Computing task allocation method and system based on cloud platform

Abstract

The invention relates to the technical field of cloud computing, and discloses a computing task allocation method and system based on a cloud platform, and the method comprises the steps: carrying out the optimization through taking the minimization of the total delay of task computing as an optimization target, and obtaining a computing node with the minimum total delay of a computing task; and the optimal path reaching the optimal calculation node is determined in the directed graph with the edge weight, so that the total delay of the calculation task is further reduced, rapid dynamic response to calculation task scheduling is realized, the task scheduling efficiency of the calculation task is improved, and the stability and reliability of system operation are ensured.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a computing task allocation method and system based on a cloud platform. Background Art

[0002] With the continuous growth of energy demand and the rapid development of power technology, the scale of the power system is expanding at an unprecedented rate, covering an increasingly wide area, and the types and number of equipment involved are increasing dramatically. This trend has accelerated the transformation of the power grid towards intelligence and automation. Among them, intelligent collection devices, as the core of the perception layer, are spread throughout all links of power generation, transmission, transformation, distribution and consumption. They can collect various types of data in the power system in real time and accurately, such as voltage, current, power factor and other key parameters. At the same time, the data processing device, as the brain of intelligent analysis, is responsible for converting massive raw data into valuable information, providing decision support for operation monitoring, fault warning, and optimized scheduling of the power system.

[0003] However, with the large-scale deployment of intelligent data collection devices and data processing devices, and the continuous expansion of data transmission lines, the amount of data information generated in the power grid has shown an explosive growth trend, and the traditional data processing model can no longer meet the needs of high efficiency, real-time and accuracy. In this context, cloud computing technology, with its powerful computing power, flexible resource allocation and high scalability, has gradually become a key way to solve the huge data processing problems of the power system.

[0004] It is not easy to effectively apply cloud computing technology to power systems. First, the servers in power systems are widely distributed, not only covering every corner of different geographical areas, but also across different geographical areas and time zones. Second, the operating characteristics of power systems determine that the demand for computing resources is highly dynamic and uncertain. For example, during peak hours, the demand for computing resources will rise sharply, while during low hours, it may drop sharply. However, the current cloud computing scheduling algorithm is difficult to achieve fast and dynamic response to computing task scheduling, and the task scheduling efficiency is poor, making it difficult to ensure the stability and reliability of system operation. Summary of the invention

[0005] In view of this, the present invention provides a computing task allocation method and system based on a cloud platform, which solves the technical problems that the current cloud computing scheduling algorithm is difficult to achieve rapid and dynamic response to computing task scheduling, the task scheduling efficiency is poor, and it is difficult to ensure the stability and reliability of system operation.

[0006] A first aspect of the present invention provides a computing task allocation method based on a cloud platform, comprising:

[0007] According to all computing nodes in the cloud platform and the network connection relationship between computing nodes, a directed graph with edge weights is constructed;

[0008] Taking minimizing the total delay of task calculation as the optimization goal, a computing node selection optimization model is constructed;

[0009] Selecting an optimization model for the computing node to perform optimal solution, and determining an optimal computing node according to the optimal solution;

[0010] Traversing all computing nodes of the directed graph and updating the weights of the paths to obtain an optimal path to the optimal computing node;

[0011] The computing task is allocated to the optimal computing node through the optimal path, and the computing task is executed.

[0012] Preferably, the edge weight is the transmission delay between every two computing nodes.

[0013] Preferably, the step of constructing a computing node selection optimization model with minimization of the total delay of task calculation as the optimization goal includes:

[0014] Determining the computing time of the computing task according to the computing capacity of the computing node and the computing requirement of the computing task;

[0015] Determine the total delay of the computing task according to the computing time of the computing task and the transmission delay from the source node to the target computing node;

[0016] Taking minimizing the total delay of the computing task as the optimization goal and taking the computing time corresponding to the urgency of the computing task as the upper limit constraint of the total delay, a computing node selection optimization model is constructed.

[0017] Preferably, before the step of constructing a directed graph with edge weights according to all computing nodes in the cloud platform and the network connection relationship between the computing nodes, the step further includes:

[0018] Upload the power data collected in real time by each node of the power grid system to the edge computing node, and pre-process the power data through the edge computing node;

[0019] Based on the fast retransmission algorithm, the preprocessed power data is uploaded to the cloud platform through a multi-level cache strategy to generate computing tasks.

[0020] Preferably, the preprocessing includes data denoising and data compression.

[0021] Preferably, the step of uploading the pre-processed power data to the cloud platform through a multi-level cache strategy based on a fast retransmission algorithm to generate a computing task includes:

[0022] Based on a fast retransmission algorithm, the preprocessed power data is uploaded from the edge computing node to a regional cache node for caching for a preset time period;

[0023] Based on a fast retransmission algorithm, the power data cached by the regional cache node is uploaded to the cloud platform for caching, and a computing task is generated according to the power data.

[0024] In a second aspect, the present invention further provides a computing task allocation system based on a cloud platform, comprising:

[0025] A directed graph construction module is used to construct a directed graph with edge weights based on all computing nodes in the cloud platform and the network connection relationship between the computing nodes;

[0026] A model building module is used to build a computing node selection optimization model with minimization of the total delay of task calculation as the optimization goal;

[0027] A node optimization module is used to optimize and solve the computing node selection optimization model, and determine the optimal computing node according to the optimal solution;

[0028] A path optimization module, used to traverse all computing nodes of the directed graph and update the weights of the paths to obtain an optimal path to the optimal computing node;

[0029] The task computing module is used to distribute the computing tasks to the optimal computing nodes through the optimal path and execute the computing tasks.

[0030] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the cloud platform-based computing task allocation method as described in the first aspect.

[0031] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the cloud platform-based computing task allocation method as described in the first aspect.

[0032] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the steps of the cloud platform-based computing task allocation method as described in the first aspect.

[0033] It can be seen from the above technical solutions that the present invention searches for the optimal solution by minimizing the total delay of task calculation as the optimization goal, obtains the computing node with the smallest total delay of the computing task, and determines the optimal path to the optimal computing node in a directed graph with edge weights, thereby further reducing the total delay of the computing task, thereby achieving rapid dynamic response to the scheduling of computing tasks, improving the task scheduling efficiency of computing tasks, and ensuring the stability and reliability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 An application environment diagram of a cloud platform-based computing task allocation method provided by an embodiment of the present invention;

[0035] Figure 2 A flowchart of a computing task allocation method based on a cloud platform provided in an embodiment of the present invention;

[0036] Figure 3 A schematic diagram of the structure of a computing task allocation system based on a cloud platform provided in an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The computing task allocation method based on the cloud platform provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, each computing node of the cloud platform communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0040] like Figure 2 As shown, the embodiment of the present application provides a computing task allocation method based on a cloud platform, and the method is applied to Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S5. Among them:

[0041] Step S1: construct a directed graph with edge weights based on all computing nodes in the cloud platform and the network connection relationship between the computing nodes.

[0042] It is assumed that the cloud platform of the power system is composed of multiple computing nodes, and the computing capabilities and network connection conditions of each computing node are different. In the embodiment of the present application, all computing nodes in the cloud platform are modeled as a directed graph G with edge weights, denoted as G=(V,E), where:

[0043] V is the set of all computing nodes in the cloud platform, including data centers and edge nodes;

[0044] E is the network connection between nodes, and the edge weight is the transmission delay between every two computing nodes, which can be set to the transmission delay between every two computing nodes.

[0045] Step S2: Taking minimizing the total delay of task calculation as the optimization goal, a computing node selection optimization model is constructed.

[0046] Assume that the current computing task to be scheduled is T, and its computing requirement is λ T , while computing task T has a certain urgency corresponding to the computing time upper limit U T ,The goal is to assign tasks to appropriate nodes so that the sum of computation delay and transmission delay is minimized while meeting the urgency of the task.

[0047] The embodiment of the present application fully considers factors such as network delay, computing power, and task urgency to ensure that each node can effectively process the assigned computing tasks when the cloud platform load changes dynamically, while avoiding waste of resources. To this end, the embodiment of the present application takes minimizing the total delay of task calculation as the optimization goal and constructs a computing node selection optimization model, including:

[0048] Step S201: Determine the computing time of the computing task according to the computing capacity of the computing node and the computing demand of the computing task;

[0049] The computing power of the computing node can be determined by the network memory of the computing node, and the computing time of the computing task is calculated as follows:

[0050]

[0051] In the formula, is the computation time of the task, is the load required to calculate the task, The network memory of the computing node.

[0052] Step S202: determine the total delay of the computing task according to the computing time of the computing task and the transmission delay from the source node to the target computing node.

[0053] The total delay of the computing task is calculated as follows:

[0054]

[0055] In the formula, To calculate the total delay of the task, It is the transmission delay from the source node to the target computing node, where the source node refers to the task generation node of the computing task on the cloud platform, and the target computing node refers to the computing node that executes the computing task on the cloud platform.

[0056] Step S203: Taking minimizing the total delay of the computing task as the optimization goal and taking the computing time corresponding to the urgency of the computing task as the upper limit constraint of the total delay, a computing node selection optimization model is constructed.

[0057] Among them, under the premise of meeting the task urgency, the embodiment of the present application constructs a computing node selection optimization model as follows:

[0058]

[0059] In the formula, is the set of computing nodes, d is the optimal computing node, The calculation time corresponding to the urgency of the calculation task.

[0060] Step S3: searching for the optimal solution for the optimization model of the computing node selection, and determining the optimal computing node according to the optimal solution.

[0061] Among them, a mathematical solver can be used to search and solve the optimization model for computing node selection, and the optimal computing node can be determined according to the optimal solution, so that the total delay of the computing task can be minimized while meeting the urgency of the computing task.

[0062] Step S4: traverse all computing nodes of the directed graph and update the weights of the paths to obtain the optimal path to the optimal computing node.

[0063] Among them, the Dijkstra algorithm can be used. The Dijkstra algorithm is an optimization algorithm for finding the shortest path between two nodes in a graph. The embodiment of the present application sets the edge weight to the transmission delay between every two computing nodes, and obtains the optimal path from the source node to the optimal computing node by continuously updating the path weight.

[0064] Step S5: Allocate the computing task to the optimal computing node through the optimal path, and execute the computing task.

[0065] It can be understood that the computing task can be transmitted along the optimal path to the optimal computing node to execute the computing task.

[0066] It should be noted that the embodiment of the present application searches for the optimal solution with the minimization of the total delay of task calculation as the optimization goal, obtains the computing node with the minimum total delay of the computing task, and determines the optimal path to the optimal computing node in a directed graph with edge weights through the Dijkstra algorithm, thereby further reducing the total delay of the computing task, thereby achieving rapid dynamic response to the scheduling of computing tasks, improving the task scheduling efficiency of computing tasks, and ensuring the stability and reliability of system operation.

[0067] In some embodiments, a computing task may also be obtained. Specifically, the method further includes:

[0068] S11. Upload the power data collected in real time by each node of the power grid system to the edge computing node, and pre-process the power data through the edge computing node.

[0069] Among them, a large number of data collection devices need to be deployed at each node of the power grid, which not only covers the different links of power generation, transmission, distribution and consumption, but also can capture the power data of each node in the operation of the power grid in real time. The power data includes but is not limited to voltage, current and power factor.

[0070] In order to improve data transmission efficiency and reduce the computing pressure of the central cloud platform, it is first necessary to pre-process these real-time collected power data through edge computing nodes to perform preliminary processing and filtering on the power data.

[0071] Among them, preprocessing includes data denoising and data compression.

[0072] For data denoising, in order to ensure the accuracy and reliability of collected data, common data denoising methods include algorithms based on wavelet transform or Fourier transform. These algorithms can identify and separate noise components by converting signals from the time domain to the frequency domain, and then remove unnecessary noise interference through technical means such as threshold filtering. Ultimately, the denoised data will be more accurate and can effectively reflect the actual operating status of the power system, providing a basis for subsequent data analysis and intelligent scheduling.

[0073] Data compression is the use of distributed edge computing technology. Each edge computing node has a certain computing power and can process part of the data on-site at the source of data collection to avoid transmitting a large amount of unprocessed raw data directly to the cloud. The application of edge computing mode can effectively reduce the bandwidth usage in data transmission and reduce latency. In addition, by introducing compression algorithms such as differential coding or predictive coding in edge computing nodes, the power data can be compressed through compression algorithms, which can further reduce the data volume and optimize transmission efficiency.

[0074] S12. Based on the fast retransmission algorithm, the preprocessed power data is uploaded to the cloud platform through a multi-level cache strategy to generate computing tasks.

[0075] Among them, in some embodiments, in the process of uploading power data to the cloud platform, the pre-processed power system data needs to be transmitted in a safe and reliable manner to ensure that it is not interfered with by the outside world during the transmission process, and can reach the cloud efficiently and with low latency for storage and further processing.

[0076] To this end, in step S12, based on the fast retransmission algorithm, the pre-processed power data is uploaded to the cloud platform through a multi-level cache strategy to generate a computing task, including:

[0077] S121. Based on a fast retransmission algorithm, the preprocessed power data is uploaded from the edge computing node to a regional cache node for caching for a preset time.

[0078] S122. Based on a fast retransmission algorithm, the power data cached by the regional cache node is uploaded to the cloud platform for caching, and a computing task is generated according to the power data.

[0079] It should be noted that, considering the huge amount of data in the power system and the possible instability of the network environment, the embodiment of the present application introduces a multi-level caching strategy to cope with potential network fluctuations. During the transmission process, a multi-level caching strategy is used to cope with network fluctuations to ensure that data can be transmitted to cloud storage reliably and with low latency.

[0080] Specifically, the multi-level cache strategy is implemented through multiple levels of cache mechanisms. Starting from the edge computing node cache, the power data is first temporarily stored in the edge node cache close to the acquisition source, and then gradually uploaded to the regional cache, where it is stored again for a short period of time, and finally transmitted to the cloud platform cache, and computing tasks are generated based on the power data. The cache time can be set based on experience.

[0081] It is understandable that the multi-level cache strategy can ensure that even if there is a temporary interruption or congestion in the network transmission path, the data can be continuously and stably uploaded to the cloud through the support of each layer of cache. At the same time, the introduction of the multi-level cache mechanism can also significantly reduce the data loss or delay caused by sudden network jitter, ensuring the smoothness and efficiency of the entire transmission process.

[0082] In addition, in order to improve the efficiency of data transmission and reduce delays, especially in a complex and dynamically changing network environment such as the power system, the embodiment of the present application adopts a fast retransmit algorithm. Under the traditional transmission control protocol, when data packets are lost or delayed, the entire transmission process is often affected. Through the fast retransmit mechanism, when certain data packets are detected to be lost, the system will quickly resend these lost data packets, avoiding long delays caused by waiting timeouts in conventional transmissions. This optimization process can effectively improve the reliability of transmission and greatly shorten the response time of data transmission.

[0083] Based on the same inventive concept, an embodiment of the present application also provides a cloud platform-based computing task allocation system for implementing the above-mentioned cloud platform-based computing task allocation method.

[0084] The implementation solution for solving the problem provided by the system is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more cloud platform-based computing task allocation system embodiments provided below can be referred to the limitations on the cloud platform-based computing task allocation method above, and will not be repeated here.

[0085] like Figure 3 As shown, the embodiment of the present application also provides a computing task allocation system based on a cloud platform, including:

[0086] A directed graph construction module 100 is used to construct a directed graph with edge weights according to all computing nodes in the cloud platform and the network connection relationship between the computing nodes;

[0087] A model building module 200 is used to build a computing node selection optimization model with minimization of the total delay of task calculation as the optimization goal;

[0088] The node optimization module 300 is used to optimize and solve the computing node selection optimization model and determine the optimal computing node according to the optimal solution;

[0089] The path optimization module 400 is used to traverse all computing nodes of the directed graph and update the weight of the path to obtain the optimal path to the optimal computing node;

[0090] The task computing module 500 is used to distribute the computing tasks to the optimal computing nodes through the optimal path and execute the computing tasks.

[0091] In some embodiments, the edge weight is the transmission delay between every two computing nodes.

[0092] In some embodiments, the model building module 200 includes:

[0093] A computing time determination module, used to determine the computing time of a computing task according to the computing capacity of the computing node and the computing requirements of the computing task;

[0094] A total delay determination module, used to determine the total delay of the computing task according to the computing time of the computing task and the transmission delay from the source node to the target computing node;

[0095] The model optimization module is used to build a computing node selection optimization model with the minimization of the total delay of the computing task as the optimization goal and the computing time corresponding to the urgency of the computing task as the upper limit constraint of the total delay.

[0096] In some embodiments, the system further comprises:

[0097] The preprocessing module is used to upload the power data collected in real time by each node of the power grid system to the edge computing node, and preprocess the power data through the edge computing node;

[0098] The task generation module is used to upload the pre-processed power data to the cloud platform through a multi-level cache strategy based on a fast retransmission algorithm to generate computing tasks.

[0099] In some embodiments, preprocessing includes data denoising and data compression.

[0100] In some embodiments, the task generation module includes:

[0101] A first cache module is used to upload the pre-processed power data from the edge computing node to the regional cache node for caching for a preset time based on a fast retransmission algorithm;

[0102] The second cache module is used to upload the power data cached by the regional cache node to the cloud platform for caching based on the fast retransmission algorithm, and generate computing tasks according to the power data.

[0103] like Figure 4 As shown, an embodiment of the present application also provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the cloud platform-based computing task allocation method in any of the above embodiments.

[0104] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed, the steps of the cloud platform-based computing task allocation method in any of the above embodiments are implemented.

[0105] An embodiment of the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the cloud platform-based computing task allocation method in any of the above embodiments.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, electronic devices, computer storage media and computer program products can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0108] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0109] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated unit 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. Based on this understanding, the technical solution of the present invention is essentially 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. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the method described in each embodiment of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A computing task allocation method based on a cloud platform, characterized in that: include: According to all computing nodes in the cloud platform and the network connection relationship between computing nodes, a directed graph with edge weights is constructed; Taking minimizing the total delay of task calculation as the optimization goal, a computing node selection optimization model is constructed; Selecting an optimization model for the computing node to perform optimal solution, and determining an optimal computing node according to the optimal solution; Traversing all computing nodes of the directed graph and updating the weights of the paths to obtain an optimal path to the optimal computing node; The computing task is allocated to the optimal computing node through the optimal path, and the computing task is executed.

2. The cloud platform-based computing task allocation method according to claim 1, characterized in that: The edge weight is the transmission delay between every two computing nodes.

3. The cloud platform-based computing task allocation method according to claim 1, characterized in that: The step of constructing a computing node selection optimization model with minimization of the total delay of task calculation as the optimization goal includes: Determining the computing time of the computing task according to the computing capacity of the computing node and the computing requirement of the computing task; Determine the total delay of the computing task according to the computing time of the computing task and the transmission delay from the source node to the target computing node; Taking minimizing the total delay of the computing task as the optimization goal and taking the computing time corresponding to the urgency of the computing task as the upper limit constraint of the total delay, a computing node selection optimization model is constructed.

4. The computing task allocation method based on the cloud platform according to claim 1, characterized in that: Before the step of constructing a directed graph with edge weights according to all computing nodes in the cloud platform and the network connection relationship between the computing nodes, the method further includes: Upload the power data collected in real time by each node of the power grid system to the edge computing node, and pre-process the power data through the edge computing node; Based on the fast retransmission algorithm, the preprocessed power data is uploaded to the cloud platform through a multi-level cache strategy to generate computing tasks.

5. The method for allocating computing tasks based on a cloud platform according to claim 4, characterized in that: The preprocessing includes data denoising and data compression.

6. The method for allocating computing tasks based on a cloud platform according to claim 4, characterized in that: The step of uploading the pre-processed power data to the cloud platform through a multi-level cache strategy based on a fast retransmission algorithm to generate a computing task includes: Based on a fast retransmission algorithm, the preprocessed power data is uploaded from the edge computing node to a regional cache node for caching for a preset time period; Based on a fast retransmission algorithm, the power data cached by the regional cache node is uploaded to the cloud platform for caching, and a computing task is generated according to the power data.

7. A computing task allocation system based on a cloud platform, characterized in that: include: A directed graph construction module is used to construct a directed graph with edge weights based on all computing nodes in the cloud platform and the network connection relationship between the computing nodes; A model building module is used to build a computing node selection optimization model with minimization of the total delay of task calculation as the optimization goal; A node optimization module is used to optimize and solve the computing node selection optimization model, and determine the optimal computing node according to the optimal solution; A path optimization module, used to traverse all computing nodes of the directed graph and update the weights of the paths to obtain an optimal path to the optimal computing node; The task computing module is used to distribute the computing tasks to the optimal computing nodes through the optimal path and execute the computing tasks.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the cloud platform-based computing task allocation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the cloud platform-based computing task allocation method as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the steps of the cloud platform-based computing task allocation method as described in any one of claims 1 to 6.

Images