Method and system for processing computing tasks

Abstract

The present invention discloses a method and system for processing computing tasks. The method includes: obtaining a target computing task, wherein the target computing task is used to solve a target problem; identifying the target computing task and determining the problem structure information of the target problem; determining the processing mode of the target computing task based on the problem structure information; and obtaining the computing result of the target computing task according to the processing mode. The present invention solves the technical problem that the method in the related art in which the solver processing mode is fixed as local solving or remote solving has poor flexibility in the solving process.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for processing computing tasks. Background Art

[0002] Solvers are software used to solve numerical computing tasks and are widely used in cloud computing, finance, transportation, manufacturing, energy and other technical fields. In the prior art, solvers will choose to solve locally or remotely when running.

[0003] The advantage of a local solver is that it solves directly on the local device without data transmission. The disadvantage is that the performance of the local device is limited, which may lead to slower solving of large-scale and difficult problems.

[0004] For solvers that solve on remote servers, the client-server architecture is used to send model files and parameter files to the remote server, and then receive the calculation results returned by the remote server. Its advantage is that the remote computing server has good performance and strong computing power. Its disadvantage is that for simple problems that are easy to solve, the data transmission process will cause the total time of remote solving to be longer than local solving.

[0005] Therefore, how to combine the advantages of local solving and remote solving to improve the flexibility of the solver solving process has become one of the important issues in this field. Currently, no effective solution has been proposed for the above problems. Summary of the invention

[0006] The embodiments of the present invention provide a method and system for processing computing tasks, so as to at least solve the technical problem that the method in the related art in which the solver processing mode is fixed to local solution or remote solution has poor flexibility in the solution process.

[0007] According to one aspect of an embodiment of the present invention, a method for processing a computing task is provided, comprising: obtaining a target computing task, wherein the target computing task is used to solve a target problem; identifying the target computing task and determining problem structure information of the target problem; determining a processing mode of the target computing task based on the problem structure information; and obtaining a computing result of the target computing task according to the processing mode.

[0008] According to another aspect of an embodiment of the present invention, a method for processing computing tasks is also provided, including: obtaining an e-commerce traffic allocation task, wherein the e-commerce traffic allocation task is used to solve the e-commerce traffic allocation problem; identifying the e-commerce traffic allocation task and determining the problem structure information of the e-commerce traffic allocation problem; determining a processing mode of the e-commerce traffic allocation task based on the problem structure information; and obtaining the e-commerce traffic allocation result of the e-commerce traffic allocation task according to the processing mode.

[0009] According to another aspect of an embodiment of the present invention, a method for processing computing tasks is also provided, including: obtaining a power dispatching task, wherein the power dispatching task is used to solve a power dispatching problem; identifying the power dispatching task and determining problem structure information of the power dispatching problem; determining a processing mode of the power dispatching task based on the problem structure information; and obtaining a power dispatching result of the power dispatching task according to the processing mode.

[0010] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, a method for controlling the device where the computer-readable storage medium is located to perform any one of the above-mentioned processing and computing tasks.

[0011] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein the program executes any one of the above-mentioned methods for processing computing tasks when running.

[0012] According to another aspect of an embodiment of the present invention, a system for processing computing tasks is also provided, including: a processor; and a memory, connected to the above-mentioned processor, for providing the above-mentioned processor with instructions for processing the following processing steps: obtaining a target computing task, wherein the target computing task is used to solve a target problem; identifying the target computing task and determining problem structure information of the target problem; determining a processing mode of the target computing task based on the problem structure information; and obtaining a computing result of the target computing task according to the processing mode.

[0013] In an embodiment of the present invention, a target computing task is first obtained, and the target computing task is used to solve a target problem. The target computing task is identified to determine the problem structure information of the target problem. A method for determining a processing mode of the target computing task based on the problem structure information is adopted, and a calculation result of the target computing task is obtained according to the processing mode.

[0014] It is easy to notice that, through the embodiment of the present application, the target computing task is identified and the problem structure information of the target problem is obtained, so as to determine the processing mode of the target computing task, which can be local solution, remote solution or simultaneous solution. Thus, the purpose of enabling the solver to automatically select the processing mode according to the problem structure information of the target problem is achieved, thereby achieving the technical effect of combining the advantages of local solution and remote solution and improving the flexibility of the solver solution process, thereby solving the technical problem of poor flexibility of the solution process in the method of fixing the solver processing mode to local solution or remote solution in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for processing computing tasks is shown;

[0017] Figure 2 is a flow chart of a method for processing a computing task according to an embodiment of the present invention;

[0018] Figure 3 is a schematic diagram of an optional process of processing a computing task according to an embodiment of the present invention;

[0019] Figure 4 is a flow chart of a method for processing a computing task according to an embodiment of the present invention;

[0020] Figure 5 is a flow chart of a method for processing a computing task according to an embodiment of the present invention;

[0021] Figure 6 is a schematic diagram of the structure of a device for processing a computing task according to an embodiment of the present invention;

[0022] Figure 7 is a schematic structural diagram of another device for processing computing tasks according to an embodiment of the present invention;

[0023] Figure 8 is a schematic structural diagram of another device for processing computing tasks according to an embodiment of the present invention;

[0024] Fig. 9 is a schematic structural diagram of another device for processing computing tasks according to an embodiment of the present invention;

[0025] Fig.10 is a structural block diagram of another computer terminal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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 should fall within the scope of protection of the present invention.

[0027] 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.

[0028] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0029] Optimization solver: Numerical computing software for solving optimization problems.

[0030] SolveProblem: The Application Programming Interface (API) used by the solver to execute local solve commands.

[0031] SubmitTask: The API used by the client to submit computing tasks during the remote solution process.

[0032] RetrieveTask: During the remote solution process, the client uses the API to obtain the calculation results from the remote computing server.

[0033] Example 1

[0034] According to an embodiment of the present invention, a method embodiment for processing a computing task is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for processing computing tasks is shown. Figure 1As shown, the computer terminal 10 (or mobile device 10) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0036] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the method for processing computing tasks in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned method for processing computing tasks. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0038] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0040] It should be noted that, in some optional embodiments, the above Figure 1 The computer device (or mobile device) shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above-described computer device (or mobile device).

[0041] Under the above operating environment, this application provides Figure 2 A method of processing computing tasks is shown. Figure 2 is a flow chart of a method for processing a computing task according to an embodiment of the present invention. Figure 2 As shown, the method for processing a computing task includes:

[0042] Step S202, obtaining a target computing task, wherein the target computing task is used to solve a target problem;

[0043] Step S204, identifying the target computing task and determining the problem structure information of the target problem;

[0044] Step S206, determining a processing mode of the target computing task based on the problem structure information;

[0045] Step S208, obtaining the calculation result of the target calculation task according to the processing mode.

[0046] In an embodiment of the present invention, the target computing task may be a numerical computing task in a technical field such as cloud computing, finance, transportation, manufacturing, and energy. The target computing task may be used to solve a target problem. The target problem may be a numerical problem in a technical field such as cloud computing, finance, transportation, manufacturing, and energy. Identifying the target computing task may determine the problem structure information of the target problem. The more difficult the target computing task is and the more complex the problem structure information of the target problem is, the greater the amount of computing resources to be used by the computing task.

[0047] Optionally, the processing mode of the target computing task may be: local solution, remote solution, or local and remote solution simultaneously. Based on the problem structure information of the target problem, the processing mode of the target computing task may be determined. Further, the calculation result of the target computing task may be obtained according to the processing mode.

[0048] In an embodiment of the present invention, a target computing task is first obtained, and the target computing task is used to solve a target problem. The target computing task is identified to determine the problem structure information of the target problem. A method for determining a processing mode of the target computing task based on the problem structure information is adopted, and a calculation result of the target computing task is obtained according to the processing mode.

[0049] It is easy to notice that, through the embodiment of the present application, the target computing task is identified and the problem structure information of the target problem is obtained, so as to determine the processing mode of the target computing task, which can be local solution, remote solution or simultaneous solution. Thus, the purpose of enabling the solver to automatically select the processing mode according to the problem structure information of the target problem is achieved, thereby achieving the technical effect of combining the advantages of local solution and remote solution and improving the flexibility of the solver solution process, thereby solving the technical problem of poor flexibility of the solution process in the method of fixing the solver processing mode to local solution or remote solution in the related art.

[0050] In an optional embodiment, in step S204, the target computing task is identified and the problem structure information of the target problem is determined, including the following method steps:

[0051] Step S241, identifying the target computing task and determining the target optimization model corresponding to the target computing task, wherein the target optimization model is used to model the target computing task as a target form to be solved;

[0052] Step S242, obtaining characteristic information of the target optimization model, wherein the characteristic information is used to characterize the calculation content corresponding to the target calculation task;

[0053] Step S243: determine the question structure information based on the feature information.

[0054] In the above optional embodiments, the target computing task may be a numerical computing task in a technical field such as cloud computing, finance, transportation, manufacturing, energy, etc. By identifying the target computing task, the above target optimization model corresponding to the target computing task may be determined. The target optimization model may be used to model the target computing task as a target form to be solved.

[0055] For example, the target computing task may be a mathematical programming problem Q1. The target optimization model may be an API for modeling, denoted as API01. Through the API01, the mathematical programming problem Q1 may be modeled into a form that can be understood by the program of the solver R1 (equivalent to the target form).

[0056] In the above optional embodiment, characteristic information of the above target optimization model is obtained, and the characteristic information can be used to characterize the calculation content corresponding to the above target calculation task, and can include but is not limited to: the number of variables, the number of constraints, the number of non-zero elements, etc. Based on the characteristic information, the problem structure information of the target problem can be determined.

[0057] For example: the above-mentioned target calculation task can be a mathematical programming problem Q1, and the corresponding characteristic information of the target optimization model API01 includes: the number of variables n1, the number of constraints n2, and the number of non-zero elements n3. Among them, the number of variables n1 is used to characterize the number of variables involved in the calculation process of the mathematical programming problem Q1; the number of constraints n2 is used to characterize the number of constraints involved in the calculation process of the mathematical programming problem Q1; the number of non-zero elements n3 is used to characterize the complexity of the data determinant involved in the calculation process of the mathematical programming problem Q1. Based on the number of variables n1, the number of constraints n2, and the number of non-zero elements n3, the problem structure information A1 corresponding to the mathematical programming problem Q1 can be calculated through a preset relationship.

[0058] In an optional embodiment, in step S206, determining the processing mode of the target computing task based on the problem structure information includes the following method steps:

[0059] Step S261, determining the amount of computing resources for the target computing task based on the problem structure information;

[0060] Step S262, when the amount of computing resources meets the first preset condition, determining that the processing mode of the target computing task is a local processing mode;

[0061] Step S263, when the amount of computing resources meets the second preset condition, determining that the processing mode of the target computing task is a remote processing mode;

[0062] Step S264: When the amount of computing resources meets the third preset condition, determining the processing mode of the target computing task to adopt both the local processing mode and the remote processing mode.

[0063] In the above optional embodiments, the target computing task may be a numerical computing task in a technical field such as cloud computing, finance, transportation, manufacturing, and energy. The target computing task may be used to solve a target problem. The target problem may be a numerical problem in a technical field such as cloud computing, finance, transportation, manufacturing, and energy. Based on the problem structure information of the above target problem, the amount of computing resources for the target computing task may be determined as described above. The more difficult the target computing task is and the more complex the problem structure information of the target problem is, the greater the amount of computing resources to be used for the computing task.

[0064] In the above optional embodiment, the above first preset condition may be that the amount of computing resources of the target computing task is lower than a lower preset threshold. When the amount of computing resources meets the first preset condition, it means that the resources required to execute the target computing task are small. At this time, the processing mode of the target computing task can be set to a local processing mode. The local processing mode can be to use a corresponding solver on a local device to solve the computing content corresponding to the target computing task.

[0065] In the above optional embodiment, the above second preset condition may be that the amount of computing resources of the target computing task is higher than a higher preset threshold. When the amount of computing resources meets the second preset condition, it means that more resources are required to execute the target computing task. At this time, the processing mode of the target computing task can be set to a remote processing mode. The remote processing mode can be to use a corresponding solver on a remote server to solve the computing content corresponding to the target computing task.

[0066] In the above optional embodiment, the above third preset condition may be that the amount of computing resources of the target computing task does not satisfy either the first preset condition or the second preset condition. When the third preset condition is satisfied, it indicates that it is difficult to determine how many resources are required to execute the target computing task. At this time, the processing mode of the target computing task may be set to adopt both the local processing mode and the remote processing mode.

[0067] It should be noted that in actual application scenarios in technical fields such as cloud computing, finance, transportation, manufacturing, and energy, the computing performance of local devices is usually lower than that of remote servers. Therefore, for computing tasks that require fewer resources, it is more appropriate to perform the calculation on the local device to save data transmission time, and for computing tasks that require more resources, it is more appropriate to perform the calculation on the remote server to save solution calculation time. In particular, for computing tasks that cannot determine how much resources are required, you can calculate them on the local device and the remote server at the same time, and then obtain the calculation results obtained earlier to save time.

[0068] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0069] Step S210, obtaining current network connection status information, wherein the network connection status information is used to determine whether an abnormality occurs in the current network connection;

[0070] Step S212: Switch between multiple processing modes based on the network connection status information, wherein the multiple processing modes include:

[0071] said local processing mode;

[0072] said remote processing mode;

[0073] Both local processing mode and remote processing mode are adopted.

[0074] In the above optional embodiment, the current network connection status information can be used to determine whether the current network connection of the current device is abnormal. The network connection status can be a state such as the remote network is connected, the remote network is not connected, etc. According to the network connection status information, multiple processing modes can be switched during the processing of the computing task.

[0075] Optionally, the above-mentioned multiple processing modes may include: local processing mode, which may be using a corresponding solver on a local device to solve the calculation content corresponding to the target computing task; remote processing mode, which may be using a corresponding solver on a remote server to solve the calculation content corresponding to the target computing task; and using both local processing mode and remote processing mode.

[0076] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0077] Step S214, when the local processing mode and the remote processing mode are used simultaneously, and when either the local processing mode or the remote processing mode obtains a calculation result, the other processing mode is terminated.

[0078] In the above optional embodiment, when the amount of computing resources of the target computing task meets the third preset condition, it means that it is difficult to determine how much resources are required to execute the target computing task. At this time, the following racing mechanism can be adopted: the local processing mode and the remote processing mode are adopted at the same time, and when any processing mode of the local processing mode and the remote processing mode obtains the computing result, the computing result is directly obtained, and the other processing mode of the local processing mode and the remote processing mode is terminated at the same time.

[0079] For example, when it is difficult to determine how many resources are needed to execute the mathematical programming problem Q1, the mathematical programming problem Q1 is calculated on the local device and the remote server at the same time. If the local device obtains the calculation result first, the calculation result is obtained and the calculation on the remote server is stopped; if the remote server returns the calculation result first, the calculation result is obtained and the calculation on the local device is stopped.

[0080] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0081] Step S216, determining whether the target computing task is set with parameter information corresponding to the remote processing mode;

[0082] Step S218: When the target computing task is set with parameter information, it is determined that the processing mode includes the remote processing mode.

[0083] In the above optional embodiments, the target computing task may be a numerical computing task in the technical fields of cloud computing, finance, transportation, manufacturing, energy, etc. The target computing task may be provided with a plurality of parameter information (such as processing mode information, processing time limit information, task type information, etc.).

[0084] In the above optional embodiment, before determining the processing mode of the corresponding target computing task based on the problem structure information of the target problem, it is necessary to determine whether the target computing task is set with parameter information corresponding to the remote processing mode. If the target computing task is set with parameter information corresponding to the remote processing mode, the processing mode of the target computing task can be set to the local processing mode, the remote processing mode, and the local processing mode and the remote processing mode at the same time according to the problem structure information. If the target computing task is not set with parameter information corresponding to the remote processing mode, the processing mode of the target computing task can be set to the local processing mode.

[0085] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0086] Step S220, determining a solver corresponding to the processing mode and an optimization parameter corresponding to the solver, wherein the optimization parameter is used to control the behavior of the solver;

[0087] Step S222, using the optimization parameters to control the solver to obtain the calculation results.

[0088] In the above optional embodiment, for the processing mode of the target computing task, the solver corresponding to the processing mode and the optimization parameters corresponding to the solver can be determined. The optimization parameters can be used to control the solver to obtain the calculation result of the target computing task.

[0089] For example, if the processing mode of a computing task Q2 is determined to be a local processing mode based on the problem structure information of the computing task Q2, then the solver R2 for local processing corresponding to the computing task Q2 can be determined, and the optimization parameter c2 corresponding to the solver can be determined. Then, the optimization parameter c2 can be used on the local device to control the solver R2 to obtain the calculation result R2 of the computing task Q2.

[0090] For another example: according to the problem structure information of a computing task Q3, it is determined that the processing mode of the computing task Q3 is the remote processing mode, then the solver R3 for remote processing corresponding to the computing task Q3 can be determined, and the optimization parameter c3 corresponding to the solver can be determined. Then, the optimization parameter c3 can be used on the remote server to control the solver R3 to obtain the calculation result R3 of the computing task Q3.

[0091] Figure 3 is a schematic diagram of an optional process of processing a computing task according to an embodiment of the present invention, such as Figure 3 As shown in the figure, the processing of computing tasks can include three parts: user input, solver execution, and result output. In particular, in the solver execution part, the processing mode corresponding to the computing task is automatically selected through two judgments: "whether to set remote solving parameters" and "whether it is a difficult problem to solve". The solution results output by the solver are displayed to the user through the result output part.

[0092] Specifically, Figure 3 As shown, in the user input part, the user needs to perform the following operations: input the optimization model, which can be an API for modeling; set the optimization parameters, which are used to control the behavior of the solver to optimize the solution process.

[0093] Specifically, Figure 3 As shown, in the solver execution part, first determine "whether to set remote solution parameters". For computing tasks that do not set remote solution parameters, directly perform local solution. For computing tasks that set remote solution parameters, identify the optimization problem corresponding to the computing task. The identification process may include the following method steps:

[0094] Step 1: Obtain the computing task and determine the modeling API corresponding to the computing task from the optimization model input by the user;

[0095] Step 2: Use the modeling API corresponding to the computing task to model the computing task into a form to be solved that can be recognized by the solver;

[0096] Step 3: Obtain the feature information of the modeling API corresponding to the computing task;

[0097] Step 4: Determine the amount of computing resources for the computing task based on the characteristic information.

[0098] The computing resource amount of the computing task can be obtained through the above method steps, and then it can be determined whether the computing task is a difficult problem to solve. If the computing task is a difficult problem to solve, the computing task is sent to the remote solution, the model file and the parameter file are uploaded to the remote server, and the calculation is performed on the remote server. If the computing task is not a difficult problem to solve, it is calculated directly on the local device. If it is impossible to determine whether the computing problem is a difficult problem to solve, the computing task is sent to the local device and the remote server for solution at the same time. In particular, when solving at the same time, when any one side has completed the solution, all solution processes are terminated. The solution result obtained earlier is output to the user.

[0099] It should be noted that the characteristic information used in the above-mentioned identification of optimization problems may include but is not limited to data size, number of variables, number of constraints, number of non-zero elements, etc. The method provided in this embodiment can be used in the architecture of the client and the server, and the remote solving API (including the submitTask API for submitting problems and the retrieveTask API for obtaining results) is embedded in the locally solved SolveProblem API, thereby unifying the APIs of the local solving mode and the remote solving mode, and realizing the automatic selection of the computing task processing mode.

[0100] Through the method provided in this embodiment, the APIs of the local solution mode and the remote solution mode can be unified, and then the amount of computing resources to be used can be obtained by identifying the target computing task, and the local processing mode or the remote processing mode or the local and remote simultaneous processing mode can be automatically determined based on this. This can avoid the problem of users having to manually select when processing computing tasks.

[0101] In an optional embodiment, a graphical user interface is provided by a terminal device, and the content displayed by the graphical user interface at least partially includes a computing task solution scenario, and the method for processing the computing task further includes the following method steps:

[0102] Step S302, displaying multiple candidate optimization models in a graphical user interface;

[0103] Step S304, in response to the first control operation acting on the graphical user interface, determining a target optimization model from a plurality of candidate optimization models;

[0104] Step S306, in response to the second control operation acting on the target optimization model, setting the optimization parameters associated with the target optimization model;

[0105] Step S308, in response to the third control operation acting on the target control, obtaining calculation results based on the target optimization model and the optimization parameters, and displaying the calculation results in the graphical user interface.

[0106] In the above optional embodiment, the user can at least partially obtain the above computing task solution scenario through the graphical user interface content displayed by the electronic device. In the computing task solution scenario, the user can perform the above first control operation, the above second control operation and the above third control operation. Multiple candidate optimization models can be displayed in the above graphical user interface.

[0107] Specifically, in the above-mentioned graphical user interface, the user can perform a first control operation on the graphical user interface, that is, the user determines the target optimization model by controlling some candidate optimization models among the multiple candidate optimization models displayed in the graphical user interface.

[0108] Specifically, in the above graphical user interface, the user can perform a second control operation on the target optimization model, that is, the user controls the control button corresponding to the target optimization model (such as a setting button, an association button, etc.) to set the above optimization parameters of the target optimization model. The optimization parameters can be used to control the solver to obtain the calculation results of the target calculation task.

[0109] Specifically, in the above-mentioned graphical user interface, the user can perform a third control operation on the target control, that is, the user controls the trigger button corresponding to the calculation process (such as a start calculation button, a get result button, etc.) to obtain the above-mentioned calculation results based on the above-mentioned target optimization model and the above-mentioned optimization parameters, and display the calculation results to the user through the graphical user interface.

[0110] In particular, the first control operation, the second control operation and the third control operation may be touch operations. The touch operation refers to an operation in which a user touches the display screen of the terminal device with a finger and controls the terminal device. The touch operation may include single-point touch and multi-point touch, wherein the touch operation of each touch point may include click, long press, heavy press, swipe, etc. The first control operation, the second control operation and the third control operation may also be control operations implemented by input devices such as a mouse and a keyboard.

[0111] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0112] Step S310, in response to the fourth control operation acting on the optimization parameter, adjusting the parameter content of the optimization parameter to obtain an adjustment result;

[0113] Step S312: updating the calculation result based on the adjustment result.

[0114] In the above optional embodiment, the user can also perform a fourth control operation on the optimization parameter in the graphical user interface, that is, the user adjusts the parameter content of the optimization parameter by controlling a control (such as an adjustment button, an adjustment control bar, etc.) related to the parameter content of the optimization parameter, thereby obtaining an adjustment result. The adjustment result can be used to update the calculation result.

[0115] In particular, the fourth control operation may be a touch operation. The touch operation refers to an operation in which a user touches the display screen of the terminal device with a finger and controls the terminal device. The touch operation may include single-point touch and multi-point touch, wherein the touch operation of each touch point may include click, long press, heavy press, swipe, etc. The fourth control operation may also be a control operation implemented by an input device such as a mouse or keyboard.

[0116] Under the above operating environment, the present invention provides Figure 4 A method of processing computing tasks is shown. Figure 4 is a flowchart of another method for processing a computing task according to an embodiment of the present invention. Figure 4 As shown, the method for processing a computing task includes:

[0117] Step S402, obtaining an e-commerce traffic allocation task, wherein the e-commerce traffic allocation task is used to solve the e-commerce traffic allocation problem;

[0118] Step S404, identifying the e-commerce traffic allocation task and determining the problem structure information of the e-commerce traffic allocation problem;

[0119] Step S406, determining a processing mode for the e-commerce traffic allocation task based on the problem structure information;

[0120] Step S408, obtaining the e-commerce traffic allocation result of the e-commerce traffic allocation task according to the processing mode.

[0121] In an embodiment of the present invention, the e-commerce traffic allocation task is a computing task for allocating online customer traffic in an actual e-commerce application scenario. The e-commerce traffic allocation task can be used to solve the e-commerce traffic allocation problem. The e-commerce traffic allocation problem can be a numerical problem related to online customer traffic allocation in an actual e-commerce application scenario. Identifying the e-commerce traffic allocation task can determine the problem structure information of the e-commerce traffic allocation problem. The more difficult the e-commerce traffic allocation task is, the more complex the problem structure information of the e-commerce traffic allocation problem is, and the corresponding amount of computing resources to be used is larger.

[0122] Optionally, the processing mode of the e-commerce traffic allocation task may be: local solution, remote solution, local and remote solution at the same time. Based on the problem structure information of the e-commerce traffic allocation problem, the processing mode of the e-commerce traffic allocation task can be determined. Then, the e-commerce traffic allocation result of the e-commerce traffic allocation task can be obtained according to the processing mode.

[0123] In an embodiment of the present invention, an e-commerce traffic allocation task is first obtained, and the e-commerce traffic allocation task is used to solve the e-commerce traffic allocation problem. The e-commerce traffic allocation task is identified to determine the problem structure information of the e-commerce traffic allocation problem. A method for determining a processing mode of the e-commerce traffic allocation task based on the problem structure information is adopted, and the e-commerce traffic allocation result of the e-commerce traffic allocation task is obtained according to the processing mode.

[0124] It is easy to notice that, through the embodiment of the present application, the e-commerce traffic allocation task is identified and the problem structure information of the e-commerce traffic allocation problem is obtained, so as to determine the processing mode of the e-commerce traffic allocation task, which can be local solution, remote solution or simultaneous solution. Thus, the purpose of enabling the solver to automatically select the processing mode according to the problem structure information of the e-commerce traffic allocation problem is achieved, thereby achieving the technical effect of combining the advantages of local solution and remote solution and improving the flexibility of the solver solution process, thereby solving the technical problem of poor flexibility of the solution process in the method of fixing the solver processing mode to local solution or remote solution in the related art.

[0125] In an optional embodiment, a graphical user interface is provided by a terminal device, and the content displayed by the graphical user interface at least partially includes an e-commerce traffic distribution scenario. The method for processing the computing task also includes the following method steps:

[0126] Step S410, displaying multiple candidate e-commerce traffic allocation models in a graphical user interface;

[0127] Step S412, in response to the first control operation acting on the graphical user interface, determining a target e-commerce traffic allocation model from a plurality of candidate e-commerce traffic allocation models;

[0128] Step S414, in response to the second control operation acting on the target e-commerce traffic allocation model, setting the e-commerce traffic allocation parameters associated with the target e-commerce traffic allocation model;

[0129] Step S416, in response to the third control operation acting on the target control, the e-commerce traffic allocation result is obtained based on the target e-commerce traffic allocation model and the e-commerce traffic allocation parameters, and the e-commerce traffic allocation result is displayed in the graphical user interface.

[0130] In the above optional embodiment, the user can at least partially obtain the above e-commerce traffic allocation scenario through the graphical user interface content displayed by the electronic device. In the e-commerce traffic allocation scenario, the user can perform the above first control operation, the above second control operation and the above third control operation. Multiple candidate e-commerce traffic allocation models can be displayed in the above graphical user interface.

[0131] Specifically, in the above-mentioned graphical user interface, the user can perform a first control operation on the graphical user interface, that is, the user determines the target e-commerce traffic distribution model by controlling some candidate e-commerce traffic distribution models among the multiple candidate e-commerce traffic distribution models displayed in the graphical user interface.

[0132] Specifically, in the above graphical user interface, the user can perform a second control operation on the target e-commerce traffic allocation model, that is, the user controls the control button corresponding to the target e-commerce traffic allocation model (such as a setting parameter button, an associated parameter button, etc.) to set the above e-commerce traffic allocation parameters of the target e-commerce traffic allocation model. The e-commerce traffic allocation parameters can be used to control the target e-commerce traffic allocation model to obtain the above e-commerce traffic allocation results.

[0133] Specifically, in the above-mentioned graphical user interface, the user can perform a third control operation on the target control, that is, the user controls the trigger button corresponding to the e-commerce traffic distribution process (such as the start distribution button, the get distribution result button, etc.) to obtain the above-mentioned calculation results based on the above-mentioned target optimization model and the above-mentioned optimization parameters, and display the calculation results to the user through the graphical user interface.

[0134] In particular, the first control operation, the second control operation and the third control operation may be touch operations. The touch operation refers to an operation in which a user touches the display screen of the terminal device with a finger and controls the terminal device. The touch operation may include single-point touch and multi-point touch, wherein the touch operation of each touch point may include click, long press, heavy press, swipe, etc. The first control operation, the second control operation and the third control operation may also be control operations implemented by input devices such as a mouse and a keyboard.

[0135] Under the above operating environment, the present invention provides Figure 5 A method of processing computing tasks is shown. Figure 5 is a flowchart of another method for processing a computing task according to an embodiment of the present invention. Figure 5 As shown, the method for processing a computing task includes:

[0136] Step S502, obtaining a power dispatching task, wherein the power dispatching task is used to solve the power dispatching problem;

[0137] Step S504, identifying the power dispatching task and determining the problem structure information of the power dispatching problem;

[0138] Step S506, determining a processing mode of the power dispatching task based on the problem structure information;

[0139] Step S508, obtaining the power dispatch result of the power dispatch task according to the processing mode.

[0140] In an embodiment of the present invention, a power dispatching task may be a computing task for dispatching power output or power stock in scenarios such as power production and power sales. The power dispatching task may be used to solve a power dispatching problem. The power dispatching problem may be a numerical problem related to dispatching power output or power stock in scenarios such as power production and power sales. Identifying the power dispatching task may determine the problem structure information of the power dispatching problem. The more difficult the power dispatching task is, the more complex the problem structure information of the power dispatching problem is, and the corresponding amount of computing resources to be used is greater.

[0141] Optionally, the processing mode of the power dispatching task may be: local solution, remote solution, or local and remote solution at the same time. Based on the problem structure information of the power dispatching problem, the processing mode of the power dispatching task can be determined. Furthermore, the power dispatching result of the power dispatching task can be obtained according to the processing mode.

[0142] In an embodiment of the present invention, a power dispatching task is first obtained, and the power dispatching task is used to solve the power dispatching problem. The problem structure information of the power dispatching problem is determined by identifying the power dispatching task, and a method for determining a processing mode of the power dispatching task based on the problem structure information is adopted. The power dispatching result of the power dispatching task is obtained according to the processing mode.

[0143] It is easy to notice that, through the embodiment of the present application, the power dispatching task is identified and the problem structure information of the power dispatching problem is obtained, so as to determine the processing mode of the power dispatching task, which can be local solution, remote solution or simultaneous solution. Thus, the purpose of enabling the solver to automatically select the processing mode according to the problem structure information of the power dispatching problem is achieved, thereby achieving the technical effect of combining the advantages of local solution and remote solution and improving the flexibility of the solver solution process, thereby solving the technical problem of poor flexibility of the solution process in the method of fixing the solver processing mode to local solution or remote solution in the related art.

[0144] In an optional embodiment, in step S504, the power dispatching task is identified to determine the problem structure information of the power dispatching problem, including the following method steps:

[0145] Step S541, identifying the power dispatching task and determining the target optimization model corresponding to the power dispatching task, wherein the target optimization model is used to model the power dispatching task as a target form to be solved;

[0146] Step S542, obtaining characteristic information of the target optimization model, wherein the characteristic information is used to characterize the calculation content corresponding to the power dispatching task;

[0147] Step S543: determine the question structure information based on the feature information.

[0148] In the above optional embodiment, the power dispatching task may be a computing task for dispatching power output or power stock in scenarios such as power production and power sales. By identifying the power dispatching task, the above target optimization model corresponding to the power dispatching task may be determined. The target optimization model may be used to model the power dispatching task as a target form to be solved.

[0149] For example, the power dispatching task may be a mathematical programming problem Q21. The target optimization model may be an API for modeling, denoted as API201. Through the API201, the mathematical programming problem Q21 may be modeled into a form that can be understood by the program of the solver R1 (equivalent to the target form).

[0150] In the above optional embodiment, characteristic information of the above target optimization model is obtained, and the characteristic information can be used to characterize the calculation content corresponding to the above power dispatching task, which can include but is not limited to: the number of variables, the number of constraints, the number of non-zero elements, etc. Based on the characteristic information, the problem structure information of the power dispatching problem can be determined.

[0151] For example: the above-mentioned power dispatching task can be a mathematical programming problem Q21, and the corresponding characteristic information of the target optimization model API201 includes: the number of variables n21, the number of constraints n22, and the number of non-zero elements n23. Among them, the number of variables n21 is used to characterize the number of variables involved in the calculation process of the mathematical programming problem Q21; the number of constraints n22 is used to characterize the number of constraints involved in the calculation process of the mathematical programming problem Q21; the number of non-zero elements n23 is used to characterize the complexity of the data determinant involved in the calculation process of the mathematical programming problem Q21. Based on the number of variables n21, the number of constraints n22, and the number of non-zero elements n23, the problem structure information A21 corresponding to the mathematical programming problem Q21 can be calculated by a preset relationship.

[0152] In an optional embodiment, in step S506, determining a processing mode of the power dispatching task based on the problem structure information includes the following method steps:

[0153] Step S561, determining the amount of computing resources for the power dispatching task based on the problem structure information;

[0154] Step S562, when the amount of computing resources meets the first preset condition, determining that the processing mode of the power dispatching task is a local processing mode;

[0155] Step S563, when the amount of computing resources meets the second preset condition, determining that the processing mode of the power dispatching task is a remote processing mode;

[0156] Step S564: when the amount of computing resources meets the third preset condition, determining the processing mode of the power dispatching task to adopt both the local processing mode and the remote processing mode.

[0157] In the above optional embodiment, the power dispatching task is a computing task for dispatching power output or power stock in scenarios such as power production and power sales. The power dispatching task can be used to solve the power dispatching problem. The power dispatching problem can be a numerical problem related to dispatching power output or power stock in scenarios such as power production and power sales. Based on the problem structure information of the above power dispatching problem, the amount of computing resources for the power dispatching task can be determined as above. The more difficult the power dispatching task is and the more complex the problem structure information of the power dispatching problem is, the greater the amount of computing resources to be used for the power dispatching task.

[0158] In the above optional embodiment, the above first preset condition may be that the amount of computing resources of the power dispatching task is lower than a lower preset threshold. When the amount of computing resources meets the first preset condition, it means that the resources required to execute the power dispatching task are small. At this time, the processing mode of the power dispatching task can be set to a local processing mode. The local processing mode can be to use a corresponding solver on a local device to solve the computing content corresponding to the power dispatching task.

[0159] In the above optional embodiment, the above second preset condition may be that the amount of computing resources of the power dispatching task is higher than a higher preset threshold. When the amount of computing resources meets the second preset condition, it means that more resources are required to execute the power dispatching task. At this time, the processing mode of the power dispatching task can be set to a remote processing mode. The remote processing mode can be to use a corresponding solver on a remote server to solve the computing content corresponding to the power dispatching task.

[0160] In the above optional embodiment, the above third preset condition may be that the amount of computing resources of the power dispatching task does not meet the first preset condition nor the second preset condition. When the third preset condition is met, it means that it is difficult to determine how many resources are required to execute the power dispatching task. At this time, the processing mode of the power dispatching task can be set to adopt both the local processing mode and the remote processing mode.

[0161] It should be noted that in the actual application scenario of power dispatch, the computing performance of the local device is usually lower than that of the remote server. Therefore, for computing tasks that require less resources, it is more suitable to perform the calculation on the local device to save data transmission time, and for computing tasks that require more resources, it is more suitable to perform the calculation on the remote server to save the solution calculation time. In particular, for computing tasks that cannot determine how much resources are required, they can be calculated simultaneously on the local device and the remote server, and then the power dispatch results obtained earlier can be obtained to save time.

[0162] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0163] Step S510, obtaining current network connection status information, wherein the network connection status information is used to determine whether an abnormality occurs in the current network connection;

[0164] Step S512: Switch between multiple processing modes based on the network connection status information, wherein the multiple processing modes include:

[0165] said local processing mode;

[0166] said remote processing mode;

[0167] Both local processing mode and remote processing mode are adopted.

[0168] In the above optional embodiment, the current network connection status information can be used to determine whether an abnormality occurs in the current network connection of the current device. The network connection status can be a state such as the remote network is connected, the remote network is not connected, etc. According to the network connection status information, multiple processing modes can be switched during the processing of the power dispatching task.

[0169] Optionally, the above-mentioned multiple processing modes may include: local processing mode, which may be using a corresponding solver on a local device to solve the calculation content corresponding to the power dispatching task; remote processing mode, which may be using a corresponding solver on a remote server to solve the calculation content corresponding to the power dispatching task; and using both local processing mode and remote processing mode.

[0170] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0171] Step S514, when the local processing mode and the remote processing mode are used simultaneously, and when either the local processing mode or the remote processing mode obtains the power dispatch result, the other processing mode is terminated.

[0172] In the above optional embodiment, when the amount of computing resources of the power dispatching task meets the third preset condition, it means that it is difficult to determine how many resources are required to execute the power dispatching task. At this time, the following racing mechanism can be adopted: adopting the local processing mode and the remote processing mode at the same time, when any processing mode of the local processing mode and the remote processing mode obtains the power dispatching result, directly obtain the power dispatching result, and terminate the other processing mode of the local processing mode and the remote processing mode at the same time.

[0173] For example, when it is difficult to determine how many resources are needed to execute the mathematical programming problem Q21, the mathematical programming problem Q21 is calculated on both the local device and the remote server. If the local device obtains the power dispatch result first, the power dispatch result is obtained and the calculation on the remote server is stopped; if the remote server returns the power dispatch result first, the power dispatch result is obtained and the calculation on the local device is stopped.

[0174] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0175] Step S516, determining whether the power dispatching task is set with parameter information corresponding to the remote processing mode;

[0176] Step S518: When the power dispatching task is set with parameter information, it is determined that the processing mode includes a remote processing mode.

[0177] In the above optional embodiment, the power dispatching task may be a computing task for dispatching power output or power stock in scenarios such as power production and power sales. The power dispatching task may be provided with a plurality of parameter information (such as processing mode information, processing time limit information, task type information, etc.).

[0178] In the above optional embodiment, before determining the processing mode of the corresponding power dispatching task based on the problem structure information of the power dispatching problem, it is necessary to determine whether the power dispatching task is set with parameter information corresponding to the remote processing mode. If the power dispatching task is set with parameter information corresponding to the remote processing mode, the processing mode of the power dispatching task can be set to the local processing mode, the remote processing mode, and the local processing mode and the remote processing mode at the same time according to the problem structure information. If the power dispatching task is not set with parameter information corresponding to the remote processing mode, the processing mode of the power dispatching task can be set to the local processing mode.

[0179] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0180] Step S520, determining a solver corresponding to the processing mode and an optimization parameter corresponding to the solver, wherein the optimization parameter is used to control the behavior of the solver;

[0181] Step S522, using the optimization parameter control solver to obtain the power dispatch result.

[0182] In the above optional embodiment, for the processing mode of the power dispatching task, the solver corresponding to the processing mode and the optimization parameters corresponding to the solver can be determined. The optimization parameters can be used to control the solver to obtain the power dispatching result of the power dispatching task.

[0183] For example, according to the problem structure information of a power dispatching calculation task Q22, it is determined that the processing mode of the power dispatching calculation task Q22 is the local processing mode, then the solver R2 for local processing corresponding to the power dispatching calculation task Q22 can be determined, and the optimization parameter c22 corresponding to the solver can be determined. Then, the optimization parameter c22 can be used on the local device to control the solver R22 to obtain the power dispatching result R22 of the power dispatching calculation task Q22.

[0184] For another example: according to the problem structure information of a certain power dispatching calculation task Q23, it is determined that the processing mode of the power dispatching calculation task Q23 is the remote processing mode, then the solver R23 for remote processing corresponding to the power dispatching calculation task Q23 can be determined, and the optimization parameter c23 corresponding to the solver can be determined. Then, the optimization parameter c23 can be used on the remote server to control the solver R23 to obtain the power dispatching result R23 of the power dispatching calculation task Q23.

[0185] Through the method provided in this embodiment, the APIs of the local solution mode and the remote solution mode can be unified, and then the amount of computing resources to be used can be obtained by identifying the power scheduling task, and the local processing mode or the remote processing mode or the local and remote simultaneous processing mode can be automatically determined based on this. This can avoid the problem of users having to manually select when processing computing tasks.

[0186] In an optional embodiment, a graphical user interface is provided by a terminal device, and the content displayed by the graphical user interface at least partially includes a power dispatching scenario. The method for processing a computing task also includes the following method steps:

[0187] Step S524, displaying a plurality of candidate power dispatch models in a graphical user interface;

[0188] Step S526, in response to the first control operation acting on the graphical user interface, determining a target power dispatch model from a plurality of candidate power dispatch models;

[0189] Step S528, in response to the second control operation acting on the target power dispatch model, setting the power dispatch parameters associated with the target power dispatch model;

[0190] Step S530, in response to the third control operation acting on the target control, obtaining a power dispatch result based on the target power dispatch model and the power dispatch parameter, and displaying the power dispatch result in a graphical user interface.

[0191] In the above optional embodiment, the user can at least partially obtain the above power dispatching scenario through the graphical user interface content displayed by the electronic device. In the power dispatching scenario, the user can perform the above first control operation, the above second control operation and the above third control operation. Multiple candidate power dispatching models can be displayed in the above graphical user interface.

[0192] Specifically, in the above-mentioned graphical user interface, the user can perform a first control operation on the graphical user interface, that is, the user determines the target power dispatching model by controlling some of the multiple candidate power dispatching models displayed in the graphical user interface.

[0193] Specifically, in the above graphical user interface, the user can perform a second control operation on the target power dispatch model, that is, the user controls the control button (such as a setting button, an association button, etc.) corresponding to the target power dispatch model to set the above power dispatch parameters of the target power dispatch model. The power dispatch parameters can be used to control the solver to obtain the power dispatch result of the power dispatch task.

[0194] Specifically, in the above-mentioned graphical user interface, the user can perform a third control operation on the target control, that is, the user controls the trigger button corresponding to the calculation process (such as the start calculation button, the get result button, etc.) to obtain the above-mentioned power dispatching result based on the above-mentioned target power dispatching model and the above-mentioned power dispatching parameters, and display the power dispatching result to the user through the graphical user interface.

[0195] In particular, the first control operation, the second control operation and the third control operation may be touch operations. The touch operation refers to an operation in which a user touches the display screen of the terminal device with a finger and controls the terminal device. The touch operation may include single-point touch and multi-point touch, wherein the touch operation of each touch point may include click, long press, heavy press, swipe, etc. The first control operation, the second control operation and the third control operation may also be control operations implemented by input devices such as a mouse and a keyboard.

[0196] In an optional embodiment, the method for processing a computing task further includes the following method steps:

[0197] Step S532, in response to the fourth control operation acting on the power dispatching parameter, adjusting the parameter content of the power dispatching parameter to obtain an adjustment result;

[0198] Step S534: update the power dispatch result based on the adjustment result.

[0199] In the above optional embodiment, the user can also perform a fourth control operation on the power dispatch parameter in the graphical user interface, that is, the user adjusts the parameter content of the power dispatch parameter by controlling a control (such as an adjustment button, an adjustment control bar, etc.) related to the parameter content of the power dispatch parameter, thereby obtaining an adjustment result. The adjustment result can be used to update the power dispatch result.

[0200] In particular, the fourth control operation may be a touch operation. The touch operation refers to an operation in which a user touches the display screen of the terminal device with a finger and controls the terminal device. The touch operation may include single-point touch and multi-point touch, wherein the touch operation of each touch point may include click, long press, heavy press, swipe, etc. The fourth control operation may also be a control operation implemented by an input device such as a mouse or keyboard.

[0201] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0202] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0203] Example 2

[0204] According to an embodiment of the present invention, there is also provided an apparatus embodiment for implementing the above method for processing computing tasks. Figure 6 is a schematic diagram of a structure of a device for processing a computing task according to an embodiment of the present invention, such as Figure 6 As shown, the device includes: an acquisition module 601, an identification module 602, a determination module 603, and a calculation module 604, wherein:

[0205] The acquisition module 601 is used to acquire the target computing task, wherein the target computing task is used to solve the target problem; the identification module 602 is used to identify the target computing task and determine the problem structure information of the target problem; the determination module 603 is used to determine the processing mode of the target computing task based on the problem structure information; the calculation module 604 is used to obtain the calculation result of the target computing task according to the processing mode.

[0206] Optionally, the above-mentioned identification module 602 is also used to: identify the target computing task, determine the target optimization model corresponding to the target computing task, wherein the target optimization model is used to model the target computing task as a target form to be solved; obtain characteristic information of the target optimization model, wherein the characteristic information is used to characterize the calculation content corresponding to the target computing task; and determine the problem structure information based on the characteristic information.

[0207] Optionally, the above-mentioned determination module 603 is also used to: determine the amount of computing resources of the target computing task based on the problem structure information; when the amount of computing resources meets the first preset condition, determine that the processing mode of the target computing task is the local processing mode; when the amount of computing resources meets the second preset condition, determine that the processing mode of the target computing task is the remote processing mode; when the amount of computing resources meets the third preset condition, determine that the processing mode of the target computing task is to use both the local processing mode and the remote processing mode.

[0208] Optionally, Figure 7 is a schematic diagram of the structure of another device for processing computing tasks according to an embodiment of the present invention. Figure 7 As shown, the device includes Figure 6 In addition to all the modules shown, it also includes: a switching module 605, which is used to obtain the current network connection status information, wherein the network connection status information is used to determine whether an abnormality occurs in the current network connection; based on the network connection status information, switching between multiple processing modes, wherein the multiple processing modes include: local processing mode; remote processing mode; and using both local processing mode and remote processing mode.

[0209] Optionally, Figure 8 is a schematic diagram of the structure of another device for processing computing tasks according to an embodiment of the present invention. Figure 8 As shown, the device includes Figure 7 In addition to all the modules shown, it also includes: a termination module 606, which is used to terminate the other processing mode of the local processing mode and the remote processing mode when the local processing mode and the remote processing mode are used simultaneously and either processing mode obtains a calculation result.

[0210] Optionally, Fig. 9is a schematic diagram of the structure of another device for processing computing tasks according to an embodiment of the present invention. Fig. 9 As shown, the device includes Figure 8 In addition to all the modules shown, it also includes: an optimization module 607, which is used to determine the solver corresponding to the processing mode and the optimization parameters corresponding to the solver, wherein the optimization parameters are used to control the behavior of the solver; and the solver is controlled by using the optimization parameters to obtain the calculation results.

[0211] It should be noted that the acquisition module 601, the identification module 602, the determination module 603, and the calculation module 604 correspond to steps S202 to S208 in Example 1, and the four modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules, as part of the device, can be run in the computer terminal 10 provided in Example 1.

[0212] In an embodiment of the present invention, first, an acquisition module is used to acquire a target computing task, which is used to solve a target problem. The target computing task is identified by using an identification module to determine the problem structure information of the target problem. Then, a determination module is used to determine a processing mode of the target computing task based on the problem structure information. Then, a calculation module is used to obtain a calculation result of the target computing task according to the processing mode.

[0213] It is easy to notice that, through the embodiment of the present application, the target computing task is identified and the problem structure information of the target problem is obtained, so as to determine the processing mode of the target computing task, which can be local solution, remote solution or simultaneous solution. Thus, the purpose of enabling the solver to automatically select the processing mode according to the problem structure information of the target problem is achieved, thereby achieving the technical effect of combining the advantages of local solution and remote solution and improving the flexibility of the solver solution process, thereby solving the technical problem of poor flexibility of the solution process in the method of fixing the solver processing mode to local solution or remote solution in the related art.

[0214] It should be noted that the preferred implementation of this embodiment can refer to the relevant description in Example 1, which will not be repeated here.

[0215] Example 3

[0216] According to an embodiment of the present invention, an embodiment of an electronic device is also provided, and the electronic device may be any computing device in a computing device group. The electronic device includes: a processor and a memory, wherein:

[0217] A memory is connected to the above-mentioned processor and is used to provide the above-mentioned processor with instructions for processing the following processing steps: obtaining a target computing task, wherein the target computing task is used to solve a target problem; identifying the target computing task and determining problem structure information of the target problem; determining a processing mode of the target computing task based on the problem structure information; and obtaining a calculation result of the target computing task according to the processing mode.

[0218] In an embodiment of the present invention, a target computing task is first obtained, and the target computing task is used to solve a target problem. The target computing task is identified to determine the problem structure information of the target problem. A method for determining a processing mode of the target computing task based on the problem structure information is adopted, and a calculation result of the target computing task is obtained according to the processing mode.

[0219] It is easy to notice that, through the embodiment of the present application, the target computing task is identified and the problem structure information of the target problem is obtained, so as to determine the processing mode of the target computing task, which can be local solution, remote solution or simultaneous solution. Thus, the purpose of enabling the solver to automatically select the processing mode according to the problem structure information of the target problem is achieved, thereby achieving the technical effect of combining the advantages of local solution and remote solution and improving the flexibility of the solver solution process, thereby solving the technical problem of poor flexibility of the solution process in the method of fixing the solver processing mode to local solution or remote solution in the related art.

[0220] It should be noted that the preferred implementation of this embodiment can refer to the relevant description in Example 1, which will not be repeated here.

[0221] Example 4

[0222] The embodiment of the present invention can provide a computer terminal, which can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal can also be replaced by a terminal device such as a mobile terminal.

[0223] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0224] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for processing computing tasks: obtaining the target computing task; identifying the target computing task and determining the problem structure information of the target problem; determining the processing mode of the target computing task based on the amount of computing resources; and obtaining the computing result of the target computing task according to the processing mode.

[0225] Optionally, Fig.10 is a structural block diagram of another computer terminal according to an embodiment of the present invention. Fig.10As shown, the computer terminal may include: one or more (only one is shown in the figure) processors 122 , a memory 124 , and a peripheral interface 126 .

[0226] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for processing computing tasks in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method for processing computing tasks. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0227] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtain the target computing task, which is used to solve the target problem; identify the target computing task and determine the problem structure information of the target problem; determine the processing mode of the target computing task based on the problem structure information; and obtain the calculation result of the target computing task according to the processing mode.

[0228] Optionally, the processor may also execute the program code of the following steps: identifying the target computing task, determining the target optimization model corresponding to the target computing task, wherein the target optimization model is used to model the target computing task as a target form to be solved; obtaining feature information of the target optimization model, wherein the feature information is used to characterize the computing content corresponding to the target computing task; and determining the problem structure information based on the feature information.

[0229] Optionally, the processor may also execute the program code of the following steps: determining the amount of computing resources of the target computing task based on the problem structure information; when the amount of computing resources meets the first preset condition, determining the processing mode of the target computing task to be the local processing mode; when the amount of computing resources meets the second preset condition, determining the processing mode of the target computing task to be the remote processing mode; when the amount of computing resources meets the third preset condition, determining the processing mode of the target computing task to simultaneously use the local processing mode and the remote processing mode.

[0230] Optionally, the processor may also execute program code for the following steps: obtaining current network connection status information, wherein the network connection status information is used to determine whether an abnormality occurs in the current network connection; switching between multiple processing modes based on the network connection status information, wherein the multiple processing modes include: local processing mode; remote processing mode; and using both local processing mode and remote processing mode.

[0231] Optionally, the processor may also execute the following program code: when the local processing mode and the remote processing mode are used simultaneously, and when either of the local processing mode and the remote processing mode obtains a calculation result, the other processing mode is terminated.

[0232] Optionally, the processor may also execute program code of the following steps: determining a solver corresponding to a processing mode and optimization parameters corresponding to the solver, wherein the optimization parameters are used to control the behavior of the solver; and using the optimization parameters to control the solver to obtain calculation results.

[0233] Optionally, the processor may also execute program codes for the following steps: displaying multiple candidate optimization models in a graphical user interface; determining a target optimization model from multiple candidate optimization models in response to a first control operation acting on the graphical user interface; setting optimization parameters associated with the target optimization model in response to a second control operation acting on the target optimization model; obtaining calculation results based on the target optimization model and the optimization parameters in response to a third control operation acting on the target control, and displaying the calculation results in the graphical user interface.

[0234] Optionally, the processor may also execute program codes of the following steps: in response to a fourth control operation acting on the optimization parameter, adjusting the parameter content of the optimization parameter to obtain an adjustment result; and updating the calculation result based on the adjustment result.

[0235] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtain the e-commerce traffic allocation task, wherein the e-commerce traffic allocation task is used to solve the e-commerce traffic allocation problem; identify the e-commerce traffic allocation task and determine the problem structure information of the e-commerce traffic allocation problem; determine the processing mode of the e-commerce traffic allocation task based on the problem structure information; and obtain the e-commerce traffic allocation result of the e-commerce traffic allocation task according to the processing mode.

[0236] Optionally, the processor may also execute program codes of the following steps: displaying a plurality of candidate e-commerce traffic allocation models in a graphical user interface; determining a target e-commerce traffic allocation model from a plurality of candidate e-commerce traffic allocation models in response to a first control operation acting on the graphical user interface; setting e-commerce traffic allocation parameters associated with the target e-commerce traffic allocation model in response to a second control operation acting on the target e-commerce traffic allocation model; obtaining an e-commerce traffic allocation result based on the target e-commerce traffic allocation model and the e-commerce traffic allocation parameters in response to a third control operation acting on the target control, and displaying the e-commerce traffic allocation result in the graphical user interface.

[0237] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtain a power dispatching task, wherein the power dispatching task is used to solve the power dispatching problem; identify the power dispatching task and determine the problem structure information of the power dispatching problem; determine the processing mode of the power dispatching task based on the problem structure information; and obtain the power dispatching result of the power dispatching task according to the processing mode.

[0238] In an embodiment of the present invention, a target computing task is first obtained, and the target computing task is used to solve a target problem. The target computing task is identified to determine the problem structure information of the target problem. A method for determining a processing mode of the target computing task based on the problem structure information is adopted, and a calculation result of the target computing task is obtained according to the processing mode.

[0239] It is easy to notice that, through the embodiment of the present application, the target computing task is identified and the problem structure information of the target problem is obtained, so as to determine the processing mode of the target computing task, which can be local solution, remote solution or simultaneous solution. Thus, the purpose of enabling the solver to automatically select the processing mode according to the problem structure information of the target problem is achieved, thereby achieving the technical effect of combining the advantages of local solution and remote solution and improving the flexibility of the solver solution process, thereby solving the technical problem of poor flexibility of the solution process in the method of fixing the solver processing mode to local solution or remote solution in the related art.

[0240] It can be understood by those skilled in the art that Fig.10 The structure shown is for illustration only, and the computer terminal may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Fig.10 It does not limit the structure of the above electronic device. For example, the computer terminal may also include Fig.10 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Fig.10Different configurations shown.

[0241] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0242] According to an embodiment of the present invention, an embodiment of a computer-readable storage medium is also provided. Optionally, in this embodiment, the computer-readable storage medium can be used to store program codes executed by the method for processing computing tasks provided in the above embodiment 1.

[0243] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0244] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining a target computing task, which is used to solve a target problem; identifying the target computing task and determining problem structure information of the target problem; determining a processing mode of the target computing task based on the problem structure information; and obtaining a calculation result of the target computing task according to the processing mode.

[0245] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: identifying the target computing task, determining a target optimization model corresponding to the target computing task, wherein the target optimization model is used to model the target computing task as a target form to be solved; obtaining characteristic information of the target optimization model, wherein the characteristic information is used to characterize the computing content corresponding to the target computing task; and determining problem structure information based on the characteristic information.

[0246] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining the amount of computing resources of the target computing task based on the problem structure information; when the amount of computing resources meets the first preset condition, determining the processing mode of the target computing task to be the local processing mode; when the amount of computing resources meets the second preset condition, determining the processing mode of the target computing task to be the remote processing mode; when the amount of computing resources meets the third preset condition, determining the processing mode of the target computing task to simultaneously use the local processing mode and the remote processing mode.

[0247] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining current network connection status information, wherein the network connection status information is used to determine whether an abnormality occurs in the current network connection; switching between multiple processing modes based on the network connection status information, wherein the multiple processing modes include: local processing mode; remote processing mode; and using both local processing mode and remote processing mode.

[0248] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when the local processing mode and the remote processing mode are adopted at the same time, and either the local processing mode or the remote processing mode obtains a calculation result, the other processing mode of the local processing mode and the remote processing mode is terminated.

[0249] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining a solver corresponding to a processing mode and optimization parameters corresponding to the solver, wherein the optimization parameters are used to control the behavior of the solver; and using the optimization parameters to control the solver to obtain calculation results.

[0250] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: displaying multiple candidate optimization models in a graphical user interface; determining a target optimization model from multiple candidate optimization models in response to a first control operation acting on the graphical user interface; setting optimization parameters associated with the target optimization model in response to a second control operation acting on the target optimization model; and obtaining calculation results based on the target optimization model and the optimization parameters in response to a third control operation acting on the target control, and displaying the calculation results in the graphical user interface.

[0251] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: in response to a fourth control operation acting on the optimization parameter, adjusting the parameter content of the optimization parameter to obtain an adjustment result; and updating the calculation result based on the adjustment result.

[0252] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining an e-commerce traffic allocation task, wherein the e-commerce traffic allocation task is used to solve the e-commerce traffic allocation problem; identifying the e-commerce traffic allocation task and determining the problem structure information of the e-commerce traffic allocation problem; determining a processing mode of the e-commerce traffic allocation task based on the problem structure information; and obtaining the e-commerce traffic allocation result of the e-commerce traffic allocation task according to the processing mode.

[0253] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: displaying multiple candidate e-commerce traffic allocation models in a graphical user interface; in response to a first control operation acting on the graphical user interface, determining a target e-commerce traffic allocation model from multiple candidate e-commerce traffic allocation models; in response to a second control operation acting on the target e-commerce traffic allocation model, setting e-commerce traffic allocation parameters associated with the target e-commerce traffic allocation model; in response to a third control operation acting on the target control, obtaining an e-commerce traffic allocation result based on the target e-commerce traffic allocation model and the e-commerce traffic allocation parameters, and displaying the e-commerce traffic allocation result in the graphical user interface.

[0254] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining a power dispatching task, wherein the power dispatching task is used to solve a power dispatching problem; identifying the power dispatching task and determining problem structure information of the power dispatching problem; determining a processing mode of the power dispatching task based on the problem structure information; and obtaining a power dispatching result of the power dispatching task according to the processing mode.

[0255] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0256] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0257] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the 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 units or modules, which can be electrical or other forms.

[0258] 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.

[0259] 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.

[0260] 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, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0261] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing a computing task, characterized in that include: Obtaining a target computing task, wherein the target computing task is used to solve a target problem; Identifying the target computing task and determining a target optimization model corresponding to the target computing task, wherein the target optimization model is used to model the target computing task as a target form to be solved; Acquire characteristic information of the target optimization model, wherein the characteristic information is used to characterize the calculation content corresponding to the target calculation task; Based on the characteristic information, determining the problem structure information of the target problem; based on the problem structure information, determining the amount of computing resources for the target computing task; Based on the amount of computing resources, determining a processing mode of the target computing task, wherein the processing mode includes one of the following three modes: a local processing mode, a remote processing mode, and using both the local processing mode and the remote processing mode; Obtain the calculation result of the target calculation task according to the processing mode.

2. The method according to claim 1, characterized in that Based on the amount of computing resources, determining the processing mode of the target computing task includes: When the amount of computing resources meets a first preset condition, determining that the processing mode of the target computing task is the local processing mode; When the amount of computing resources meets a second preset condition, determining that the processing mode of the target computing task is the remote processing mode; When the amount of computing resources meets a third preset condition, the processing mode of the target computing task is determined to adopt both the local processing mode and the remote processing mode.

3. The method according to claim 2, characterized in that The method further comprises: Acquire current network connection status information, wherein the network connection status information is used to determine whether an abnormality occurs in the current network connection; Switching between multiple processing modes based on the network connection status information, wherein the multiple processing modes include: said local processing mode; said remote processing mode; The local processing mode and the remote processing mode are adopted simultaneously.

4. The method according to claim 2, characterized in that: The method further comprises: When the local processing mode and the remote processing mode are used simultaneously, and when any one of the local processing mode and the remote processing mode obtains the calculation result, the other one of the local processing mode and the remote processing mode is terminated.

5. The method according to claim 1, characterized in that Obtaining the calculation result of the target calculation task according to the processing mode includes: Determining a solver corresponding to the processing mode and optimization parameters corresponding to the solver, wherein the optimization parameters are used to control the behavior of the solver; The optimization parameters are used to control the solver to obtain the calculation results.

6. The method according to claim 1, characterized in that A graphical user interface is provided through a terminal device, wherein the content displayed by the graphical user interface at least partially includes a computing task solution scenario, and the method further includes: displaying a plurality of candidate optimization models in the graphical user interface; In response to a first control operation applied to the graphical user interface, determining a target optimization model from the plurality of candidate optimization models; In response to a second control operation acting on the target optimization model, setting an optimization parameter associated with the target optimization model; In response to a third control operation acting on the target control, the calculation result is obtained based on the target optimization model and the optimization parameters, and the calculation result is displayed in the graphical user interface.

7. The method according to claim 6, characterized in that The method further comprises: In response to a fourth control operation acting on the optimization parameter, adjusting parameter content of the optimization parameter to obtain an adjustment result; Based on the adjustment result, the calculation result is updated.

8. A method for processing a computing task, characterized in that include: Obtaining an e-commerce traffic allocation task, wherein the e-commerce traffic allocation task is used to solve the e-commerce traffic allocation problem; Identify the e-commerce traffic allocation task and determine a target optimization model corresponding to the e-commerce traffic allocation task, wherein the target optimization model is used to model the e-commerce traffic allocation task as a target form to be solved; Acquire characteristic information of the target optimization model, wherein the characteristic information is used to characterize the calculation content corresponding to the e-commerce traffic allocation task; Based on the characteristic information, determining problem structure information of the e-commerce traffic allocation problem; Based on the problem structure information, determining the amount of computing resources for the e-commerce traffic allocation task; Based on the computing resource amount, determining a processing mode of the e-commerce traffic allocation task, wherein the processing mode includes one of the following three modes: a local processing mode, a remote processing mode, and using both the local processing mode and the remote processing mode; The e-commerce traffic allocation result of the e-commerce traffic allocation task is obtained according to the processing mode.

9. The method according to claim 8, characterized in that A graphical user interface is provided through a terminal device, wherein the content displayed by the graphical user interface at least partially includes an e-commerce traffic allocation scenario, and the method further includes: Displaying a plurality of candidate e-commerce traffic allocation models in the graphical user interface; In response to a first control operation acting on the graphical user interface, determining a target e-commerce traffic allocation model from the plurality of candidate e-commerce traffic allocation models; In response to a second control operation acting on the target e-commerce traffic allocation model, setting an e-commerce traffic allocation parameter associated with the target e-commerce traffic allocation model; In response to a third control operation acting on a target control, the e-commerce traffic allocation result is obtained based on the target e-commerce traffic allocation model and the e-commerce traffic allocation parameter, and the e-commerce traffic allocation result is displayed in the graphical user interface.

10. A method for processing a computing task, characterized in that include: Acquire a power dispatching task, wherein the power dispatching task is used to solve a power dispatching problem; Identifying the power dispatching task and determining a target optimization model corresponding to the power dispatching task, wherein the target optimization model is used to model the power dispatching task as a target form to be solved; Acquiring characteristic information of the target optimization model, wherein the characteristic information is used to characterize the calculation content corresponding to the power dispatching task; Based on the characteristic information, determining problem structure information of the power dispatching problem; Based on the problem structure information, determining the amount of computing resources for the power dispatching task; Based on the amount of computing resources, determining a processing mode of the power dispatching task, wherein the processing mode includes one of the following three modes: a local processing mode, a remote processing mode, and using both the local processing mode and the remote processing mode; The power dispatching result of the power dispatching task is obtained according to the processing mode.

11. A system for processing computing tasks, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Step 1: obtaining a target computing task, wherein the target computing task is used to solve a target problem; Step 2: Identify the target computing task and determine the target optimization model corresponding to the target computing task, wherein the target optimization model is used to model the target computing task as a target form to be solved; obtain feature information of the target optimization model, wherein the feature information is used to characterize the computing content corresponding to the target computing task; determine the problem structure information of the target problem based on the feature information; Step 3: determining the amount of computing resources for the target computing task based on the problem structure information; determining a processing mode for the target computing task based on the amount of computing resources, wherein the processing mode includes one of the following three modes: a local processing mode, a remote processing mode, or using both the local processing mode and the remote processing mode; Step 4: Obtain the calculation result of the target calculation task according to the processing mode.

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