Power distribution system edge-cloud uplink delay optimization method adaptive to service random access

Abstract

The application relates to the technical field of electric power, and particularly discloses a power distribution system edge-cloud uplink time delay optimization method suitable for service random access, which comprises the following steps: S1, establishing a to-be-transmitted queue; S2, establishing a compression ratio constraint based on the to-be-transmitted queue and a compression strategy executed on different data packets based on the compression ratio; S3, calculating expected compression time delays of the data packets in the to-be-transmitted queue; S4, calculating corresponding transmission time delays based on the compression strategy executed on the different data packets and uplink bandwidth constraints of a power distribution edge gateway; S5, calculating an expected uplink time delay of the to-be-transmitted queue; S6, constructing an adaptive compression optimization problem with the minimum expected uplink time delay of the to-be-transmitted queue as a target; and S7, solving the adaptive compression optimization problem, executing adaptive compression operations on the to-be-transmitted queue according to an obtained optimization solution, and optimizing the power distribution system edge-cloud uplink time delay.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically to a method for optimizing the uplink latency of a power distribution system to the cloud for random access to services. Background Technology

[0002] With the construction of new power systems, the distribution Internet of Things (IoT) business is experiencing explosive growth. Services such as electricity consumption information collection and distributed energy monitoring exhibit significant periodicity and random access characteristics.

[0003] In existing technologies, edge gateways typically employ fixed transmission strategies (such as full compression or no compression). However, in bandwidth-constrained edge-cloud uplinks, this static strategy suffers from the following problems: Resource mismatch: For small data packets, the computational delay introduced by compression may be greater than the transmission delay saved by compression, resulting in an increase in overall latency.

[0004] Increased congestion: Random access to services causes queue length fluctuations, and fixed strategies cannot be dynamically adjusted to adapt to network congestion.

[0005] Therefore, a method is needed that can adaptively adjust the compression strategy based on queue status and computing resources to minimize uplink transmission latency. Summary of the Invention

[0006] To address the aforementioned issues, the present invention aims to provide a method for optimizing uplink latency in a power distribution system with random service access, which can adaptively adjust the compression strategy based on queue status and computing resources to minimize uplink transmission latency.

[0007] This invention provides a method for optimizing edge-cloud uplink latency in a power distribution system to adapt to random service access, characterized by comprising: Step S1: Collect all data packets to be uploaded at the beginning of each uplink cycle through the power distribution edge gateway, sort the data packets according to the first-in-first-out principle and establish a queue to be transmitted; Step S2: Establish compression ratio constraints based on the queue to be transmitted and compression strategies for different data packets based on the compression ratio; Step S3: Calculate the expected compression delay of each data packet in the queue to be transmitted based on the computing power, compression complexity and size of each data packet in the queue to be transmitted. Step S4: Calculate the corresponding transmission delay based on the compression strategy executed for different data packets and the uplink bandwidth constraint of the power distribution edge gateway; Step S5: Calculate the expected uplink delay of the last data packet in the queue to be transmitted, and use the expected uplink delay of the last data packet as the expected uplink delay of the queue to be transmitted. Step S6: Construct an adaptive compression optimization problem with the goal of minimizing the expected uplink latency of the queue to be transmitted. Step S7: Solve the adaptive compression optimization problem, and perform adaptive compression operation on the transmission queue based on the obtained optimization solution to optimize the uplink latency between the power distribution system and the cloud.

[0008] In one possible implementation, step S1 includes: The queue to be transmitted can be represented by the following formula: in, Indicates the first The queue of data to be transmitted for one uplink cycle. Indicates the first The size of each data packet Indicates the first The size of each data packet This indicates the number of data packets contained in the queue. In one possible implementation, step S2 includes: The compression ratio constraint is expressed by the following formula: in, Indicates the first Compression ratio per compression cycle.

[0009] In one possible implementation, step S2 further includes: Compress different data packets according to the following formula: in, Indicates based on compression ratio For the first The compression strategy applied to each data packet. Indicates the number of elements in the queue. Each data packet will undergo compression. This indicates that the action will not be taken.

[0010] In one possible implementation, step S3 includes: The expected compression latency of each data packet is calculated using the following formula: in, Indicates the number of bytes in the queue to be transmitted. The expected compression latency of each data packet, This represents the number of CPU cycles required to compress 1 bit of data, i.e., the compression complexity. This indicates the computing power allocated by the edge gateway for compressed services. Indicates based on compression ratio Regarding the first Compression strategy for each data packet, Indicates the first The size of each data packet.

[0011] In one possible implementation, step S4 includes: Calculate the transmission delay of a specified data packet in the queue to be transmitted using the following formula: in, Indicates the number of bytes in the queue to be transmitted. The transmission delay of each data packet, This indicates the ratio of the amount of data that can be reduced by the compression algorithm to the amount of original data. This indicates the uplink bandwidth of the power distribution edge gateway. Indicates based on compression ratio Regarding the first Compression strategy for each data packet, Indicates the first The size of each data packet.

[0012] In one possible implementation, step S5 includes: Calculate the expected uplink latency of the queue to be transmitted using the following formula: in, This indicates the expected uplink latency of the queue to be transmitted, which is the time it takes for the last data packet in the queue to be transmitted. This indicates the number of data packets contained in the queue. Indicates the first The compression latency of each data packet, Indicates the first The transmission delay of each data packet, Indicates the first The compression latency of each data packet, Show the first The transmission delay of each data packet.

[0013] In one possible implementation, step S6 includes: The adaptive compression optimization problem can be expressed by the following formula: in, This indicates a compression ratio constraint. This indicates compression instruction constraints. This represents the expected uplink latency of the queue of data to be transmitted. Indicates the first Compression ratio per compression cycle Indicates the first The queue of data to be transmitted for one uplink cycle. Indicates based on compression ratio Regarding the first Compression strategy for each data packet.

[0014] In one possible implementation, step S6 further includes: The adaptive compression optimization problem is solved using deep reinforcement learning.

[0015] In one possible implementation, step S7 includes: The optimal solution is expressed by the following formula: in, This indicates that the optimal solution is applied to the first... Data packets The compression command executed. This indicates the number of data packets contained in the queue.

[0016] The edge-cloud uplink latency optimization method for power distribution systems adapted to random service access provided by this invention can dynamically adjust the compression ratio according to the number and size of data packets in each cycle to adapt to the random access characteristics of services; by balancing the compression computation overhead and transmission benefits, it avoids "ineffective compression" and minimizes the total queue latency; while ensuring real-time performance, it makes full use of the computing resources on the edge side and the bandwidth resources in the cloud. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the edge-cloud uplink latency optimization method for a power distribution system adapted to random service access, provided as an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of the present invention by way of example, but should not be used to limit the scope of the present invention. That is, the present invention is not limited to the described preferred embodiments, and the scope of the present invention is defined by the claims.

[0019] In the description of this invention, it should be noted that, unless otherwise stated, "a plurality of" means two or more; the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance; those skilled in the art can understand the specific meaning of the above terms in this invention as appropriate.

[0020] Figure 1A flowchart illustrating the edge-cloud uplink latency optimization method for power distribution systems adapted to random service access, as provided in an embodiment of the present invention, is shown below. Figure 1 As shown, this invention provides a method for optimizing edge-cloud uplink latency in a power distribution system to adapt to random service access, characterized by comprising: Step S1: Collect all data packets to be uploaded at the beginning of each uplink cycle through the power distribution edge gateway, sort the data packets according to the first-in-first-out principle and establish a queue to be transmitted. This step is the data acquisition and preprocessing stage. The distribution edge gateway, acting as a hub connecting the underlying power equipment and the cloud platform, is responsible for aggregating data such as electricity consumption information. Due to the unique nature of power services, data often arises suddenly within the same timeframe (random access), leading to data accumulation at the gateway. Adopting a first-in, first-out (FIFO) principle ensures the timeliness and fairness of data transmission, preventing the loss of critical data due to priority confusion.

[0021] In one possible implementation, the queue to be transmitted can be represented by the following formula: in, Indicates the first Due to the random access characteristics of services, the number of data packets in the uplink transmission queue fluctuates each week. The transmission order of the data packets is positively correlated with their corresponding numbers. First transmitted; subscript This method is used to distinguish different statistical periods or upload windows, reflecting its periodic optimization characteristics. Indicates the first The size of each data packet Indicates the first The size of each data packet This indicates the number of data packets contained in the queue. Step S2: Establish compression ratio constraints based on the queue to be transmitted and compression strategies for different data packets based on the compression ratio; In one possible implementation, the compression ratio constraint is expressed by the following formula: in, Indicates the first Compression ratio per compression cycle. This indicates the ratio of the number of data packets in the queue that need to undergo lossless compression to the number of data packets to be transmitted within that period, thereby determining the compression strategy for different data packets. In one possible implementation, step S2 further includes: Compress different data packets according to the following formula: in, Indicates based on compression ratio For the first The compression strategy applied to each data packet. Indicates the number of elements in the queue. Each data packet will undergo compression. This indicates that the action will not be taken.

[0022] Step S3: Calculate the expected compression delay of each data packet in the queue to be transmitted based on the computing power, compression complexity and size of each data packet in the queue to be transmitted. This step quantifies the time cost of the "compression" action. Compression is a process that uses edge computing power to gain bandwidth, which inevitably consumes time. This time depends on the size of the data and the computing power of the current device.

[0023] In one possible implementation, the expected compression latency of each data packet is calculated according to the following formula: in, Indicates the number of bytes in the queue to be transmitted. The expected compression latency of each data packet, This represents the number of CPU cycles required to compress 1 bit of data, i.e., the compression complexity. This indicates the computing power allocated by the edge gateway for compressed services. Indicates based on compression ratio Regarding the first Compression strategy for each data packet, Indicates the first The size of each data packet.

[0024] Step S4: Calculate the corresponding transmission delay based on the compression strategy executed for different data packets and the uplink bandwidth constraint of the power distribution edge gateway; This step quantifies the time cost of "transmission". Compressed data is smaller and transmits faster; uncompressed data is larger and occupies more link time.

[0025] In one possible implementation, the transmission delay of a specified data packet in the queue to be transmitted is calculated according to the following formula: in, Indicates the number of bytes in the queue to be transmitted. The transmission delay of each data packet, This indicates the ratio of the amount of data that can be reduced by the compression algorithm to the amount of original data. This indicates the uplink bandwidth of the power distribution edge gateway. Indicates based on compression ratio Regarding the first Compression strategy for each data packet, Indicates the first The size of each data packet.

[0026] Step S5: Calculate the expected uplink delay of the last data packet in the queue to be transmitted, and use the expected uplink delay of the last data packet as the expected uplink delay of the queue to be transmitted. In queuing systems, the performance of a queue is usually measured not by the first packet, but by when the last packet leaves the system (i.e., tail-end delay). Only when the last packet has been transmitted is it considered that the task for this cycle is complete.

[0027] In one possible implementation, the expected uplink latency of the queue to be transmitted is calculated according to the following formula: in, This indicates the expected uplink latency of the queue to be transmitted, which is the time it takes for the last data packet in the queue to be transmitted. This indicates the number of data packets contained in the queue. Indicates the first The compression latency of each data packet, Indicates the first The transmission delay of each data packet, Indicates the first The compression latency of each data packet, Show the first The transmission delay of each data packet. The summation symbol in the first half. This represents the queuing effect—packets at the back of the queue must wait for the packets in front to be processed before they can begin, which is the main cause of accumulated delays.

[0028] Step S6: Construct an adaptive compression optimization problem with the goal of minimizing the expected uplink latency of the queue to be transmitted. This step abstracts the above logic into a mathematical problem. The goal is to find the optimal compression ratio that minimizes the total latency.

[0029] In one possible implementation, the adaptive compression optimization problem can be expressed by the following formula: in, This indicates a compression ratio constraint. This indicates compression instruction constraints. This represents the expected uplink latency of the queue of data to be transmitted. Indicates the first Compression ratio per compression cycle Indicates the first The queue of data to be transmitted for one uplink cycle. Indicates based on compression ratio Regarding the first The compression strategy for each data packet is defined. Constraints ensure that the compression ratio is within a reasonable range, and the decision for each data packet can only be "compress" or "not compress".

[0030] One possible implementation uses deep reinforcement learning to solve the adaptive compression optimization problem. Since the number of data packets is limited and the compression decision is phased, deep reinforcement learning can effectively avoid exhaustive search, quickly find the globally optimal solution, and meet the real-time requirements of the power grid.

[0031] Step S7: Solve the adaptive compression optimization problem, and perform adaptive compression operation on the transmission queue based on the obtained optimization solution to optimize the uplink latency between the power distribution system and the cloud.

[0032] This step is the execution phase. The calculated optimal strategy is sent to the hardware for execution, completing the closed-loop control.

[0033] Since the number of data packets is limited and the compression decision is phased, deep reinforcement learning can effectively avoid exhaustive search, quickly find the global optimal solution, and meet the real-time requirements of the power grid.

[0034] In one possible implementation, the optimal solution is expressed by the following formula: in, This indicates that the optimal solution is applied to the first... Data packets The compression command executed. Indicates the number of packets contained in this queue. Marked with an asterisk. This indicates that this is the optimal value calculated by the optimization algorithm. Based on this instruction table, the edge gateway selectively compresses the data streams in the queue before uploading.

[0035] The edge-cloud uplink latency optimization method for power distribution systems adapted to random service access provided by this invention can dynamically adjust the compression ratio according to the number and size of data packets in each cycle to adapt to the random access characteristics of services; by balancing the compression computation overhead and transmission benefits, it avoids "ineffective compression" and minimizes the total queue latency; while ensuring real-time performance, it makes full use of the computing resources on the edge side and the bandwidth resources in the cloud.

[0036] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0037] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the invention. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0038] The methods and apparatus provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0039] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "a specific embodiment" or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access, characterized in that, include: Step S1: Collect all data packets to be uploaded at the beginning of each uplink cycle through the power distribution edge gateway, sort the data packets according to the first-in-first-out principle and establish a queue to be transmitted; Step S2: Establish compression ratio constraints based on the queue to be transmitted and compression strategies for different data packets based on the compression ratio; Step S3: Calculate the expected compression delay of each data packet in the queue to be transmitted based on the computing power, compression complexity and size of each data packet in the queue to be transmitted. Step S4: Calculate the corresponding transmission delay based on the compression strategy executed for different data packets and the uplink bandwidth constraint of the power distribution edge gateway; Step S5: Calculate the expected uplink delay of the last data packet in the queue to be transmitted, and use the expected uplink delay of the last data packet as the expected uplink delay of the queue to be transmitted. Step S6: Construct an adaptive compression optimization problem with the goal of minimizing the expected uplink latency of the queue to be transmitted. Step S7: Solve the adaptive compression optimization problem, and perform adaptive compression operation on the transmission queue based on the obtained optimization solution to optimize the uplink latency between the power distribution system and the cloud.

2. The method for optimizing the uplink latency of a power distribution system with random service access according to claim 1, characterized in that, Step S1 includes: The queue to be transmitted can be represented by the following formula: ； in, Indicates the first The queue of data to be transmitted for one uplink cycle. Indicates the first The size of each data packet Indicates the first The size of each data packet This indicates the number of data packets contained in the queue.

3. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access as described in claim 1, characterized in that, Step S2 includes: The compression ratio constraint is expressed by the following formula: ； in, Indicates the first Compression ratio per compression cycle.

4. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access as described in claim 1, characterized in that, Step S2 further includes: Compress different data packets according to the following formula: ； in, Indicates based on compression ratio For the first The compression strategy applied to each data packet. Indicates the number of elements in the queue. Each data packet will undergo compression. This indicates that the action will not be taken.

5. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access as described in claim 1, characterized in that, Step S3 includes: The expected compression latency of each data packet is calculated using the following formula: ； in, Indicates the number of bytes in the queue to be transmitted. The expected compression latency of each data packet, This represents the number of CPU cycles required to compress 1 bit of data, i.e., the compression complexity. This indicates the computing power allocated by the edge gateway for compressed services. Indicates based on compression ratio Regarding the first Compression strategy for each data packet, Indicates the first The size of each data packet.

6. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access as described in claim 1, characterized in that, Step S4 includes: Calculate the transmission delay of a specified data packet in the queue to be transmitted using the following formula: ； in, Indicates the number of bytes in the queue to be transmitted. The transmission delay of each data packet, This indicates the ratio of the amount of data that can be reduced by the compression algorithm to the amount of original data. This indicates the uplink bandwidth of the power distribution edge gateway. Indicates based on compression ratio Regarding the first Compression strategy for each data packet, Indicates the first The size of each data packet.

7. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access as described in claim 1, characterized in that, Step S5 includes: Calculate the expected uplink latency of the queue to be transmitted using the following formula: ； in, This indicates the expected uplink latency of the queue to be transmitted, which is the time it takes for the last data packet in the queue to be transmitted. This indicates the number of data packets contained in the queue. Indicates the first The compression latency of each data packet, Indicates the first The transmission delay of each data packet, Indicates the first The compression latency of each data packet, Show the first The transmission delay of each data packet.

8. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access as described in claim 1, characterized in that, Step S6 includes: The adaptive compression optimization problem can be expressed by the following formula: ； in, This indicates a compression ratio constraint. This indicates compression instruction constraints. This represents the expected uplink latency of the queue of data to be transmitted. Indicates the first Compression ratio per compression cycle Indicates the first The queue of data to be transmitted for one uplink cycle. Indicates based on compression ratio Regarding the first Compression strategy for each data packet.

9. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access according to claim 1, characterized in that, Step S6 further includes: The adaptive compression optimization problem is solved using deep reinforcement learning.

10. The method for optimizing edge-cloud uplink latency in a power distribution system adapted to random service access according to claim 1, characterized in that, Step S7 includes: The optimal solution is expressed by the following formula: ； in, This indicates that the optimal solution is applied to the first... Data packets The compression command executed. This indicates the number of data packets contained in the queue.

Images