A charging station heterogeneous device cooperation method, system and device based on electric horse distributed soft bus

Abstract

The application discloses a charging station heterogeneous device cooperation method, system and equipment based on electric Hong distribution type soft bus, the method splits subtasks by acquiring real-time cooperation events of the charging station and analyzes the computing power demand of each subtask, obtains the task descriptor of multiple cooperation subtasks, performs computing power matching from the virtual computing power resource pool according to the task descriptor and sends the subtask to the matched computing power node, the computing power node receives the corresponding subtask, and whether the self computing power resource satisfies the required computing power resource of the matched subtask is judged according to the task descriptor, if it is satisfied, local decision and processing are carried out, if it is not satisfied, the subtask is distributed in all computing power nodes with idle computing power resources for distributed cooperative processing, and the subtask processing result is fed back to the central controller for confirmation. The application has the effect of fully utilizing the idle computing power resources of multiple heterogeneous devices, cooperatively reducing the computing pressure of the central controller to achieve load balancing.

Description

Technical Field

[0001] This invention relates to the technical field of heterogeneous device management in charging stations, and in particular to a collaborative method, system, and device for heterogeneous devices in charging stations based on the Dianhong distributed soft bus. Background Technology

[0002] Currently, with the continuous expansion of the scale of new energy vehicles, the demand for charging stations is also increasing. However, existing charging stations contain a variety of heterogeneous devices with different communication protocols, such as charging piles and cameras, which require collaborative management of multiple heterogeneous devices in the charging station.

[0003] In existing charging stations, each device typically communicates with a central controller, which manages all devices uniformly. However, the operation of a charging station usually requires the coordinated operation of multiple heterogeneous devices. For example, a collaborative task of identifying a vehicle and initiating a charging session may require multiple sub-tasks such as video analysis and power adjustment. Traditional solutions usually concentrate all the computing pressure on the station controller, which needs to have built-in protocol parsing libraries from all manufacturers. A large amount of the station controller's computing resources are consumed in repetitive protocol decoding and encoding. When the charging equipment in the charging station is working synchronously, it can easily lead to single-point overload of the controller, while edge devices with computing capabilities, such as AI cameras, are idle. The computing resources are not fully utilized, resulting in the overload of computing resources of the central controller and the idle computing power of edge devices. Summary of the Invention

[0004] To address the problem of underutilized computing resources caused by the overloaded computing resource requirements of the central controller and the idle computing power of edge devices in existing technologies, this application provides a method, system, and device for heterogeneous device collaboration in charging stations based on the Dianhong distributed soft bus. This method enables dynamic offloading and load balancing of computing tasks from the center to the edge. When dealing with large-scale, high-concurrency, and multi-type device collaboration, it can intelligently allocate communication and computing loads according to real-time status, thereby significantly improving the overall efficiency and communication reliability of the charging station.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A collaborative method for heterogeneous devices in a charging station based on the Dianhong distributed soft bus, the method comprising: The real-time collaborative events of the charging station are acquired, the real-time collaborative events are divided into sub-tasks and the computing power requirements of each sub-task are analyzed to obtain task descriptors corresponding to multiple collaborative sub-tasks. According to the task descriptor, computing power is matched from the virtual computing power resource pool, and the subtask is sent to the corresponding computing power node according to the matching result; The computing node receives the corresponding subtask and determines whether its own computing resources meet the computing resources required by the matched subtask based on the corresponding task descriptor. If they do, it makes local decisions and processes the assigned subtask. If the conditions are not met, the subtask will be distributed and collaboratively processed across all computing nodes with available computing resources, and the processing results of the subtask will be fed back to the central controller for confirmation.

[0006] In a preferred embodiment, this application can be further configured such that, prior to performing real-time collaborative event analysis of the charging station, the method further includes: The system acquires real-time registration data packets when heterogeneous devices in the charging station register with the Dianhong soft bus, performs communication feature analysis on the real-time registration data packets, extracts communication protocol feature vectors, and generates protocol fingerprint IDs. Using the device ID as the association point, construct and store the mapping relationship between the protocol fingerprint ID, device ID, and protocol driver plugin, and generate a protocol fingerprint-driver mapping table. Upon receiving new registration information, a temporary registration fingerprint is generated. The temporary registration fingerprint is then matched with the protocol fingerprint-driver mapping table. Based on the matching result, the corresponding new registration processing mechanism is triggered to perform new registration processing.

[0007] In a preferred embodiment, this application can be further configured as follows: upon receiving new registration information, a temporary registration fingerprint is generated; the temporary registration fingerprint is matched with the protocol fingerprint-driver mapping table; and a corresponding new registration processing mechanism is triggered based on the matching result to perform new registration processing. Specifically, this includes: If the temporary registration fingerprint matches the fingerprint in the protocol fingerprint-driver mapping table, the corresponding protocol driver is called according to the protocol fingerprint-driver mapping table to parse the new registration information and obtain the collaborative events in the new registration information; If the temporary registered fingerprint does not match the fingerprint in the protocol fingerprint-driver mapping table, the communication protocol in the new registration information will be marked as an unknown protocol, and an alarm message will be generated and sent to the management terminal.

[0008] In a preferred embodiment, this application can be further configured as follows: if the temporary registered fingerprint does not match the fingerprint in the protocol fingerprint-driver mapping table, the communication protocol in the new registration information is marked as an unknown protocol, and an alarm message is generated and sent to the management terminal, the application further includes: Obtain confirmation information from the management terminal regarding the unknown protocol. If the unknown protocol is confirmed to be an untrusted protocol, reject the new registration application. When the unknown protocol is confirmed to be a trusted protocol, the unknown protocol is updated in the protocol fingerprint-driver mapping table.

[0009] In a preferred embodiment, this application can be further configured as follows: before performing computing power matching from the virtual computing power resource pool according to the task descriptor, and sending the subtask to the corresponding computing power node based on the matching result, the following steps are included: Obtain the registration information of each computing node with computing power resources, wherein the registration information includes device capabilities and a description of computing power resources; The computing power resource status of each computing power node is analyzed based on the registration information. The computing power resource status is associated with the corresponding computing power node and summarized to construct a virtual computing power resource pool for all heterogeneous devices in the charging station. The changes in computing resources of each computing node are dynamically acquired according to a preset update mechanism, and the virtual computing resource pool is dynamically updated.

[0010] In a preferred embodiment, this application can be further configured as follows: the computing node receives the corresponding subtask and, based on the corresponding task descriptor, determines whether its own computing resources meet the computing resources required by the matched subtask. If so, it performs local decision-making and processing on the allocated subtask, specifically including: When the computing power node's own computing power resources meet the computing power resources required by the matching subtask, the computing power node's own protocol fingerprint is invoked, and the corresponding protocol driver is loaded through the protocol fingerprint-driver mapping table to respond to the subtask. If the computing power resources of the computing power node itself do not meet the computing power resources required by the matching subtask, the idle computing power of all devices is judged and sorted through the virtual computing power resource pool, and the optimal edge computing power node is selected to perform edge processing on the subtask according to the sorting result.

[0011] In a preferred embodiment, this application can be further configured such that the method also includes: Determine whether the real-time load of the central controller has reached a preset load threshold. When the central controller reaches the preset load threshold, a peak shaving event is generated and broadcast to all computing power nodes in a distributed manner through the Elec-Tech soft bus. After each computing node receives the broadcast peak reduction event, it calls the local protocol driver through the protocol fingerprint-driver mapping table to parse the peak reduction event and obtain the target total power reduction. Combining the state constraints of each computing node with user constraints, calculate the maximum power adjustment value for each computing node to share the target total power reduction, and distribute the power allocation calculation task to each computing node in a distributed manner; The power allocation decision for each computing node is encoded using its own protocol fingerprint and fed back to the central controller for aggregation and confirmation.

[0012] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A collaborative system for heterogeneous devices in charging stations based on the Elec-Hong distributed soft bus, the system being applied to the aforementioned collaborative method for heterogeneous devices in charging stations based on the Elec-Hong distributed soft bus, the system comprising: The event processing module is used to acquire real-time collaborative events of the charging station, break down the real-time collaborative events into sub-tasks, analyze the computing power requirements of each sub-task, and obtain task descriptors corresponding to multiple collaborative sub-tasks. The computing power matching module is used to match computing power from the virtual computing power resource pool according to the task descriptor, and send the subtask to the corresponding computing power node according to the matching result; The node local decision module is used for the computing node to receive the corresponding subtask and determine whether its own computing resources meet the computing resources required by the matching subtask according to the corresponding task descriptor. If they meet the requirements, the node performs local decision-making and processing on the assigned subtask. The distributed collaborative processing module is used to perform distributed collaborative processing of the subtask on all computing nodes with available computing resources if the conditions are not met, and to feed back the subtask processing results to the central controller for confirmation.

[0013] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for collaborative operation of heterogeneous charging station devices based on the Dianhong distributed soft bus.

[0014] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for collaborative operation of heterogeneous devices in a charging station based on the Dianhong distributed soft bus.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application constructs a distributed computing power resource pool at the charging station level, realizing dynamic offloading and load balancing of computing tasks from the center to the edge, breaking the bottleneck of central computing power. At the same time, through protocol fingerprinting, the complex protocol identification problem is transformed into an efficient fingerprint matching problem, reducing the complex operation of protocol parsing in the field controller. The processing of communication protocols and the scheduling of computing power resources are deeply integrated, enabling the system to intelligently allocate communication and computing loads according to real-time status when dealing with large-scale, high-concurrency, and multi-type device collaboration, resulting in a qualitative improvement in overall efficiency and reliability. 2. This application abstracts the private communication protocol characteristics of heterogeneous devices into a lightweight, indexable "fingerprint," enabling the system to "identify" the protocol without "understanding" it. This achieves efficient protocol routing and conversion, reducing the computing power overhead of the site controller. At the same time, it virtualizes the computing power of all computing devices (controllers, smart charging piles, AI cameras) in the site into a resource pool. When device collaboration is triggered, sub-tasks are dynamically assigned based on task requirements and real-time computing power status, further reducing the computing resource requirements of the central controller. 3. This application breaks down computing power collaboration events into multiple sub-tasks, distributes computing power across all sub-tasks, uses the allocated computing power nodes to call their own protocol fingerprints to process the sub-tasks, and temporarily loads the corresponding protocol drivers through the protocol fingerprint-driver mapping table for communication and parsing. This "offloads" the computing power overhead of protocol conversion from the central controller to the edge devices, thereby achieving distributed processing of computing tasks. By offloading computing tasks and protocol conversion tasks to the edge, the system still achieves fast and reliable response to power grid commands even under high load on the central controller. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart illustrating the implementation of the collaborative method for heterogeneous devices in a charging station based on the Dianhong distributed soft bus in this embodiment.

[0018] Figure 2 This is a flowchart illustrating the implementation of the protocol fingerprint-driver mapping table in the charging station heterogeneous device collaboration method of this embodiment.

[0019] Figure 3 This is a flowchart illustrating the implementation of the new registration process for the collaborative method of heterogeneous devices in charging stations in this embodiment.

[0020] Figure 4 This is a flowchart illustrating the implementation of the charging station heterogeneous device collaboration method for constructing a virtual computing resource pool in this embodiment.

[0021] Figure 5 This is a flowchart illustrating the implementation of the charging station heterogeneous device collaboration method for local decision-making and processing in this embodiment.

[0022] Figure 6 This is a flowchart illustrating the implementation of the charging station heterogeneous device collaboration method for peak shaving event handling in this embodiment.

[0023] Figure 7 This is a structural block diagram of the heterogeneous device collaborative system for charging stations based on the Dianhong distributed soft bus in this embodiment.

[0024] Figure 8 This is a schematic diagram of the internal structure of a computer device used to implement a collaborative method for heterogeneous devices in a charging station. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] In one embodiment, such as Figure 1 As shown, this application discloses a collaborative method for heterogeneous devices in charging stations based on the Dianhong distributed soft bus, which specifically includes the following steps: S10: Obtain real-time collaborative events of the charging station, break down the real-time collaborative events into subtasks, analyze the computing power requirements of each subtask, and obtain task descriptors corresponding to multiple collaborative subtasks.

[0030] Specifically, the system acquires real-time collaborative events from charging stations. For example, when an electric vehicle enters, the charging station needs to complete a collaborative scheduling task involving "vehicle identification authentication - video monitoring - V2G scheduling initiation." The charging station's resource scheduler decomposes these real-time collaborative events into multiple sub-tasks, such as: Sub-task T1 (computation-intensive): video stream vehicle recognition and identification; Sub-task T2 (control-intensive): establishing a secure connection with the V2G charging station and distributing charging parameters; Sub-task T3 (data-intensive): recording and uploading the session log. Simultaneously, the resource scheduler analyzes the computational requirements of each sub-task, generates corresponding task descriptors, and publishes the task descriptor for each collaborative sub-task via the eHongsoft bus. The task descriptor includes the required computation type, expected completion time, and data input source.

[0031] In this embodiment, as Figure 2 As shown, before performing real-time collaborative event analysis of charging stations, the following steps are also included: S101: Obtain the real-time registration data packet when heterogeneous devices in the charging station register with the Dianhong soft bus, perform communication feature analysis on the real-time registration data packet, extract the communication protocol feature vector, and generate a protocol fingerprint ID.

[0032] Specifically, when a new device, such as a V2G pile from manufacturer A, registers with the central controller via the Elec-Tech Soft Bus, the system will have the new device send a set of real-time registration data packets. The system will parse the data packets to obtain communication characteristics, including but not limited to connection method, default port, message header feature code, data packet length distribution, and interactive heartbeat mode. The communication characteristics will be encoded using a preset encoding method to obtain a communication protocol feature vector, which will then be converted into a fixed-length, unique protocol fingerprint ID using a hash function.

[0033] S102: Using the device ID as the association point, construct and store the mapping relationship between the protocol fingerprint ID, device ID, and protocol driver plugin, and generate a protocol fingerprint-driver mapping table.

[0034] Specifically, the protocol fingerprint ID, device ID, and protocol driver plugin are associated with the device ID as the association point, and a mapping relationship between the three is constructed and stored to form a protocol fingerprint-driver mapping table.

[0035] S103: When a new registration information is received, a temporary registration fingerprint is generated. The temporary registration fingerprint is matched with the protocol fingerprint-driver mapping table. Based on the matching result, the corresponding new registration processing mechanism is triggered to perform new registration processing.

[0036] Specifically, when the system receives an unknown data packet, it extracts communication features based on the new registration information of the unknown data packet to generate a temporary registration fingerprint. This temporary registration fingerprint is then matched against the protocol fingerprint-driver mapping table. The fingerprint matching result includes successful and unsuccessful matches. Based on the matching result, the corresponding new registration processing mechanism is triggered to perform new registration processing for the location data packet, such as... Figure 3 As shown, it specifically includes: S1031: If the temporary registration fingerprint matches the fingerprint in the protocol fingerprint-driver mapping table, then the corresponding protocol driver is called according to the protocol fingerprint-driver mapping table to parse the new registration information and obtain the collaborative events in the new registration information.

[0037] Specifically, when the temporary registration fingerprint matches the fingerprint in the protocol fingerprint-driver mapping table, it indicates that the temporary registration fingerprint is a trusted protocol or a registered device. In this case, the corresponding protocol driver is directly called through the protocol fingerprint-driver mapping table to parse the new registration information and obtain the collaborative events in the new registration information, such as vehicle identification - establishing a secure connection with the V2G charging pile and allocating charging parameters.

[0038] S1032: If the temporary registration fingerprint does not match the fingerprint in the protocol fingerprint-driver mapping table, the communication protocol in the new registration information will be marked as an unknown protocol, and an alarm message will be generated and sent to the management terminal.

[0039] Specifically, if the temporary registration fingerprint does not match the fingerprint in the protocol fingerprint-driver mapping table, the communication protocol in the new registration information will be marked as an unknown protocol, and the new registration information and protocol matching status will be packaged into an alarm message and sent to the management terminal.

[0040] In this embodiment, the method further includes the following after step S1032: S1033: Obtain confirmation information from the management end regarding the unknown protocol. If the unknown protocol is confirmed to be an untrusted protocol, reject the new registration application.

[0041] Specifically, obtain confirmation information from the management end regarding unknown protocols. This confirmation information includes whether the location protocol is a trusted or untrusted protocol. If the unknown protocol is confirmed to be an untrusted protocol, the corresponding new registration application will be rejected.

[0042] S1034: When an unknown protocol is confirmed to be a trusted protocol, update the unknown protocol to the protocol fingerprint-driver mapping table.

[0043] Specifically, when an unknown protocol is confirmed as a trusted protocol, its protocol fingerprint is added to the protocol fingerprint-driver mapping table.

[0044] S20: Match computing power from the virtual computing power resource pool according to the task descriptor, and send the subtask to the corresponding computing power node based on the matching result.

[0045] Specifically, based on the task descriptor, computing resources are matched from the virtual computing resource pool according to task type and computing power requirements. For example, for subtasks such as vehicle identification and video surveillance, computing power is matched based on the computing resource status of corresponding heterogeneous devices, such as AI-capable cameras and site edge servers, in the computing resource pool. The AI ​​camera or site edge server with the lowest network latency and lightest current load is selected for execution to achieve edge processing and avoid video streams consuming backbone network bandwidth. Based on the computing resource matching results, the corresponding subtask is sent to the corresponding computing node, such as the AI ​​camera or edge server, via the Elec-Tech Soft Bus.

[0046] In this embodiment, as Figure 4 As shown, the procedure before step S20 also includes: S201: Obtain the registration information of each computing node with computing power resources. The registration information includes the device capabilities and a description of the computing power resources.

[0047] Specifically, when heterogeneous devices register with the central controller via the Elec-Tech Soft Bus, the registration information of each heterogeneous device in the charging station with computing power resources is obtained, including device capabilities and computing power resource descriptions. The computing power resource descriptions include: computing power, memory capacity, current load rate, network overhead, etc. Computing power can be CPU computing power (such as DMIPS), AI computing power (such as TOPS), etc. Memory capacity can be the size of memory space. Current load rate can be CPU utilization, memory occupancy, etc. Network overhead can be the network latency with the task requester.

[0048] S202: Analyze the computing power resource status of each computing power node based on the registration information, associate the computing power resource status with the corresponding computing power node and summarize them to build a virtual computing power resource pool for all heterogeneous devices in the charging station.

[0049] Specifically, the system combines the registration information to obtain the computing power of the devices and maintains a dynamic resource status card for each computing power node, which represents the computing power resource status of the computing power node and is used to describe the computing power of the device. The computing power resource status is associated with the corresponding computing power nodes and summarized to form a virtual computing power resource pool for all heterogeneous devices in the charging station.

[0050] S203: Dynamically acquire the changes in computing resources of each computing node according to the preset update mechanism, and dynamically update the virtual computing resource pool.

[0051] Specifically, the site controller (as a resource scheduler) periodically collects the changes in computing resources of each computing node through the Elec-Tech soft bus according to a preset update mechanism, updates the resource status cards of all computing nodes, and dynamically updates the virtual computing resource pool.

[0052] S30: The computing node receives the corresponding subtask and, based on the corresponding task descriptor, determines whether its own computing resources meet the computing resources required by the matched subtask. If they do, it makes local decisions and processes the assigned subtask.

[0053] Specifically, a computing node receives the corresponding subtask and, based on the subtask's task descriptor and its own available computing resources, determines whether its idle computing resources meet the subtask's requirements. If its idle computing resources are greater than or equal to the subtask's requirements, it is considered satisfied, and it uses its own computing resources to perform local decision-making and task response processing for the assigned subtask. If its idle computing resources are less than the subtask's requirements, it is considered unsatisfactory. If a single computing node in the virtual resource pool has sufficient idle computing resources to respond to the current subtask, it assigns the subtask to the optimal edge computing node for edge processing. Figure 5 As shown, it specifically includes: S301: When the computing power node's own computing power resources meet the computing power resources required by the matching subtask, the computing power node's own protocol fingerprint is called, and the corresponding protocol driver is loaded through the protocol fingerprint-driver mapping table to respond to the subtask.

[0054] Specifically, when the computing power node's own resources are greater than or equal to the computing power resources required by the matching subtask, the computing power node's own protocol fingerprint is invoked, and the corresponding protocol driver is loaded through the protocol fingerprint-driver mapping table to respond to the subtask.

[0055] S302: If the computing power resources of the computing power node itself do not meet the computing power resources required by the matching subtask, the idle computing power of all devices is judged and sorted through the virtual computing power resource pool, and the optimal edge computing power node is selected to perform edge processing on the subtask according to the sorting result.

[0056] Specifically, when the computing power resources of a computing node are less than the computing power resources required by the matched subtask, the idle computing power of all devices is determined by checking the latest updated status of the computing power nodes corresponding to each heterogeneous device in the virtual computing power resource pool, based on their respective load operation requirements. These idle computing power resources are then sorted from largest to smallest, and the optimal edge computing power node ranked first is selected for edge processing of the subtask. Depending on the task, the target edge computing power can be an AI camera, a site edge server, or a V2G charging pile, etc.

[0057] S40: If not satisfied, the subtask will be distributed and collaboratively processed on all computing nodes with available computing resources, and the processing results of the subtask will be fed back to the central controller for confirmation.

[0058] Specifically, when the computing power resources of a single computing node cannot meet the computing power resources required by a subtask, a virtual computing power resource pool is used to coordinate the computing power of all computing nodes with available computing power resources to perform distributed collaborative processing of the subtask. For example: For subtask T1: Both AI-capable cameras and site edge servers can respond using their own protocol fingerprints. The scheduler selects the AI ​​camera or site edge server with the lowest network latency and lightest current load from the computing resource pool to execute the task, achieving edge processing and avoiding video streams consuming backbone network bandwidth.

[0059] For subtask T2: The scheduler discovers that the communication protocol fingerprint ID of the target V2G stub is FP_A1B2C3D4, and automatically loads the corresponding protocol driver in the soft bus agent that commands the V2G stub. Control commands are correctly encoded and sent through the secure link.

[0060] Protocol driver plugins can be dynamically deployed to requesting devices. For example, when an AI camera needs to directly query the status of a V2G stub, the camera's local soft bus agent can temporarily load the protocol driver plugin for that V2G stub to directly communicate and parse the data. This "offloads" the computational overhead of protocol conversion from the central controller to the edge devices, enabling distributed processing of computing tasks.

[0061] The processing results of each subtask are fed back to the central controller via the Elec-Tech soft bus for task processing confirmation.

[0062] like Figure 6 As shown, this embodiment also includes: S50: Determines whether the real-time load of the central controller has reached the preset load threshold. When the central controller reaches the preset load threshold, it generates a peak shaving event and broadcasts it to all computing power nodes via the Elec-Tech soft bus.

[0063] Specifically, the system monitors the real-time load of the central controller and determines whether the real-time load has reached a preset load threshold. If the CPU load of the central controller is greater than or equal to 80%, it indicates that the preset load threshold has been reached. When the preset load threshold is reached, a peak-shaving event is generated to avoid overload, and a distributed broadcast is made to all computing power nodes via the Elec-Tech soft bus. The computing power nodes here include all V2G charging piles and DC charging piles.

[0064] S60: After each computing node receives the broadcast peak reduction event, it calls the local protocol driver through the protocol fingerprint-driver mapping table to parse the peak reduction event and obtain the target total power reduction.

[0065] Specifically, after each computing node receives the broadcast peak shaving event, it uses the protocol fingerprint of the charging pile to call the local protocol driver through the protocol fingerprint-driver mapping table to autonomously parse the peak shaving event and obtain the target total power reduction.

[0066] S70: Combining the state constraints of each computing node with user constraints, calculate the maximum power adjustment value for each computing node to share the target total power reduction, and distribute the power allocation calculation task to each computing node.

[0067] Specifically, based on the current load and computing power occupancy of each computing node, each computing node is subject to its own state constraints and user constraints. Under the constraint of the target total power reduction, the maximum power adjustment value that each computing node can share is calculated collaboratively. For example, V2G pile A: discharges 20kW, DC pile B: reduces power by 15kW. The complex power allocation calculation task of the central controller is distributed to each computing node, i.e., each charging pile. The computing power consumed is the local, idle computing power of each charging pile, which achieves the peak shaving effect of reducing the computing pressure on the central controller.

[0068] S80: The power allocation decision of each computing node is encoded through its own protocol fingerprint and fed back to the central controller for aggregation and confirmation.

[0069] Specifically, the power allocation decision of each computing node is encoded using its own protocol fingerprint and reported to the central controller via the Elec-Tech soft bus for aggregation and confirmation. The central controller only needs to perform simple aggregation and confirmation, resulting in extremely low CPU load. By "offloading" computing and protocol conversion tasks to edge computing nodes, a fast and reliable response to power grid commands can be achieved even under high load on the central controller.

[0070] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

[0071] In one embodiment, a collaborative system for heterogeneous charging station devices based on the Elec-Honda distributed soft bus is provided. This collaborative system corresponds one-to-one with the collaborative method for heterogeneous charging station devices based on the Elec-Honda distributed soft bus described in the above embodiments. Figure 7As shown, the charging station heterogeneous device collaborative system based on the Dianhong distributed soft bus includes an event processing module, a computing power matching module, a node local decision-making module, and a distributed collaborative processing module. Detailed descriptions of each functional module are as follows: The event processing module is used to acquire real-time collaborative events of the charging station, break down the real-time collaborative events into subtasks, analyze the computing power requirements of each subtask, and obtain task descriptors corresponding to multiple collaborative subtasks.

[0072] The computing power matching module is used to match computing power from the virtual computing power resource pool according to the task descriptor, and send the subtask to the corresponding computing power node according to the matching result.

[0073] The node local decision module is used by computing nodes to receive the corresponding subtasks and determine whether their own computing resources meet the computing resources required by the matching subtasks based on the corresponding task descriptors. If they do, the nodes will make local decisions and process the assigned subtasks.

[0074] The distributed collaborative processing module is used to perform distributed collaborative processing of the subtask on all computing nodes with available computing resources if the conditions are not met, and to feed back the subtask processing results to the central controller for confirmation.

[0075] Preferably, before the event handling module, it also includes: The protocol fingerprint generation submodule is used to obtain the real-time registration data packets when heterogeneous devices in the charging station register with the Dianhong soft bus, perform communication feature analysis on the real-time registration data packets, extract the communication protocol feature vector, and generate the protocol fingerprint ID.

[0076] The fingerprint driver mapping submodule is used to build and store the mapping relationship between protocol fingerprint ID, device ID, and protocol driver plugin with device ID as the association point, and generate a protocol fingerprint-driver mapping table.

[0077] The new registration processing submodule is used to generate a temporary registration fingerprint when new registration information is received, match the temporary registration fingerprint with the protocol fingerprint-driver mapping table, and trigger the corresponding new registration processing mechanism to perform new registration processing based on the matching result.

[0078] Preferably, the new registration processing submodule specifically includes: The fingerprint matching unit is used to parse the new registration information and obtain the collaborative events in the new registration information by calling the corresponding protocol driver according to the protocol fingerprint-driver mapping table if the temporary registration fingerprint matches the fingerprint in the protocol fingerprint-driver mapping table.

[0079] The unknown alarm unit is used to mark the communication protocol in the newly registered information as an unknown protocol and generate alarm information to be sent to the management terminal if the temporary registered fingerprint does not match the fingerprint in the protocol fingerprint-driver mapping table.

[0080] Preferably, the unknown alarm unit also includes: The new application rejection unit is used to obtain confirmation information from the management end regarding unknown protocols. When it is confirmed that the unknown protocol is an untrusted protocol, the new registration application is rejected.

[0081] The protocol update unit is used to update the unknown protocol to the protocol fingerprint-driver mapping table when the unknown protocol is confirmed to be a trusted protocol.

[0082] Preferably, before the computing power matching module, it includes: The registration information acquisition submodule is used to obtain the registration information of each computing node with computing power resources. The registration information includes the device capabilities and a description of the computing power resources.

[0083] The computing power resource pool construction submodule is used to analyze the computing power resource status of each computing power node based on the registration information, associate the computing power resource status with the corresponding computing power node and summarize them to build a virtual computing power resource pool for all heterogeneous devices in the charging station.

[0084] The resource pool update submodule is used to dynamically obtain the changes in computing resources of each computing node according to a preset update mechanism, and to dynamically update the virtual computing resource pool.

[0085] Preferably, the node local decision-making module specifically includes: The subtask local processing submodule is used to call the computing power node's own protocol fingerprint when the computing power node's own computing power resources meet the computing power resources required by the matching subtask. The corresponding protocol driver is loaded through the protocol fingerprint-driver mapping table to respond to and process the subtask.

[0086] The subtask edge processing submodule is used to determine the idle computing power of all devices and sort them through the virtual computing power resource pool when the computing power resources of the computing power node itself do not meet the computing power resources required by the matching subtask. Based on the sorting result, the optimal edge computing power node is selected to perform edge processing on the subtask.

[0087] Preferably, the method further includes: The event broadcast module is used to determine whether the real-time load of the central controller has reached the preset load threshold. When the central controller reaches the preset load threshold, a peak shaving event is generated and broadcast to all computing power nodes through the Elec-Tech soft bus.

[0088] The event collaborative parsing module is used by each computing node to parse the peak-shaving event after receiving the broadcast peak-shaving event, and to obtain the target total power reduction by calling the local protocol driver through the protocol fingerprint-driver mapping table based on its own protocol fingerprint.

[0089] The power adjustment module is used to combine the state constraints of each computing node with user constraints to calculate the maximum power adjustment value of the target total power reduction to be shared by each computing node, and to distribute the power allocation calculation task to each computing node.

[0090] The decision confirmation module is used to encode the power allocation decision of each computing node through its own protocol fingerprint and feed it back to the central controller for aggregation and confirmation.

[0091] Specific limitations regarding the collaborative system for heterogeneous charging station devices based on the Elec-Hong distributed soft bus can be found in the limitations of the collaborative method for heterogeneous charging station devices based on the Elec-Hong distributed soft bus mentioned above, and will not be repeated here. Each module in the aforementioned collaborative system for heterogeneous charging station devices based on the Elec-Hong distributed soft bus can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0092] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores collaborative data for heterogeneous charging station devices based on the Dianhong distributed soft bus. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a collaborative method for heterogeneous charging station devices based on the Dianhong distributed soft bus.

[0093] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a method for collaborative operation of heterogeneous devices in a charging station based on a distributed soft bus.

[0094] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0095] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0096] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0097] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A collaborative method for heterogeneous devices in a charging station based on a distributed soft bus, characterized in that, The method includes: The real-time collaborative events of the charging station are acquired, the real-time collaborative events are divided into sub-tasks and the computing power requirements of each sub-task are analyzed to obtain task descriptors corresponding to multiple collaborative sub-tasks. According to the task descriptor, computing power is matched from the virtual computing power resource pool, and the subtask is sent to the corresponding computing power node according to the matching result; The computing node receives the corresponding subtask and determines whether its own computing resources meet the computing resources required by the matched subtask based on the corresponding task descriptor. If they do, it makes local decisions and processes the assigned subtask. If the conditions are not met, the subtask will be distributed and collaboratively processed across all computing nodes with available computing resources, and the processing results of the subtask will be fed back to the central controller for confirmation.

2. The method for collaborative operation of heterogeneous devices in charging stations based on the Dianhong distributed soft bus according to claim 1, characterized in that, Before performing real-time collaborative event analysis of the charging station, the method further includes: The real-time registration data packet of the heterogeneous device of the charging station registers with the Dianhong soft bus is obtained, and the communication feature analysis of the real-time registration data packet is performed to extract the communication protocol feature vector and generate the protocol fingerprint ID. Using the device ID as the association point, construct and store the mapping relationship between the protocol fingerprint ID, device ID, and protocol driver plugin, and generate a protocol fingerprint-driver mapping table. Upon receiving new registration information, a temporary registration fingerprint is generated. The temporary registration fingerprint is then matched with the protocol fingerprint-driver mapping table. Based on the matching result, the corresponding new registration processing mechanism is triggered to perform new registration processing.

3. The method for collaborative operation of heterogeneous devices in charging stations based on the Dianhong distributed soft bus according to claim 2, characterized in that, Upon receiving new registration information, a temporary registration fingerprint is generated. This temporary registration fingerprint is then matched against the protocol fingerprint-driver mapping table. Based on the matching result, a corresponding new registration processing mechanism is triggered to perform new registration processing. Specifically, this includes: If the temporary registration fingerprint matches the fingerprint in the protocol fingerprint-driver mapping table, then the corresponding protocol driver is called according to the protocol fingerprint-driver mapping table to parse the new registration information and obtain the collaborative events in the new registration information; If the temporary registered fingerprint does not match the fingerprint in the protocol fingerprint-driver mapping table, the communication protocol in the new registration information will be marked as an unknown protocol, and an alarm message will be generated and sent to the management terminal.

4. The method for collaborative operation of heterogeneous devices in charging stations based on the Dianhong distributed soft bus according to claim 3, characterized in that, If the temporary registered fingerprint does not match the fingerprint in the protocol fingerprint-driver mapping table, the communication protocol in the new registration information is marked as an unknown protocol, and an alarm message is generated and sent to the management terminal. This also includes: Obtain confirmation information from the management terminal regarding the unknown protocol. If the unknown protocol is confirmed to be an untrusted protocol, reject the new registration application. When the unknown protocol is confirmed to be a trusted protocol, the unknown protocol is updated in the protocol fingerprint-driver mapping table.

5. The method for collaborative operation of heterogeneous devices in charging stations based on the Dianhong distributed soft bus according to claim 1, characterized in that, Before performing computing power matching from the virtual computing power resource pool according to the task descriptor, and sending the subtask to the corresponding computing power node based on the matching result, the following steps are included: Obtain the registration information of each computing node with computing power resources, wherein the registration information includes device capabilities and a description of computing power resources; The computing power resource status of each computing power node is analyzed based on the registration information. The computing power resource status is associated with the corresponding computing power node and summarized to construct a virtual computing power resource pool for all heterogeneous devices in the charging station. The changes in computing resources of each computing node are dynamically acquired according to a preset update mechanism, and the virtual computing resource pool is dynamically updated.

6. The method for collaborative operation of heterogeneous devices in charging stations based on the Dianhong distributed soft bus according to claim 1, characterized in that, The computing node receives the corresponding subtask and, based on the corresponding task descriptor, determines whether its own computing resources meet the requirements of the matched subtask. If so, it performs local decision-making and processing on the assigned subtask, specifically including: When the computing power node's own computing power resources meet the computing power resources required by the matching subtask, the computing power node's own protocol fingerprint is invoked, and the corresponding protocol driver is loaded through the protocol fingerprint-driver mapping table to respond to the subtask. If the computing power resources of the computing power node itself do not meet the computing power resources required by the matching subtask, the idle computing power of all devices is judged and sorted through the virtual computing power resource pool, and the optimal edge computing power node is selected to perform edge processing on the subtask according to the sorting result.

7. The method for collaborative operation of heterogeneous devices in charging stations based on the Dianhong distributed soft bus according to claim 1, characterized in that, The method further includes: Determine whether the real-time load of the central controller has reached a preset load threshold. When the central controller reaches the preset load threshold, a peak shaving event is generated and broadcast to all computing power nodes in a distributed manner through the Elec-Tech soft bus. After each computing node receives the broadcast peak reduction event, it calls the local protocol driver through the protocol fingerprint-driver mapping table to parse the peak reduction event and obtain the target total power reduction. Combining the state constraints of each computing node with user constraints, calculate the maximum power adjustment value for each computing node to share the target total power reduction, and distribute the power allocation calculation task to each computing node in a distributed manner; The power allocation decision for each computing node is encoded using its own protocol fingerprint and fed back to the central controller for aggregation and confirmation.

8. A collaborative system for heterogeneous devices in a charging station based on a distributed soft bus, characterized in that, The system is applied to the charging station heterogeneous device collaboration method based on the Dianhong distributed soft bus as described in any one of claims 1-7, and the system includes: The event processing module is used to acquire real-time collaborative events of the charging station, break down the real-time collaborative events into sub-tasks, analyze the computing power requirements of each sub-task, and obtain task descriptors corresponding to multiple collaborative sub-tasks. The computing power matching module is used to match computing power from the virtual computing power resource pool according to the task descriptor, and send the subtask to the corresponding computing power node according to the matching result; The node local decision module is used for the computing node to receive the corresponding subtask and determine whether its own computing resources meet the computing resources required by the matching subtask according to the corresponding task descriptor. If they meet the requirements, the node performs local decision-making and processing on the assigned subtask. The distributed collaborative processing module is used to perform distributed collaborative processing of the subtask on all computing nodes with available computing resources if the conditions are not met, and to feed back the subtask processing results to the central controller for confirmation.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the collaborative method for heterogeneous devices in a charging station based on the Dianhong distributed soft bus as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the collaborative method for heterogeneous devices in a charging station based on the Dianhong distributed soft bus as described in any one of claims 1 to 7.

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