Route scheduling method, system and equipment based on virtual power plant business priority and medium

Abstract

The invention belongs to the field of virtual power plants, and discloses a route scheduling method, a route scheduling system, route scheduling equipment and a route scheduling medium based on the business priority of a virtual power plant, and the route scheduling method comprises the following steps: updating the current network state of a pre-constructed business flow model of the virtual power plant according to the business priority of a new access business request; and carrying out iterative optimization on the updated network state by utilizing a Q-learning reinforcement learning method until a termination condition is met, thereby outputting an optimal routing scheduling strategy. According to the dynamic adjustment mode, the limitation that a traditional routing scheduling method depends on a fixed routing algorithm and is difficult to adapt to a complex and changeable network environment is overcome, the utilization rate of network resources is effectively improved, and more efficient and reliable data transmission can be realized on the basis of guaranteeing the service security; according to the method, the service flow and the service priority of the virtual power plant are comprehensively considered, so that the problem of routing and network resource allocation tension caused by a multi-concurrent flow phenomenon in a virtual power plant network is effectively solved.

Description

Technical Field

[0001] The present invention belongs to the field of virtual power plant services, and in particular relates to a routing scheduling method, system, equipment and medium based on virtual power plant service priorities. Background Art

[0002] Virtual power plant is one of the important technologies of smart distribution network. It can efficiently aggregate the massive distributed clean energy, controllable load and energy storage system in the distribution network through advanced information communication and intelligent metering technology, and participate in the peak load regulation, frequency regulation and demand response of large power grid. How to effectively manage the data transmission route according to the importance and real-time requirements of different businesses is an important research direction for virtual power plant business scheduling.

[0003] Traditional routing scheduling methods usually rely on fixed routing algorithms to guide the transmission path of data packets in the network. Traditional routing scheduling methods are mostly based on static network topology and predefined routing strategies, with the goal of efficiently and reliably transmitting data in the network; however, in the face of complex business requirements and dynamic network conditions, the performance of traditional routing scheduling methods will drop sharply, resulting in low utilization of network resources and business security risks.

[0004] It can be seen that the existing traditional routing scheduling methods usually rely on fixed routing algorithms. Under complex business requirements and dynamic network conditions, the performance will drop sharply, resulting in low network resource utilization and inability to guarantee business security. Summary of the invention

[0005] The present invention provides a routing scheduling method, system, device and medium based on the business priority of a virtual power plant to solve the technical problem that the existing traditional routing scheduling method usually relies on a fixed routing algorithm, and the performance will drop sharply under complex business requirements and dynamic network conditions, resulting in low network resource utilization and inability to ensure business security.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a routing scheduling method based on virtual power plant service priority, comprising: Based on the service priority of the new service request received, updating the current network status of the pre-built virtual power plant service flow model; wherein the virtual power plant service flow model includes the service priority; Based on the Q-learning reinforcement learning method, the updated network state is iterated until the termination condition is met, and the optimal routing scheduling strategy is output.

[0007] The virtual power plant service flow model is constructed based on service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as follows:

[0008] in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0009] A further improvement of the present invention is that the updating of the current network state of the pre-built virtual power plant service flow model based on the service priority of the new service request received includes: When a new service request is received, the service priority of the new service request is calculated. and parameters of the virtual power plant business flow model , add the new business request to the corresponding business queue, update the current network status of the pre-built virtual power plant business flow model, and obtain the updated network status.

[0010] A further improvement of the present invention is that the Q-learning-based reinforcement learning method iterates the updated network state until the termination condition is met and outputs the optimal routing scheduling strategy, including: initialization Value Table ,all Set the value to 0; define the state space and action space; set the learning rate , Discount Factor and exploration rate ; Observe the updated network status ;in, is the current network topology; is the service queue length of each node; Action selection process: Use Greedy strategy to choose actions ; Scheduling process: Based on the selected action Carry out business scheduling and path selection to meet the requirements of the virtual power plant business flow model; State Observation Process: Action After the execution is completed, observe the next network status and the current rewards obtained ; Value update process: According to the current Value Update Formula Update value; Transfer the updated network state s to the next network state , repeat the action selection process, scheduling process, state observation process and The value update process continues until the termination condition is met and the optimal routing scheduling strategy is output; Among them, the The value update formula is as follows:

[0011] in, is the updated network status; For the current action; is the learning rate; For current rewards; Discount factor; For the next network state; For optional actions in the next network state; The maximum value for the next state value.

[0012] A further improvement of the present invention is that the current reward It is expressed as: ;in, : Represents different weight coefficients, used to adjust the priority and delay in the current reward The weight in .

[0013] A further improvement of the present invention is that the virtual power plant business flow model uses a virtual power plant data flow for interaction; wherein the virtual power plant data flow includes an acquisition flow and a control flow.

[0014] A further improvement of the present invention is that the virtual power plant business is divided into four levels for management, including real-time business, quasi-real-time business, non-real-time business and default business; the business priority of the business request corresponding to the four-level virtual power plant business is 1 to 4.

[0015] In a second aspect, the present invention provides a routing scheduling system based on virtual power plant service priority, comprising: A service request access module, used to update the current network status of a pre-built virtual power plant service flow model based on the service priority of the new service request being accessed; wherein the virtual power plant service flow model includes the service priority; The scheduling strategy output module is used to iterate the updated network status based on the Q-learning reinforcement learning method until the termination condition is met and output the optimal routing scheduling strategy; The virtual power plant service flow model is constructed based on service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as follows:

[0016] in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0017] In a third aspect, the present invention provides a device, comprising: Memory for storing computer programs; A processor is used to implement the steps of the above-mentioned routing scheduling method based on virtual power plant business priority when executing the computer program.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program is used to implement the steps of the above-mentioned routing scheduling method based on virtual power plant business priority.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention also provides a routing scheduling method based on the service priority of a virtual power plant. The method updates the current network state of the pre-built virtual power plant service flow model according to the service priority of the newly accessed service request, and uses the Q-learning reinforcement learning method to iteratively optimize the updated network state until the termination condition is met, thereby outputting the optimal routing scheduling strategy. This dynamic adjustment method overcomes the limitations of traditional routing scheduling methods that rely on fixed routing algorithms and are difficult to adapt to complex and changeable network environments, effectively improves network resource utilization, and can achieve more efficient and reliable data transmission on the basis of ensuring service security; the method effectively solves the problem of tight routing and network resource configuration caused by multiple concurrent flows in the virtual power plant network by comprehensively considering the virtual power plant service flow and service priority.

[0020] Preferably, in the present invention, the virtual power plant business flow model is defined, including business type, priority, path, bandwidth requirement and delay requirement, providing a more comprehensive information basis for routing scheduling; by defining the business flow path as a sequence of a series of nodes and connections, the flexibility of routing scheduling is increased, which can better adapt to complex network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A system architecture diagram of a virtual power plant service flow provided by an embodiment of the present invention; Figure 2 A flowchart of a routing scheduling method based on virtual power plant service priority provided by an embodiment of the present invention; Figure 3 A flowchart of a routing scheduling method based on virtual power plant service priority provided by the present invention; Figure 4 A structural schematic diagram of a routing scheduling system based on virtual power plant business priority provided by the present invention. DETAILED DESCRIPTION

[0022] Example 1 The present invention provides a routing scheduling method based on virtual power plant service priority, such as Figure 3 As shown, the following steps are included: S1: based on the service priority of the incoming new service request, updating the current network status of the pre-built virtual power plant service flow model; wherein the virtual power plant service flow model includes the service priority; S2: Iterate the updated network status based on the Q-learning reinforcement learning method until the termination condition is met and output the optimal routing scheduling strategy; The virtual power plant business flow model uses virtual power plant data flow for interaction, where the virtual power plant data flow includes acquisition flow and control flow, which are used to support scheduling, marketing business and source-grid-load-storage interaction. The virtual power plant business flow model uses virtual power plant data flow for interaction, providing strong support for the scheduling, marketing business and source-grid-load-storage interaction of virtual power plants; virtual power plant data flow includes acquisition flow and control flow, which helps to achieve more efficient data interaction and business processing.

[0023] In this method, the virtual power plant service is divided into 4 levels for management, including real-time service, quasi-real-time service, non-real-time service and default service; the service priority of the service request corresponding to the 4-level virtual power plant service is 1 to 4. In this embodiment, the virtual power plant service is divided into 4 levels for management, and the corresponding service priority is set, which helps to give priority to the transmission needs of key services when network resources are limited; through reasonable priority setting, the allocation and utilization of network resources can be optimized, and the operating efficiency and stability of the entire network system can be improved.

[0024] Specifically, the virtual power plant service flow model includes service type, service priority, service flow path, service bandwidth requirement and service delay requirement, which are specifically expressed as follows:

[0025] is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0026] In this embodiment, the virtual power plant business flow model is defined, including business types, priorities, paths, bandwidth requirements and latency requirements, providing a more comprehensive information basis for routing scheduling; by defining the business flow path as a sequence of nodes and connections, the flexibility of routing scheduling is increased, which can better adapt to complex network environments. The business flow path is the network transmission path in the virtual power plant business flow model.

[0027] Here, based on the service priority of the incoming new service request, the current network status of the pre-built virtual power plant service flow model is updated, including: When a new service request is received, the service priority of the new service request is calculated. and parameters of the virtual power plant business flow model , add the new business request to the corresponding business queue, update the current network status of the pre-built virtual power plant business flow model, and obtain the updated network status.

[0028] Specifically, the updated network state is iterated based on the Q-learning reinforcement learning method until the termination condition is met, and the optimal routing scheduling strategy is output, including: initialization Value Table ,all Set the value to 0; define the state space and action space; set the learning rate , Discount Factor and exploration rate ; Observe the updated network status ;in, is the current network topology; is the service queue length of each node; Action selection process: Use Greedy strategy to choose actions ; Scheduling process: Based on the selected action Carry out business scheduling and path selection to meet the requirements of the virtual power plant business flow model; State Observation Process: Action After the execution is completed, observe the next network status and the current rewards obtained ; Value update process: According to the current Value Update Formula Update value; Transfer the updated network state s to the next network state , repeat the action selection process, scheduling process, state observation process and The value update process continues until the termination condition is met and the optimal routing scheduling strategy is output; Among them, the The value update formula is as follows:

[0029] in, is the updated network status; For the current action; is the learning rate; For current rewards; Discount factor; For the next network state; For optional actions in the next network state; The maximum value for the next state value.

[0030] Here, the updated network status It is expressed as: ; Current Rewards It is expressed as: ;in, : Represents different weight coefficients, which are used to adjust the weights of priority and delay in the current reward.

[0031] In this embodiment, by initializing the Q value table and learning parameters, a good starting point is provided for the operation of the Q-learning algorithm; the ε-greedy greedy strategy is adopted to select actions to achieve intelligent service scheduling and path selection, thereby improving the intelligence level of network scheduling; by continuously iteratively updating the Q value, the optimal routing scheduling strategy is gradually approached to improve network performance and resource utilization. At the same time, the representation of the updated network status contains rich network information, which helps to more accurately evaluate the network status and formulate routing scheduling strategies; by representing flexible current rewards, the weights of factors such as priority and delay in the rewards can be adjusted according to actual needs, thereby achieving more refined scheduling control.

[0032] like Figure 4 As shown, the present invention also provides a routing scheduling system based on the service priority of a virtual power plant, including: a service request access module, which is used to update the current network state of a pre-built virtual power plant service flow model based on the service priority of the new service request accessed; wherein the virtual power plant service flow model includes service priority; a scheduling strategy output module, which is used to iterate the updated network state based on the Q-learning reinforcement learning method until the termination condition is met, and output the optimal routing scheduling strategy; the virtual power plant service flow model is constructed based on service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as follows:

[0033] in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0034] The present invention also provides a device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the routing scheduling method based on virtual power plant business priority when executing the computer program.

[0035] When the processor executes the computer program, the above-mentioned routing scheduling steps based on the virtual power plant service priority are implemented, for example: based on the service priority of the new service request being accessed, the current network state of the pre-built virtual power plant service flow model is updated; wherein the virtual power plant service flow model includes the service priority; based on the Q-learning reinforcement learning method, the updated network state is iterated until the termination condition is met, and the optimal routing scheduling strategy is output; the virtual power plant service flow model is constructed based on the service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as follows:

[0036] in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0037] Alternatively, the processor implements the functions of each module in the above system when executing the computer program, for example: a service request access module, which is used to update the current network state of a pre-built virtual power plant service flow model based on the service priority of the new service request being accessed; wherein the virtual power plant service flow model includes service priority; a scheduling strategy output module, which is used to iterate the updated network state based on the Q-learning reinforcement learning method until the termination condition is met, and output the optimal routing scheduling strategy; the virtual power plant service flow model is constructed based on service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as follows:

[0038] in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0039] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete preset functions, and the instruction segments are used to describe the execution process of the computer program in the routing scheduling device based on the virtual power plant service priority. For example, the computer program can be divided into a service request access module and a scheduling strategy output module; the specific functions of each module are as follows: a service request access module, which is used to update the current network status of the pre-built virtual power plant service flow model based on the service priority of the new service request being accessed; wherein the virtual power plant service flow model includes service priority; a scheduling strategy output module, which is used to iterate the updated network status based on the Q-learning reinforcement learning method until the termination condition is met, and output the optimal routing scheduling strategy; the virtual power plant service flow model is constructed based on service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as:

[0040] in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0041] The routing and scheduling device based on the business priority of the virtual power plant can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The routing and scheduling device based on the business priority of the virtual power plant may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is an example of a routing and scheduling device based on the business priority of a virtual power plant, and does not constitute a limitation on the routing and scheduling device based on the business priority of a virtual power plant, and may include more components than the above, or a combination of certain components, or different components. For example, the routing and scheduling device based on the business priority of the virtual power plant may also include input and output devices, network access devices, buses, etc.

[0042] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the routing scheduling based on the business priority of the virtual power plant, and uses various interfaces and lines to connect the various parts of the routing scheduling device based on the business priority of the virtual power plant.

[0043] The memory can be used to store the computer program and / or module, and the processor implements the various functions of the routing and scheduling device based on the virtual power plant business priority by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0044] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0045] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the routing scheduling method based on the business priority of a virtual power plant.

[0046] If the module / unit integrated in the routing and scheduling system based on virtual power plant business priority is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0047] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned routing scheduling method based on the business priority of the virtual power plant, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of the above-mentioned routing scheduling method based on the business priority of the virtual power plant can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0048] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0049] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0050] The present invention will be further described below in conjunction with the embodiments and drawings: Example 2 As described in the background technology, traditional routing scheduling methods usually rely on fixed routing algorithms to guide the transmission path of data packets in the network. Most traditional routing scheduling methods are based on static network topology and predefined routing strategies, with the goal of efficiently and reliably transmitting data in the network; however, in the face of complex business requirements and dynamic network conditions, the performance of traditional routing scheduling methods will drop sharply, resulting in low network resource utilization and business security risks.

[0051] In order to solve the above problems, the present invention provides a routing scheduling method based on the business priority of a virtual power plant. This method relies on a reinforcement learning optimization algorithm to realize the priority scheduling of virtual power plant business routes, improve the allocation efficiency of network resources and data transmission performance, and support the implementation and efficient operation of virtual power plants.

[0052] This embodiment provides a routing scheduling method based on virtual power plant service priority, which is oriented to virtual power plant services and applied to a virtual power plant service flow model. The construction of the virtual power plant service flow model is as follows: like Figure 1 As shown, Figure 1It is the system architecture of the virtual power plant business flow, including the virtual power plant business master station, the virtual power plant business terminal aggregation device and the virtual power plant distributed resources; among them, the virtual power plant business aggregation node is connected to the distributed resources of the virtual power plant through a wired or wireless network, such as distributed photovoltaic, distributed energy storage, flexible adjustable load, etc., to achieve equipment access; at the same time, the virtual power plant business aggregation node is connected to the virtual power plant business master station through the network infrastructure to support the virtual power plant business.

[0053] The data flow of the virtual power plant includes the collection flow and the control flow, which are used to support scheduling, marketing business, source grid load storage interaction, etc. The collection flow and the control flow have both periodic collection and non-periodic execution business characteristics, and usually adopt 104 protocol and DL / T1867-2018. In this embodiment, based on the system architecture of the virtual power plant business flow, a virtual power plant business flow model is constructed, which is specifically expressed as follows:

[0054] in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

[0055] In this embodiment, in order to improve the allocation efficiency of network resources and data transmission performance, the service priorities are defined as follows: This embodiment divides the virtual power plant services into four levels for management. Among them, the highest service priority is real-time service, which is level 1 service; the second is quasi-real-time service, which is level 2 service; non-real-time service is level 3 service; the default service has the lowest priority, which is level 4 service, and the service priorities of the corresponding service requests are 1 to 4. When scheduling various services of the power communication network, it is necessary to consider the service priority of the newly added service request, and first schedule the service with the highest service priority to ensure the safe and stable operation of the power grid, and then schedule the service with low service priority, as shown in Table 1: Table 1 shows the business priority classification

[0056] QoS (Quality of Service) is a network term used to describe the level of service quality provided by the network to specific services or users when transmitting data packets through the network. The goal of QoS is to provide different priorities and service qualities for different network traffic to meet the needs of different users.

[0057] This embodiment provides a routing scheduling method based on the service priority of a virtual power plant. This method is based on the Q-learning reinforcement learning algorithm, and is specifically described as follows: The Q-learning reinforcement learning algorithm is a reinforcement learning algorithm based on value iteration. It selects the optimal action by learning the value (Q value) of the state-action. The present invention dispatches various services in the power communication network based on service priority and service flow to optimize the allocation and use of network resources. Defined as network status Next Select Behavior And follows the estimate of the optimal policy, which is updated by using the immediate reward and the discounted reward.

[0058] The update of Q value is as follows:

[0059] in, : Current network status; : current action; : learning rate; : Current reward; : discount factor; : next state; For optional actions in the next network state; : The maximum value of the next state value.

[0060] Updated network status express: . : Current network topology; : The service queue length of each node; : The priority of each service. The smaller the value, the higher the priority (level 1 is the highest and level 4 is the lowest).

[0061] Current Rewards express: . Indicates different weight coefficients, used to adjust the priority and delay in the current reward The weight in .

[0062] like Figure 2 As shown, this embodiment provides a routing scheduling method based on virtual power plant service priority, and the specific steps are as follows: Step 1: Initialization Value Table ,all Set the value to 0. Define the state space and action space. Set the learning rate , Discount Factor and exploration rate .

[0063] Step 2: When a new service request is received, the service priority is determined. and parameters of the traffic model , add the new service request to the corresponding service queue .

[0064] Step 3: Observe the updated network status .

[0065] Step 4: Use Greedy strategy to choose actions . Set a smaller Value, use The probability of greedily choosing the best action, and using The probability of randomly choosing from all possible Select one of the available actions.

[0066] Step 5: According to the selected action Carry out business scheduling and path selection to meet the requirements of the virtual power plant business flow model.

[0067] Step 6: After executing the action, observe the next network status and the current rewards obtained .

[0068] Step 7: According to Value Update Formula Update value.

[0069] Step 8: Transfer the updated network state s to the next network state Repeat steps 4 to 7 until the termination condition is met. The termination condition here can be the number of iterations or The value converges; when the termination condition is reached, the optimal routing scheduling strategy combination is finally output.

[0070] It should be noted that the order of step 1 and step 2 can be swapped or performed simultaneously.

[0071] It can be seen that this method comprehensively considers the business traffic and business priority of the virtual power plant, solves the problem of multiple concurrent flows in the virtual power plant network, and thus causes tight routing and network resource configuration; at the same time, this method proposes a Q-learning routing and scheduling scheme based on traffic and obtains basic network information, uses an improved Q-learning algorithm to find paths, and improves the performance of the entire virtual power plant network.

[0072] The present invention provides a routing scheduling method based on virtual power plant service priority, which has the following advantages over traditional routing scheduling methods: This method provides a highly adaptive and intelligent routing scheduling method for virtual power plant business representation. This method updates the network status in real time to respond to new business requests, and uses the Q-learning reinforcement learning algorithm to iteratively optimize the routing strategy, so as to achieve efficient and reliable routing scheduling under the conditions of limited and dynamically changing network resources. The pre-built virtual power plant business flow model contains detailed information such as business priority, type, path, bandwidth requirement and delay requirement, which provides precise guidance for routing scheduling. This method effectively improves the utilization of network resources and reduces the risk of business transmission by intelligently allocating network resources to higher priority businesses and meeting their specific bandwidth and delay requirements. In addition, this method also ensures the continuous optimization and convergence of the routing scheduling strategy through the finely represented current reward and Q value update formula to adapt to the ever-changing network environment and business needs. In summary, this method not only improves the transmission efficiency and quality of virtual power plant business, but also enhances the stability and security of the entire network system.

[0073] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention.

Claims

1. A routing scheduling method based on virtual power plant service priority, characterized in that: include: Based on the service priority of the new service request received, updating the current network status of the pre-built virtual power plant service flow model; wherein the virtual power plant service flow model includes the service priority; Based on the Q-learning reinforcement learning method, the updated network status is iterated until the termination condition is met, and the optimal routing scheduling strategy is output; The virtual power plant service flow model is constructed based on service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as follows: in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

2. The routing scheduling method based on virtual power plant service priority according to claim 1 is characterized in that: The updating of the current network status of the pre-built virtual power plant service flow model based on the service priority of the new service request received includes: When a new service request is received, the service priority of the new service request is calculated. and parameters of the virtual power plant business flow model , add the new business request to the corresponding business queue, update the current network status of the pre-built virtual power plant business flow model, and obtain the updated network status.

3. The routing scheduling method based on virtual power plant service priority according to claim 2 is characterized in that: The Q-learning-based reinforcement learning method iterates the updated network state until the termination condition is met and outputs the optimal routing scheduling strategy, including: initialization Value Table ,all Set the value to 0; define the state space and action space; set the learning rate , Discount Factor and exploration rate ; Observe the updated network status ;in, is the current network topology; is the service queue length of each node; Action selection process: Use Greedy strategy to choose actions ; Scheduling process: Based on the selected action Carry out business scheduling and path selection to meet the requirements of the virtual power plant business flow model; State Observation Process: Action After the execution is completed, observe the next network status and the current rewards obtained ; Value update process: According to the current Value Update Formula Update value; Transfer the updated network state s to the next network state , repeat the action selection process, scheduling process, state observation process and The value update process continues until the termination condition is met and the optimal routing scheduling strategy is output; Among them, the The value update formula is as follows: in, is the updated network status; For the current action; is the learning rate; For current rewards; Discount factor; For the next network state; For optional actions in the next network state; The maximum value for the next state value.

4. The routing scheduling method based on virtual power plant service priority according to claim 3 is characterized in that: Current Rewards It is expressed as: ;in, : Represents different weight coefficients, used to adjust the priority and delay in the current reward The weight in .

5. The routing scheduling method based on virtual power plant service priority according to claim 1 is characterized in that: The virtual power plant business flow model uses a virtual power plant data flow for interaction; wherein the virtual power plant data flow includes an acquisition flow and a control flow.

6. The routing scheduling method based on virtual power plant service priority according to claim 1 is characterized in that: Virtual power plant services are managed at four levels, including real-time services, quasi-real-time services, non-real-time services and default services; the service priorities of service requests corresponding to the four-level virtual power plant services are 1 to 4.

7. A routing scheduling system based on virtual power plant service priority, characterized in that: include: A service request access module, used to update the current network status of a pre-built virtual power plant service flow model based on the service priority of the new service request being accessed; wherein the virtual power plant service flow model includes the service priority; The scheduling strategy output module is used to iterate the updated network status based on the Q-learning reinforcement learning method until the termination condition is met and output the optimal routing scheduling strategy; The virtual power plant service flow model is constructed based on service type, service priority, service flow path, service bandwidth requirement and service delay requirement, and is specifically expressed as follows: in, is the type of business; For business priorities; To meet business bandwidth requirements; Service latency requirements; is the business flow path, represented as a sequence of nodes and connections: ;in; represents a node in the network, 1≤i≤k; and k≥2.

8. A device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the routing scheduling method based on virtual power plant business priority as described in any one of claims 1-6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the routing scheduling method based on virtual power plant business priority as described in any one of claims 1-6.

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