacency lists or adjacency matrices to characterize the connection status of each network physical node. Finally, the optimal path is selected using a shortest path algorithm (e.g., Dijkstra's algorithm or A* algorithm), and the target agent link information is generated.

[0054] The fourth step is to construct the target intelligent agent link corresponding to the charging device identifier based on the aforementioned target intelligent agent link information. In practice, the aforementioned execution entity can assemble the various intelligent agents into logical links according to the link order represented by the aforementioned target intelligent agent link information, forming a target intelligent agent link from the user to the target charging device.

[0055] Step 302: Based on the target intelligent agent link, perform link verification processing on the charging service request to generate the first link verification result.

[0056] In some embodiments, the aforementioned executing entity may perform link verification processing on the aforementioned charging service request based on the aforementioned target intelligent agent link, so as to generate a first link verification result. The aforementioned first link verification result can characterize whether the link communication has passed link verification.

[0057] In some optional implementations of certain embodiments, the aforementioned execution entity may perform link verification processing on the aforementioned charging service request based on the aforementioned target intelligent agent link through the following steps to generate a first link verification result:

[0058] The first step is to encapsulate the charging service request to generate a charging service request message. In practice, the executing entity can package the charging service request into a standard protocol format, encrypt it using a pre-agreed public key, and finally append the signature information corresponding to the executing entity to obtain the charging service request message.

[0059] It should be noted that the method for generating the signature information corresponding to the above-mentioned execution entity can refer to the steps for generating the node signature information corresponding to the above-mentioned network physical nodes, and will not be repeated here.

[0060] The second step is to perform the following node verification steps for each target agent in the above target agent link:

[0061] The first sub-step involves sending the charging service request message to the target agent to receive agent verification information returned by the target agent. This agent verification information includes the corresponding node signature information, agent parameter information, and agent verification result. The agent verification result can be a Boolean variable, representing whether the corresponding agent has successfully verified the charging service request message.

[0062] It should be noted that after receiving the charging service request message, the target intelligent agent can verify the authenticity of the signature information of the execution entity and whether it has been tampered with through the pre-stored public key and signature algorithm, and verify whether the charging service request message has been tampered with.

[0063] The second sub-step involves determining the agent verification result included in the received agent verification information as the target agent verification result in response to the determination that the agent parameter information meets a preset confidence condition. The confidence condition can be that the agent confidence level included in the corresponding agent parameter information is greater than or equal to the aforementioned confidence threshold.

[0064] The third step is to generate the first link verification result based on the verification results of each agent. In practice, the aforementioned executing entity can perform an AND operation on the verification results of each agent to generate the first link verification result.

[0065] Step 303: In response to the determination that the first link verification result indicates that the link verification is successful, a charging command is sent to the target charging device through the target intelligent agent link.

[0066] In some embodiments, the aforementioned execution entity may, in response to determining that the first link verification result indicates that the link verification has passed, send a charging command to the target charging device through the target intelligent agent link. The charging command may be an instruction acting on the target charging device to initiate device discharge behavior.

[0067] Step 304: In response to determining that the target agent link is not interrupted, the status of the target agent link is monitored to update the target agent link in real time.

[0068] In some embodiments, the execution entity may, in response to determining that the target agent link is not interrupted, perform status monitoring on the target agent link to update the target agent link in real time.

[0069] In some optional implementations of certain embodiments, the aforementioned executing entity may perform state monitoring on the aforementioned target agent link through the following steps to update the aforementioned target agent link in real time:

[0070] The first step, for each agent in the target agent chain mentioned above, is to perform the following steps:

[0071] The first sub-step involves collecting agent operation information. This information includes: agent CPU utilization, agent memory utilization, number of abnormal connection requests per unit time, number of signature failures, and vote rejection rate. The agent CPU utilization can be the percentage of CPU resources used by the agent within the charging link network node. The agent memory utilization can be the proportion of memory used by the agent process relative to the total memory of the corresponding network physical node. The number of abnormal connection requests can be the number of abnormal connection attempts received by the agent per unit time (e.g., per minute). These abnormal connection attempts may include, but are not limited to, illegal source addresses, incorrect request formats, and repeated connection attempts exceeding frequency thresholds. The number of signature failures can be the number of times the agent failed to verify signatures per unit time or in a single task. These failures include receiving invalid request message signatures and having one's own signature rejected by other agents. The vote rejection rate can be the percentage of votes not ultimately adopted by the agent during consensus processes such as policy adjudication and path selection.

[0072] It should be noted that, in the charging link network, to improve the consistency and fault tolerance of each agent in key decision-making processes (e.g., path selection decisions), the aforementioned executing entities can also conduct consensus analysis on agent behavior through policy adjudication. Specifically, the aforementioned executing entities can collect consensus-related information such as node signature information, node profile tags, network bandwidth, and egress policy suggestions from each currently online agent through a distributed parallel mechanism, and perform consensus judgment (i.e., majority voting mechanism). The aforementioned key decision-making process can refer to path selection decisions (choosing which of multiple reachable network link paths to use as the transmission path for network communication links) or agent online / offline voting (whether to remove an agent if its operating state is abnormal, i.e., its health or confidence is less than the corresponding threshold). The aforementioned egress policy suggestions can refer to policy recommendations given by an agent for its own node's data egress behavior (packet forwarding, dropping, rate limiting, etc.) in the link, used to assist the aforementioned executing entities in determining whether the node is suitable as a hop point on the target agent's link path. The above formula judgment refers to statistically analyzing the results submitted by multiple agents. If a certain decision item (such as a link path) receives support from more than half of the agents, it is considered "consensus passed." If a candidate path or strategy (e.g., a traffic scheduling scheme, i.e., deciding whether to enable rate limiting, discard abnormal requests, or a security policy scheme, i.e., deciding which agent plugins to enable, whether to use multi-factor authentication, etc.) receives support from more than half of the heterogeneous agents, it can be considered a consensus result, and this result is synchronized to the relevant agents for subsequent network link path construction and behavior adjustment. The aforementioned behavior adjustment can be that after receiving the consensus result, each agent modifies its own behavior logic or updates its strategy to ensure the consistency and security of the overall link. For example, if an agent originally selected the link path as network physical node A, network physical node D, and network physical node E, and the consensus result selected network physical node A, network physical node B, and network physical node C, then the agent modifies the routing priority to prioritize forwarding to network physical node B. Meanwhile, for agents exhibiting abnormal voting behavior, the aforementioned executing entities can report their abnormal information and identity to the feedback control module. The feedback control module, based on the agent's historical performance, health level, and confidence level, will perform dynamic demotion or agent reconfiguration scheduling operations, thereby further enhancing the stability and security of the charging link service. Abnormal voting behavior can refer to a significant deviation between an agent's voting result and the majority of agent voting results, which is considered a potential anomaly (possibly due to a malicious node or incorrect judgment). For example, if six agents vote to support network physical node DA45 as a path relay, and one agent votes alone to exclude network physical node DA45, then this can be considered abnormal voting behavior by that agent.

[0073] The second sub-step involves standardizing the aforementioned agent operation information to obtain standardized agent operation information. This standardized information includes standardized CPU utilization, standardized memory utilization, the number of abnormal connection requests, the number of signature failures, and the vote rejection rate. In practice, the executing entity can use standardization to unify the agent's CPU utilization, memory utilization, number of abnormal connection requests per unit time, number of signature failures, and vote rejection rate to the [0, 1] interval, thus obtaining the standardized CPU utilization, standardized memory utilization, number of abnormal connection requests, number of signature failures, and vote rejection rate.

[0074] The third sub-step involves determining the system resource score based on the aforementioned standardized CPU utilization and standardized memory utilization. In practice, the executing entity can multiply the standardized CPU utilization by the first system resource weight and add it to the product of the standardized memory utilization and the second system resource weight to obtain the system resource score. The sum of the first system resource weight and the second system resource weight is 1.

[0075] The fourth sub-step involves determining the abnormal connection score and the signature failure score based on the number of abnormal connection requests and signature failures following the aforementioned standards. In practice, firstly, the executing entity can determine the abnormal connection score by multiplying the number of abnormal connection requests and the preset abnormal connection penalty coefficient. Then, the executing entity can determine the signature failure score by multiplying the number of signature failures and the preset signature failure penalty coefficient.

[0076] The fifth sub-step involves determining the real-time agent's health based on the aforementioned system resource score, abnormal connection score, signature failure score, and the rate of rejection of votes after the standard. In practice, the executing entity can preferably determine the multi-dimensional scores using (SR×w1+SN×w2+SC×w3+SV×w4) / 4. Here, SR, SN, SC, and SV represent the system resource score, abnormal connection score, signature failure score, and the rate of rejection of votes after the standard, respectively. w1, w2, w3, and w4 are the corresponding dimension weights, which sum to 1. Finally, the executing entity can determine the real-time agent's health as the difference between 1 and the multi-dimensional scores.

[0077] The sixth sub-step involves determining the agent's health level based on the aforementioned real-time and historical agent health levels. The historical agent health level can be the one determined in the previous unit of time. In practice, the executing entity can determine the agent health level using SH×R + (1-R)×SCU. R can be a smoothing factor (e.g., a value of 0.6) used to control the balance between the current and historical health levels. SH can be the historical agent health level. SCU can be the real-time agent health level.

[0078] The seventh sub-step involves determining the agent's confidence level based on the determined agent health level. In practice, the aforementioned executor can determine the agent confidence level using the formula C = α·S + β·Recomm + γ·SpeedFactor. Here, α, β, and γ are the corresponding weighting coefficients, summing to 1. S can be the determined agent health level. SpeedFactor can be the reciprocal of the normalized average response delay between the previous unit time and the current unit time. Recommendm can be the frequency normalization value recommended by other physical network nodes or controllers for the agent or the physical network node to which the agent resides.

[0079] The eighth sub-step involves updating the target agent link in response to the determination that the agent's health and confidence do not meet preset threshold conditions. The preset threshold conditions can be that the agent's health is greater than or equal to a preset health threshold or that the agent's confidence is greater than or equal to a preset confidence threshold. In practice, the executing entity can remove the agent from the target agent link and take it offline, and select an agent deployed in a neighboring network physical node from the constructed adjacency list or adjacency matrix representing the connection status of each network physical node to replace the agent, thereby updating the target agent link.

[0080] The aforementioned content, as an inventive point of this disclosure, solves the technical problem that "when using static links to construct network services, the dynamic changes in the operating status of each network physical node participating in communication are not considered. In cases of abnormal node resource usage or unstable behavior, it is difficult to promptly detect and remove performance-degraded or malicious nodes, leading to network service path interruptions and high response latency." Factors leading to poor network stability and low communication response latency often include: when using static links to construct network services, the dynamic changes in the operating status of each network physical node participating in communication are not considered. In cases of abnormal node resource usage or unstable behavior, it is difficult to promptly detect and remove performance-degraded or malicious nodes. To achieve this effect, multi-dimensional detection of network physical node indicators such as CPU utilization, memory utilization, abnormal connection frequency, signature failure count, and vote rejection rate is used. Weighted fusion and exponential smoothing are then employed to determine the agent's health and confidence. This allows for the early prediction and monitoring of potentially high-risk or low-reliability agent nodes, enabling timely node shutdown and replacement, thereby effectively improving the stability of the charging link service chain and reducing network communication latency.

[0081] Step 305: In response to receiving a power outage service request for the target charging device, a power outage command is sent to the target charging device according to the target intelligent agent link, and each intelligent agent is updated.

[0082] In some embodiments, the aforementioned execution entity may, in response to receiving a power-off service request for the target charging device, send a power-off command to the target charging device according to the target intelligent agent link, and update the aforementioned intelligent agents. The aforementioned power-off service request may be a request from a user's mobile terminal to stop the charging service.

[0083] In some optional implementations of certain embodiments, the aforementioned execution entity may send a power-off command to the aforementioned target charging device through the following steps based on the aforementioned target intelligent agent link:

[0084] The first step is to encapsulate the power outage service request to generate a power outage service request message. In practice, the specific implementation of encapsulating the power outage service request to generate a power outage service request message can be found in the steps outlined above for generating a charging service request message, and will not be repeated here.

[0085] The second step involves performing link verification processing on the power outage service request message based on the aforementioned target agent link to generate a second link verification result. In practice, the specific implementation of "performing link verification processing on the aforementioned power outage service request message based on the aforementioned target agent link to generate a second link verification result" can be found in the implementation steps described in the "Node Verification Steps" section above, and will not be repeated here.

[0086] Third, in response to the determination that the second link verification result indicates that the link verification is successful, a power-off command is sent to the target charging device through the target intelligent agent link. The power-off command can be an instruction acting on the target charging device to interrupt its discharge behavior.

[0087] In some optional implementations of certain embodiments, the aforementioned execution entity may update the aforementioned intelligent agents through the following steps:

[0088] The first step is to interrupt the target agent link. In practice, the executing entity can release the target agent link originally used for this charging task and disconnect the logical connections between various network physical nodes to interrupt the target agent link.

[0089] The second step involves performing the following node update steps for each agent deployed within the aforementioned charging link network:

[0090] The first sub-step involves determining the online duration of the aforementioned agent in response to the determination that the agent's parameter information includes an online identifier indicating that the agent is online. This online duration can be the runtime of the corresponding agent.

[0091] The second sub-step involves, in response to determining that the online duration exceeds a preset update cycle, taking the agent offline to update the online identifier included in the agent's parameter information. The preset update cycle can be 2 hours. In practice, the executing entity can stop the agent's operation (i.e., terminate the corresponding executable binary program), update the online identifier included in the corresponding agent parameter information to "offline," and store the agent's health and confidence levels.

[0092] The third sub-step, in response to determining that the above online duration is less than or equal to the preset update cycle, executes the following processing steps:

[0093] Sub-step one: Update the corresponding agent parameter information, including the agent's health level. In practice, the implementation steps for "updating the corresponding agent parameter information, including the agent's health level" can be referenced from the "determining agent health level" section above, and will not be repeated here.

[0094] Sub-step two involves updating the corresponding agent parameter information, including the agent confidence level. In practice, the implementation steps for "updating the corresponding agent parameter information, including the agent confidence level" can be referenced from the specific steps for "determining agent confidence level" described above, and will not be repeated here.

[0095] Sub-step three: In response to determining that the health of the aforementioned agent is less than the preset health threshold, the agent is taken offline, and the agent's corresponding parameter information is reset. In practice, the executing entity can take the agent offline and set the agent's health and confidence levels, including the agent's corresponding parameter information, to default initial values.

[0096] It should be noted that when there are offline agents in the aforementioned charging link network, the aforementioned execution entity can rebuild and run new agents in the corresponding network physical nodes to replace the offline agents.

[0097] The above embodiments of this disclosure have the following beneficial effects: the dynamic network link construction method for charging link networks according to some embodiments of this disclosure can reduce network latency and improve network service stability in charging link network scenarios through dynamic link optimization. Specifically, the reasons for high network latency and unstable network services are that static network paths are usually difficult to adapt to real-time dynamic changes in node load and network status, and when a node in the network path fails, it will directly lead to increased network service latency, frequent abnormal disconnections, or network service interruptions. Based on this, the dynamic network link construction method for charging link networks according to some embodiments of this disclosure firstly, in response to receiving a charging service request for a target charging device, constructs a target intelligent agent link based on the charging service request and pre-built intelligent agents. The intelligent agents are deployed on different network physical nodes in the charging link network. Thus, during the initial construction of the network link, by selecting the network physical node where the intelligent agent with low response time and high stability resides, the average link latency and failure rate can be reduced. Then, based on the target intelligent agent link, the charging service request is processed for link verification to generate a first link verification result. Subsequently, in response to the determination that the first link verification result indicates that the link verification is successful, a charging command is sent to the target charging device through the target agent link. This allows link verification to be performed between multiple nodes, reducing the risk of path errors or hijacking caused by tampering with a single node in the link. Secondly, in response to the determination that the target agent link is uninterrupted, the status of the target agent link is monitored to update it in real time. This allows for the determination of agent status based on real-time link data (bandwidth, latency, packet loss rate) and the dynamic replacement of abnormal agent nodes, thereby reducing network latency and improving link continuity and network communication stability. Finally, in response to receiving a power outage service request for the target charging device, a power outage command is sent to the target charging device according to the target agent link, and the various agents are updated. Therefore, after each network service ends, it is possible to promptly identify agents with high response latency and low health within the charging link network, and update or remove such agents, thereby maintaining the overall response speed of the charging link network. Consequently, in scenarios oriented towards the charging link network, network latency can be reduced and network service stability improved through dynamic link optimization.

[0098] Further reference Figure 4 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a network link dynamic construction device for charging link networks. These device embodiments are similar to... Figure 3Corresponding to the method embodiments shown, this network link dynamic construction device for charging link networks can be specifically applied to various electronic devices.

[0099] like Figure 4 As shown, a network link dynamic construction device 400 for charging link networks in some embodiments includes: a construction unit 401, a link verification unit 402, a first sending unit 403, a status monitoring unit 404, and a second sending unit 405. The device comprises the following components: a construction unit 401 configured to, in response to receiving a charging service request for a target charging device, construct a target agent link based on the charging service request and pre-built agents, wherein the agents are deployed at different network physical nodes in the charging link network; a link verification unit 402 configured to perform link verification processing on the charging service request based on the target agent link to generate a first link verification result; a first sending unit 403 configured to, in response to determining that the first link verification result indicates successful link verification, send a charging command to the target charging device through the target agent link; a status monitoring unit 404 configured to, in response to determining that the target agent link is not interrupted, perform status monitoring on the target agent link to update it in real time; and a second sending unit 405 configured to, in response to receiving a power-off service request for the target charging device, send a power-off command to the target charging device based on the target agent link and update the agents. It is understood that the units described in this network link dynamic construction device 400 for charging link networks are similar to those in reference devices. Figure 3 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to the network link dynamic construction device 400 for charging link networks and the units contained therein, and will not be repeated here.

[0100] The following is for reference. Figure 5 It illustrates electronic devices suitable for implementing some embodiments of the present disclosure (such as...). Figure 2 The diagram shows the structure of the computing device 201. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of this disclosure. Figure 5As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the methods described above. The processor provides computational and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the execution of the computer program in the non-volatile storage medium; when executed by the processor, the computer program causes the processor to perform any of the methods described above. The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the computer device to which the present disclosure is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0101] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0102] In one embodiment, the processor is configured to run a computer program stored in a memory to perform the following steps: In response to receiving a charging service request for a target charging device, constructing a target intelligent agent link based on the charging service request and pre-built intelligent agents, wherein the intelligent agents are deployed at different network physical nodes in the charging link network; performing link verification processing on the charging service request based on the target intelligent agent link to generate a first link verification result; in response to determining that the first link verification result indicates that the link verification is successful, sending a charging command to the target charging device through the target intelligent agent link; in response to determining that the target intelligent agent link is not interrupted, performing status monitoring on the target intelligent agent link to update the target intelligent agent link in real time; and in response to receiving a power outage service request for the target charging device, sending a power outage command to the target charging device based on the target intelligent agent link and updating the intelligent agents.

[0103] This disclosure also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to the various embodiments of the methods described above.

[0104] The aforementioned computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. Alternatively, the aforementioned computer-readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0105] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0106] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for dynamically constructing network links for charging link networks, applied to computing devices, characterized in that, include: In response to receiving a charging service request for a target charging device, a target intelligent agent link is constructed based on the charging service request and pre-built intelligent agents, wherein the intelligent agents are deployed at different network physical nodes in the charging link network. Based on the target intelligent agent link, the charging service request is subjected to link verification processing to generate a first link verification result; In response to determining that the first link verification result indicates that the link verification is successful, a charging command is sent to the target charging device through the target intelligent agent link; In response to determining that the target agent link is not interrupted, the status of the target agent link is monitored to update the target agent link in real time; In response to receiving a power outage service request for the target charging device, a power outage command is sent to the target charging device according to the target intelligent agent link, and the individual intelligent agents are updated. The step of performing link verification processing on the charging service request based on the target intelligent agent link to generate a first link verification result includes: The charging service request is encapsulated to generate a charging service request message, wherein the charging service request message includes: signature information corresponding to the computing device; For each target agent in the target agent link, perform the following node verification steps: The charging service request message is sent to the target agent to receive agent verification information returned by the target agent. After receiving the charging service request message, the target agent verifies whether the signature information of the computing device has been tampered with using a pre-stored public key and signature algorithm, and verifies whether the charging service request message has been tampered with. The agent verification information includes the corresponding node signature information, agent parameter information, and agent verification result. The corresponding node signature information is the node signature information of the network physical node where the target agent is located. In response to determining that the agent parameter information included in the received agent verification information meets the preset confidence conditions, the agent verification result included in the agent verification information is determined as the target agent verification result; Based on the verification results of each agent, the first link verification result is generated.

2. The method according to claim 1, characterized in that, Before receiving the charging service request for the target charging device, the method further includes: For the network physical nodes in the charging link network, the following node preprocessing steps are performed: Collect the node parameter information and node identifier of the network physical nodes; Based on the collected node parameter information, construct node profile tags; The node profile label and the node identifier are encrypted and signed to generate node signature information; Based on the node parameter information, intelligent agents are deployed in the network physical nodes.

3. The method according to claim 2, characterized in that, The step of constructing the target agent link based on the charging service request and the pre-built agents includes: The charging service request is parsed to obtain the charging device identifier and the charging device location information; Based on the corresponding intelligent agent parameter information, node filtering processing is performed on each intelligent agent to determine each candidate intelligent agent; Based on the location information of the charging device, link simulation is performed on each candidate intelligent agent to generate the target intelligent agent link information; Based on the target agent link information, construct the target agent link corresponding to the charging device identifier.

4. The method according to claim 1, characterized in that, Sending a power-off command to the target charging device according to the target intelligent agent link includes: The power outage service request is encapsulated to generate a power outage service request message; Based on the target intelligent agent link, the power outage service request message is processed for link verification to generate a second link verification result; In response to determining that the second link verification result indicates that the link verification is successful, a power-off command is sent to the target charging device through the target intelligent agent link.

5. The method according to claim 1, characterized in that, The updating of each of the intelligent agents includes: Disrupt the target agent's link; For each agent deployed within the charging link network, the following node update steps are performed: In response to determining that the online identifier of the agent's parameter information indicates that the agent is online, the online duration of the agent is determined; In response to determining that the online duration is greater than a preset update cycle, the agent is taken offline to update the online identifier included in the agent parameter information corresponding to the agent. In response to determining that the online duration is less than or equal to a preset update cycle, the following processing steps are performed: Update the corresponding agent parameter information, including agent health. Update the corresponding agent parameter information, including the agent confidence level; In response to determining that the health of the agent is less than a preset health threshold, the agent is taken offline, and the agent parameter information corresponding to the agent is reset.

6. A network link dynamic construction device for charging link networks, characterized in that, include: The construction unit is configured to, in response to receiving a charging service request for a target charging device, construct a target intelligent agent link based on the charging service request and pre-constructed intelligent agents, wherein the intelligent agents are deployed at different network physical nodes in the charging link network; A link verification unit is configured to perform link verification processing on the charging service request based on the target agent link to generate a first link verification result; wherein, the step of performing link verification processing on the charging service request based on the target agent link to generate the first link verification result includes: The charging service request is encapsulated to generate a charging service request message, wherein the charging service request message includes: signature information corresponding to the computing device; For each target agent in the target agent link, perform the following node verification steps: The charging service request message is sent to the target agent to receive agent verification information returned by the target agent. After receiving the charging service request message, the target agent verifies whether the signature information of the computing device has been tampered with using a pre-stored public key and signature algorithm, and verifies whether the charging service request message has been tampered with. The agent verification information includes the corresponding node signature information, agent parameter information, and agent verification result. The corresponding node signature information is the node signature information of the network physical node where the target agent is located. In response to determining that the agent parameter information included in the received agent verification information meets the preset confidence conditions, the agent verification result included in the agent verification information is determined as the target agent verification result; Based on the verification results of each agent, the first link verification result is generated; The first sending unit is configured to send a charging command to the target charging device through the target intelligent agent link in response to determining that the first link verification result indicates that the link verification is successful. A status monitoring unit is configured to monitor the status of the target agent link in response to determining that the target agent link is not interrupted, so as to update the target agent link in real time. The second sending unit is configured to, in response to receiving a power-off service request for the target charging device, send a power-off command to the target charging device according to the target agent link, and update each agent.

7. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 5.

8. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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