CN114327839A

CN114327839A - Task optimization method and system

Info

Publication number: CN114327839A
Application number: CN202210238994.5A
Authority: CN
Inventors: 赵亮; 黄国凌; 印卧涛; 吴悠; 王峰; 蒋虎; 沈洋斌
Original assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Current assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Priority date: 2022-03-11
Filing date: 2022-03-11
Publication date: 2022-04-12
Anticipated expiration: 2042-03-11
Also published as: CN114327839B

Abstract

The embodiment of the specification provides a task optimization method and a task optimization system, wherein the task optimization method comprises the following steps: receiving a task optimization request of a task to be optimized, and acquiring optimization demand data based on a task scheduling rule carried in the task optimization request; executing an optimization modeling task based on the optimization demand data, and generating an optimization task model; and determining a target optimization result meeting the task to be optimized based on the optimization task model so as to realize automatic modeling processing of the task to be optimized without repeated modeling according to different application requirements, thereby not only avoiding the problem of large amount of computing resource consumption, but also improving the processing efficiency of the optimization problem.

Description

Task optimization method and system

Technical Field

The embodiment of the specification relates to the technical field of computers, in particular to a task optimization method and a power resource optimization method.

Background

The optimization problem has wide application in many fields such as energy and electricity, electronic commerce, supply chain, cloud computing, finance, manufacturing industry and the like. Currently, solving an actual optimization problem usually includes two steps of optimization modeling and solution. With the characteristics of many professional fields, huge data volume, various data types and the like, the modeling output by conventional modeling software cannot obtain a better optimization result, and the modeling needs to be continuously optimized according to different parameters corresponding to the optimization problem so as to adapt to the actual application requirements.

Disclosure of Invention

In view of this, the embodiments of the present specification provide a task optimization method. One or more embodiments of the present specification also relate to a power resource optimization method, a task optimization system, a scheduler, a computing device, a computer-readable storage medium, and a computer program, which solve the technical problems of the prior art.

According to a first aspect of embodiments of the present specification, there is provided a task optimization method, including:

receiving a task optimization request of a task to be optimized, and acquiring optimization demand data based on a task scheduling rule carried in the task optimization request;

executing an optimization modeling task based on the optimization demand data, and generating an optimization task model;

and determining a target optimization result meeting the task to be optimized based on the optimization task model.

According to a second aspect of embodiments herein, there is provided a power resource optimization method, including:

receiving a task optimization request of a power resource optimization task, and acquiring optimization demand data based on a power resource scheduling rule carried in the task optimization request;

executing a power resource optimization modeling task based on the optimization demand data, and generating a power resource optimization task model;

and determining a target optimization result meeting the power resource optimization task based on the power resource optimization task model.

According to a third aspect of embodiments herein, there is provided a task optimization system, the task optimization system comprising a development module, a production cloud module, wherein the production cloud module comprises a cloud scheduling component, a cloud modeling component, a cloud solver,

the cloud scheduling component is configured to receive a task optimization request of a task to be optimized, which is sent by the development module, acquire optimization demand data based on a task scheduling rule carried in the task optimization request, and send a modeling control instruction to the cloud modeling component;

the cloud modeling component is configured to respond to the modeling control instruction, execute an optimization modeling task based on the optimization demand data and generate an optimization task model;

the cloud solver is configured to respond to an optimization solving instruction sent by the cloud scheduling component, and determine a target optimization result meeting the task to be optimized based on the optimization task model.

According to a fourth aspect of embodiments herein, there is provided a scheduler comprising:

the data acquisition module is configured to receive a task optimization request of a task to be optimized and acquire optimization demand data based on a task scheduling rule carried in the task optimization request;

a model generation module configured to execute an optimization modeling task based on the optimization demand data and generate an optimization task model;

and the optimization result determining module is configured to determine a target optimization result meeting the task to be optimized based on the optimization task model.

According to a fifth aspect of embodiments herein, there is provided a computing device comprising:

a memory and a processor;

the memory is configured to store computer-executable instructions and the processor is configured to execute the computer-executable instructions, which when executed by the processor implement the steps of the task optimization method described above.

According to a sixth aspect of embodiments herein, there is provided a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the task optimization method described above.

According to a seventh aspect of embodiments herein, there is provided a computer program, wherein when the computer program is executed in a computer, the computer is caused to perform the steps of the task optimization method described above.

One embodiment of the present specification provides a task optimization method, which receives a task optimization request of a task to be optimized, and obtains optimization demand data based on a task scheduling rule carried in the task optimization request; executing an optimization modeling task based on the optimization demand data, and generating an optimization task model; and determining a target optimization result meeting the task to be optimized based on the optimization task model.

Specifically, according to a task scheduling rule carried in a task optimization request, optimization demand data required by modeling is automatically acquired, a modeling task is executed, an optimization task model is generated, further, a target optimization result of the task to be optimized is determined, automatic modeling processing of the task to be optimized is achieved, repeated modeling according to different application requirements is not needed, the problem of consumption of a large amount of computing resources is avoided, and the processing efficiency of optimization problems can be improved.

Drawings

FIG. 1 is a system architecture diagram of a task optimization system provided in an embodiment of the present disclosure;

fig. 2 is a flowchart of a task optimization method provided in an embodiment of the present specification:

FIG. 3 is a flow chart of a scheduler process of a task optimization method according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a task optimization system for automated optimization process-centric production cloud modeling according to an embodiment of the present disclosure;

FIG. 5 is a flow chart of a power resource optimization method provided by an embodiment of the present specification;

fig. 6 is a schematic structural diagram of a scheduler provided in an embodiment of the present specification;

fig. 7 is a block diagram of a computing device according to an embodiment of the present disclosure.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, as those skilled in the art will be able to make and use the present disclosure without departing from the spirit and scope of the present disclosure.

The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in one or more embodiments of the present specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It will be understood that, although the terms first, second, etc. may be used herein in one or more embodiments to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first can also be referred to as a second and, similarly, a second can also be referred to as a first without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

First, the noun terms to which one or more embodiments of the present specification relate are explained.

Algebraic Modeling Language (AML): and converting the complex optimization model into an abstract algebraic expression form, inputting the abstract algebraic expression form in a script program, and connecting each optimization solver.

Optimizing a solver: software for solving optimization problems like mindopot, Gurobi, CPLEX, etc.

IDE (integrated development environment): an editor that provides a user interface.

Modeling tools: and integrating an algebraic modeling language, an optimization solver and an IDE.

Cloud-native architecture: the whole set of scheme is deployed and executed on the cloud, and a user does not need to perform any installation on a local computer.

The optimization problem has wide application in many fields such as energy and electricity, electronic commerce, supply chain, cloud computing, finance, manufacturing industry and the like. Solving the actual optimization problem often includes two steps of optimization modeling and solution. The optimization solver is main software for solving the optimization problem, for example, mindopot, Gurobi, SCIP and the like provide rich interfaces for establishing model solution, however, the process of inputting the optimization model through the program interface is not intuitive, a lot of time and energy are consumed by a user, and errors are easy to occur.

In order to simplify the step, an algebraic modeling language software application is generated, and the purpose is to input a complex optimization model in an algebraic expression form, so that a series of complex operations such as calling a program interface and the like are avoided, and a user can concentrate on the establishment of the optimization model. The modeling language does not always solve the actual problem, but outputs the model in a file form, and then calls each optimization solver to solve according to the instruction, so that the development process of the optimization model is accelerated, and the possibility of model input errors is effectively reduced.

Therefore, the algebraic modeling language software and the optimization solver are important components for applying the optimization technology. A complete set of modeling tools generally includes the algebraic modeling language SDK, the optimization solver SDK, and the IDE with GUI, such as AMPL, GAMS, AIMMS, etc. At present, most of the schemes adopt a traditional software mode of local installation and execution, and are usually concentrated on the editing and running of a modeling script, so that the series connection between a plurality of optimization modeling solving tasks and data possibly involved in the industrial application and deployment process, the scheduling, operation and maintenance of a production environment and the like are omitted. As the cloud of infrastructure and data becomes a great trend, and the optimization problem in the actual project becomes more and more complex, the data scale becomes larger and larger, and the advantages of the cloud-native optimization modeling tool in the aspects of development and production, multi-person cooperation, data transmission and processing, application deployment and the like become more and more prominent. Correspondingly, the current scheme can upload a locally edited modeling language script (through Kestrel interface) or an MPS file to an NEOS Server for solving, is simple, but cannot be debugged on line; or only having the on-cloud IDE, the method can edit and solve the model, view the result and the like, and most of the above schemes focus on providing the on-cloud development environment which does not need to be installed locally for the user, namely, the modeling language software, the optimization solver, the IDE and the like are deployed on the cloud, but the method lacks a production environment construction and optimization operation scheduling tool on the cloud.

Based on the scheme, the production cloud of the modeling tool on the cloud is provided, and the scheme of optimizing the modeling tool by taking a scheduling process as a center is a whole set of solution for applying an optimization technology to production on the cloud; the method has the advantages that a user can set scheduling and operation rules of a production environment after developing and testing an optimization model by a modeling tool on the cloud, and carry out operation and maintenance and the like, so that the production cloud constructs a set of automatic operation flow, wherein each key node is connected and driven by data; it should be noted that the scheduler for optimizing modeling and the solution for automatically optimizing the flow provided by the present disclosure include, but are not limited to, a supply chain scheduling application scenario, an e-commerce scheduling application scenario, and the like, where, taking e-commerce commodity supply scheduling optimization as an example, assuming that the demand for a certain commodity is M, N warehouses may provide the commodity, and for the problem how to schedule the commodity from the N warehouses to meet the demand of M, the scheduling scheme may be provided by using the optimization task model provided in the embodiment of the present disclosure, so as to implement optimization processing on commodity scheduling of each warehouse.

In the present specification, a task optimization method is provided, and the present specification also relates to a power resource optimization method, a task optimization system, a scheduler, a computing device, and a computer-readable storage medium, a computer program, which are described in detail one by one in the following embodiments.

Referring to fig. 1, fig. 1 is a schematic system structure diagram of a task optimization system according to an embodiment of the present disclosure.

Fig. 1 includes a development module a and a production cloud module B, where the development module a includes initial modeling data, and the production cloud module B includes a cloud scheduling component, a cloud modeling component, and a cloud solver.

It should be noted that the development module a can be understood as an integrated development environment, where the integrated development environment includes initial modeling data to implement processing of a modeling language, optimize development components such as a solver, and connect and call between the components; the production cloud module B can be understood as a production environment which is isolated from the development environment and deployed at the cloud end, mainly supports automatic process construction, and performs automatic optimization processing on the modeling task determined in the development environment; wherein, the method is isolated from the development environment to ensure the data and flow safety in the production environment.

Specifically, the embodiment of the specification provides a task optimization system, which comprises a development module and a production cloud module, wherein the production cloud module comprises a cloud scheduling component, a cloud modeling component and a cloud solver,

the cloud scheduling component is configured to receive a task optimization request of a task to be optimized, which is determined by the development module, acquire optimization demand data based on a task scheduling rule carried in the task optimization request, and send a modeling control instruction to the cloud modeling component;

The cloud scheduling component can be understood as a scheduler in a production environment, the start, operation, output and the like of each task to be optimized issued to the production cloud module are uniformly controlled by the scheduler, a core module for implementing an automatic process is ensured, and data, the cloud modeling component and the cloud solver are controlled mainly in an API (application programming interface) calling mode.

The task to be optimized can be understood as an optimized modeling task for the initial modeling data; for example, in an application scenario of warehouse goods scheduling, the initial modeling data may be understood as an initial mathematical model created to obtain data of warehouse-out and warehouse-in of each warehouse determined to meet scheduling requirements, and the corresponding optimized modeling task may be understood as a modeling task optimized again for the initial data model.

The task scheduling rule can be understood as a series rule from a modeling language to a specific execution required by a solver in the process of executing the optimization modeling to obtain a target optimization result, and can also be understood as a composition rule of a specific execution process of the optimization modeling.

The optimization task model can be understood as a model which is further optimized according to task requirements based on initial modeling data, and in practical application, the optimized model can be a mathematical model determined in a form of algebraic function expression.

The target optimization result may be understood as a result of a variable solved by the optimization task model, for example, in an application scenario of warehouse goods scheduling, the target optimization result is a data result of warehouse exit and warehouse entry of each warehouse determined to meet a scheduling requirement.

During specific implementation, a development user establishes initial modeling data for task requirements in a development module A, determines a corresponding task to be optimized according to the initial modeling data, and sends a task optimization request corresponding to the task to be optimized to a cloud scheduling component in a production cloud module B; and further, the cloud scheduling component acquires optimization demand data according to a task scheduling rule carried in the task optimization request and sends a modeling control instruction to the cloud modeling component, the cloud modeling component carries out automatic modeling on the task optimization request and generates an optimization task model, further, the cloud scheduling component can also send an optimization solving instruction to the cloud solver, and the cloud solver is controlled to respond to the optimization solving instruction to process the optimization task model, so that a target optimization result meeting the task to be optimized is determined.

In practical application, a scheduler in a production cloud module automatically controls a process of modeling and solving for a task to be optimized in a mode of issuing a control instruction, and it should be noted that the scheduler controls the series connection of modeling and solving according to a task scheduling rule carried in a received task optimization request.

The task optimization system provided by the embodiment of the description schedules the operation of each component, such as modeling and solving the task to be optimized, based on the cloud scheduling component in the cloud production environment by establishing the cloud production environment isolated from the development environment, so that the optimization operation can be automatically executed in the production environment after the whole modeling and solving link is developed.

Further, the production cloud module also comprises a cloud database,

the cloud scheduling component is configured to record an optimization log of the task to be optimized, store the optimization log to the cloud database, and send a data storage instruction to the cloud solver;

the cloud solver is configured to respond to the data storage instruction and store a target optimization result of the task to be optimized to the cloud database.

In practical application, the cloud scheduler can also record an optimization log of a task to be optimized of the optimization model, then store the optimization log into the cloud database, and simultaneously send a data storage instruction to the cloud solver, so that the cloud solver responds to the data storage instruction and stores a target optimization result of the task to be optimized into the cloud database; the cloud database is configured in the production cloud module, so that the cloud modeling component and the cloud solver can be docked, data can be pulled and stored, and the data can be calculated and processed.

The task optimization system provided in the embodiment of the present specification can also record an optimization log of a model optimization process by configuring the cloud database, and store a target optimization result corresponding to a task to be optimized, so that corresponding data can be subsequently pulled from the cloud database for data analysis.

Further, the production cloud module further comprises a management component,

the management component is configured to receive an optimization result query request aiming at the task to be optimized, and pull a target optimization result of the task to be optimized from the cloud database based on the optimization result query request;

the management component is further configured to adjust an optimization task model corresponding to the task to be optimized based on the target optimization result under the condition that the target optimization result is determined to be empty.

The management component can be understood as a monitoring operation and maintenance module configured in the production cloud module, the operation of the monitoring operation and maintenance module in the production environment is controlled by the cloud scheduling component to operate, and a development user can monitor the operation and maintenance of the operation and check the operation result of each optimized operation.

In practical application, the management component can also receive an optimization result query request of a task to be optimized, and pull a target optimization result corresponding to the task to be optimized from the cloud database according to the optimization result query request, so that the target optimization result is fed back to a user interface corresponding to a development user to be displayed, and scheduling problems in practical application are solved; furthermore, the development user can modify parameters in the optimization task model or modify task scheduling rules and the like based on the target optimization result; in addition, when the development user configures the production cloud module at the cloud end, the output interface of the whole production cloud module is configured in the management assembly, so that the resource configuration assembly in the system is saved.

The management component may also be configured to determine that the received target optimization result may be empty, and then, when the development user determines that the target optimization result of the task to be optimized is empty, the development user may further adjust an optimization task model corresponding to the task to be optimized based on the target optimization result, and when the development user specifically implements, the development user may determine a parameter for adjusting the optimization task model according to specific content of the target optimization result, so as to further optimize the task model.

It should be noted that, if the target optimization result is null, the case of no solution caused by constraint condition conflict in the optimization task model may be understood, or the case of no solution caused by other conditions that cause the task optimization model to solve is understood, which is not specifically limited in the embodiment of the present specification.

The task optimization system provided in the embodiment of the present specification monitors, operates and maintains a modeling solving process in a production cloud module by configuring a management component, and can query a target optimization result corresponding to a task to be optimized for a development user, and further, can adjust an optimization task model again so as to provide analysis and application for follow-up.

In addition, it should be noted that, in the task optimization system, an authority control component may be further configured to implement authority control on the development user, so as to be suitable for a process of jointly developing an optimization task model in the production cloud module by a plurality of development users, which is not specifically limited in this embodiment.

In summary, in the task optimization system provided in the embodiment of the present specification, a scheduler for optimizing modeling is configured on a cloud, and is isolated from a development environment of a development user, so as to ensure data and process safety in a production environment; and then, the cloud modeling component and the cloud solver configured in the production cloud module are controlled to perform optimization modeling processing based on the scheduler, so that not only can an on-cloud development environment which does not need local installation be provided for a development user, but also an automatic optimization solving task can be realized by deploying the scheduler for optimizing operation.

Referring to fig. 2, fig. 2 is a flowchart illustrating a task optimization method according to an embodiment of the present disclosure, which specifically includes the following steps.

It should be noted that, the task optimization method provided in this embodiment is described by taking a cloud scheduling component (scheduler) applied to the task optimization system as an example, but a specific application scenario is not limited; that is, the start, operation, output, etc. of each optimized job task issued to production are uniformly controlled by a scheduler, and the optimized job task is a core module for ensuring the implementation of an automation flow.

Step 202: receiving a task optimization request of a task to be optimized, and acquiring optimization demand data based on a task scheduling rule carried in the task optimization request.

In practical application, after receiving a task optimization request for a task to be optimized, a scheduler may obtain optimization demand data corresponding to the task to be optimized according to a task scheduling rule carried in the task request to be optimized, where the task scheduling rule may be understood as a rule in the above embodiment, and will not be described in detail herein.

Further, the receiving a task optimization request of a task to be optimized includes:

receiving a task optimization request corresponding to a task to be optimized and generated based on initial modeling data, wherein the initial modeling data is generated based on initial development conditions.

The initial development condition can be understood as a development environment of a development user, the development user needs to develop and debug the link for optimizing modeling and solving in the development environment to form a complete operation link, the link can contain serial solving or parallel solving of a plurality of problems, and a data pulling mode in a cloud database needs to be determined.

In practical application, in a received task optimization request corresponding to a task to be optimized, which is sent by a development user, a scheduler generates the task to be optimized based on initial modeling data in a development environment, wherein the initial modeling data can be understood as an algebraic model which is created by the development user in advance according to task requirements, the initial modeling data is created in the development environment, and it can be ensured that no matter how the initial modeling data is optimized subsequently, the content of the initial modeling data cannot be influenced due to damage or loss of cloud data.

In the task optimization method provided in the embodiment of the present specification, the task to be optimized is determined by the initial modeling data in the development environment, so that the subsequent scheduler can automatically process the modeling solution process of the task to be optimized.

In order to realize the automatic optimization processing of the task to be optimized, a development user needs to input command grammar in advance under the condition of sending a scheduling instruction to a scheduler so as to realize the automatic optimization modeling of the subsequent scheduler according to a rule formed by the command grammar; specifically, the acquiring optimization demand data based on the task scheduling rule carried in the task optimization request includes:

analyzing the task scheduling rule carried in the task optimization request, acquiring optimization demand data corresponding to the task optimization request from a cloud database based on the analysis result,

and the task scheduling rule is generated based on the grammar command determined by the task to be optimized.

The syntax command can be understood as a syntax instruction input by a development user for scheduling other components in the production cloud module to work, including but not limited to a model instruction, a solution instruction, and an option instruction (the model instruction can be understood as a process for executing modeling on a model script in a modeling language, the solution instruction can be understood as a process for calling a solver to solve a model after the modeling is finished, and the option instruction can be understood as a selection of the solver).

In practical application, the scheduler may further analyze a task scheduling rule carried in the task optimization request, analyze which grammatical commands in the task scheduling rule are composed, and obtain optimization requirement data required for executing the grammatical commands from the cloud database according to the corresponding grammatical commands determined in the analysis result, where the cloud database may be understood as a database configured in the production cloud module, and refer to the description in the above embodiment, which is not described herein in detail.

In the task optimization method provided in the embodiment of the present specification, the task scheduling rule input by the development user is analyzed, and an automatic optimization process corresponding to the task to be optimized is determined, so that the task to be optimized is automatically modeled and solved in the production cloud module, and a target optimization result corresponding to the task to be optimized is determined, so that the optimized modeling result is quickly determined.

Step 204: and executing an optimization modeling task based on the optimization demand data, and generating an optimization task model.

In practical application, under the condition that the scheduler obtains optimization demand data corresponding to a task to be optimized, the task of optimization modeling can be started to be executed according to the optimization demand data, and an optimization task model is generated; the method comprises the following steps of determining constraint conditions, variable parameters and the like which are possibly required in a mathematical modeling process according to task requirements of a task to be optimized, wherein the specific modeling process is not specifically limited in the embodiment of the specification; it should be noted that the scheduler may send a control instruction to a modeling language component (the cloud modeling component in the above embodiment) to control the cloud modeling component to automatically execute a modeling task, and generate an optimization task model capable of obtaining a target optimization result of a task to be optimized, where the optimization task model is a mathematical model expressed by an algebra, an expression form and a complexity of the mathematical model are determined according to different tasks to be optimized, and a specific optimization task model is not specifically limited in this embodiment.

Furthermore, the scheduler can also set preset scheduling time, and can execute an optimization modeling task to adapt to different production scheduling requirements under the condition of reaching the preset scheduling time point; specifically, the executing an optimization modeling task based on the optimization demand data and generating an optimization task model includes:

and determining preset optimization operation time carried in the task optimization request, executing an optimization modeling task according to the preset optimization operation time based on the optimization demand data, and generating an optimization task model.

In practical application, the scheduler may further determine a preset optimized operation time carried in the optimized task request, where the preset optimized operation time may be understood as an initial operation time of an automatic optimized flow set by a development user in advance according to different requirements, for example, in an application scenario of a home appliance inventory, the development user determines that the production scheduling is started only at 00:00 in the early morning of each day according to the actual application requirements, and then the preset optimized operation time point is 00:00 of each day, so that the scheduler is started only at 00:00 of each day to control the optimized modeling task to be executed according to the optimized requirement data, and generate an optimized task model.

According to the task optimization method provided by the embodiment of the specification, the user-defined optimization operation time is set, the optimization modeling task can be executed according to the preset optimization operation time, so that an optimization task model corresponding to the task to be optimized is generated, and the production requirement under the task application scene can be met.

In addition, after the scheduler generates an optimization task model corresponding to the task to be optimized, a certain optimization period can be determined so as to realize periodic operation; specifically, after the generating of the optimization task model, the method further includes:

updating the optimization task model based on a predetermined optimization period.

In practical application, the scheduler may further continuously update the optimized task model according to a predetermined optimization period, where it should be noted that the predetermined optimization period is determined by the scheduler itself, for example, the scheduler may set to schedule once every 15 minutes, once every 6 hours, and the like according to task requirements, and this embodiment is not specifically limited to this; in a specific production application scenario, the scheduling amount of each warehouse of the household electrical appliance products in a short time (for example, within ten minutes) is not very large, and for the optimization task model in such a scenario, the determined preset optimization cycle time may be long (for example, scheduling once every 12 hours); for the optimization task model in this scenario, the predetermined optimization cycle time may be short (for example, scheduled once every 1 hour), because the adjustment amount of each warehouse for the food product may be large in a short time (for example, within ten minutes).

Therefore, the scheduler needs to continuously update the optimization task model according to the preset optimization period, a new optimization model does not need to be continuously established, and the waste of computing resources is caused.

Step 206: and determining a target optimization result meeting the task to be optimized based on the optimization task model.

In practical application, in the process of determining a target optimization result of an optimization task model, a scheduler can utilize a cloud solver configured in a production cloud module to calculate, and because the data volume is large and the value of a variable is also large in a production environment, manual calculation of the optimization task model cannot be realized to obtain a better solution of the algebraic optimization task model, so that the cloud scheduler (the optimization solver) is required to be used for realizing the butt joint with a cloud modeling component to calculate the optimization task model to obtain the target optimization result meeting the task to be optimized.

Further, the optimization task model comprises n sub-optimization task models, wherein n is greater than or equal to 1 and is a positive integer,

correspondingly, the determining, based on the optimization task model, a target optimization result satisfying the task to be optimized includes:

and calling a preset solver, and controlling the preset solver to process each sub-optimization task model to obtain a target optimization result meeting the task to be optimized.

In practical application, because the calculation process of the optimization task model may be complex and needs a process of solving for many times, the optimization task model may be composed of n sub-optimization task models, where n is greater than or equal to 1 and is a value of a positive integer; for example, the optimization task model may be composed of 3 sub-optimization task models, and then it means that the optimization task model needs to be calculated 3 times, and it may be the case that a result of the first calculation may be a basis (understood as a constant) of the calculation processes of the last two times, and further, after the scheduler calls a preset solver (cloud solver), the scheduler may control the preset solution to calculate each sub-optimization task model, and finally obtain a target optimization result that meets the criteria to be optimized.

The task optimization method provided by the embodiment of the specification can also solve each sub-optimization task model by calling a preset solver so as to accurately determine the target optimization result of the task to be optimized.

In addition, the scheduler can also record logs in the process of establishing an optimization task model, and simultaneously store the recorded log data and the determined target optimization result of the task to be optimized into a cloud database, so that the subsequent monitoring operation and maintenance can carry out data analysis; specifically, after determining the target optimization result satisfying the task to be optimized based on the optimization task model, the method further includes:

and recording an optimization log of the task to be optimized, and storing the optimization log and the target optimization result to the cloud database.

In practical applications, the scheduler may record an optimization log of a task to be optimized, and simultaneously store both the optimization log and a target optimization result in the cloud database, and the specific recording and storing process may refer to the description in the foregoing embodiments, which is not limited herein.

In the task optimization method provided in the embodiment of the present specification, the scheduler records the corresponding optimization log, and stores the optimization log and the target optimization result in the cloud database, so as to subsequently pull corresponding data from the cloud database for monitoring, operation and maintenance management.

Fig. 3, which is described below with reference to fig. 3, illustrates a flow chart of a scheduler process of a task optimization method provided in an embodiment of the present specification, and specifically includes the following steps.

Step 302: the scheduler determines whether the current time reaches the periodic scheduling data, if yes, step 304 is executed, and if no, the determination is restarted.

Step 304: the scheduler further needs to determine whether the preamble data is generated, if so, step 306 is executed, and if not, step 302 is executed.

Specifically, the basic data may be understood as the basic data that needs to establish the optimization task model, and the basic data may be understood as the basic data that establishes the optimization task model before that, for example, in the present optimization task of the inventory data, the basic data is the historical inventory data or the historical demand, etc.

Step 306: and the scheduler acquires the optimization model data corresponding to the task to be optimized from the database.

Step 308: and the scheduler acquires a scheduling rule corresponding to the task to be optimized.

Specifically, the scheduling rule may be understood as a scheduling rule that controls a subsequent automatic modeling and solving process.

Step 310: and the scheduler executes the modeling task through the modeling language API to generate a model file.

Step 312: the scheduler executes the solution task through the solver API.

Step 314: the scheduler determines whether there are other (serialized/looped) modeling solution tasks, if so, continues to perform step 310, and if not, performs step 316.

Step 316: and the scheduler stores the output optimization result and the log data to a database.

In the task optimization method provided by the embodiment of the specification, the scheduler can realize unified responsible control on starting, running, outputting and the like of the optimized job tasks issued to production so as to ensure the realization of an automatic flow, and the scheduler in the production cloud schedules and guides the running of modeling and solving and connects a plurality of optimized solving tasks in series so as to form a systematic solution.

The following description further describes the task optimization system with reference to fig. 4 by taking an application of the task optimization system provided in this specification in a cloud native architecture as an example. Fig. 4 is a schematic diagram illustrating a production cloud modeling process centered on an automated optimization process by a task optimization system according to an embodiment of the present specification.

Fig. 4 includes three main parts, namely a development module a, a production cloud module B, and a development user C, wherein the development module a can be understood as a development environment of the development user, which is isolated from the production cloud module B, and includes a database, a modeling language, and an optimization solver to generate initial modeling data (an initial modeling basis of a subsequent job optimization task is established by the development user in advance, but does not satisfy an actual optimization requirement).

In practical application, after the development environment is debugged, the development user C may issue the job to the production cloud module, and set scheduling operation parameters in the production cloud, where the scheduling parameters include, but are not limited to, data, modeling, a link for optimizing solution, a trigger time and mechanism, and rerun data, and then send a job scheduling instruction to a cloud scheduling component in the production cloud module B, and after receiving the job scheduling instruction, the cloud scheduling component starts issuing a control instruction in a data-driven manner, and the scheduler may always check whether preamble data (which may be understood as data stored in a database at the previous time required for modeling, such as stock and demand of yesterday) is generated, and if the preamble data is generated, may control to pull data corresponding to the job task to the database in the production cloud module, and input a modeling language component (a modeling analysis script for data modeling, wherein, the modeling script can adopt a language of a mathematical class), controlling and executing an optimization modeling task, and outputting an optimization model file.

Further, the cloud scheduling component inputs the optimized parameters into the scheduling interface to control the optimization solver to solve the optimized model file (the optimization solver can perform modeling solution on the optimized model file for multiple times through a link), and then outputs a solved result, which is the optimized result in fig. 4, and meanwhile, the cloud scheduling component can record corresponding log data in the process of modeling solution in the production cloud module B, and transfer the log data and the optimized result to the database.

In addition, the development user can also issue an operation result analysis request to the monitoring operation and maintenance component in the production cloud module B, so that the monitoring operation and maintenance component can obtain the optimization result of the optimization operation from the database, generate a visual chart and return the visual chart to the development user, and subsequent data analysis is facilitated. Meanwhile, the production cloud module B also comprises a permission management and control component for managing and controlling task operation and user permission setting.

In the task optimization system provided by the embodiment of the specification, a scheduler for optimizing modeling is configured on a cloud, and meanwhile, the task optimization system is isolated from a development environment of a development user so as to ensure data and process safety in a production environment; and then, the cloud modeling component and the cloud solver configured in the production cloud module are controlled to perform optimization modeling processing based on the scheduler, so that not only can an on-cloud development environment which does not need local installation be provided for a development user, but also an automatic optimization solving task can be realized by deploying the scheduler for optimizing operation.

In addition, referring to fig. 5, fig. 5 shows that the present specification further provides another embodiment, namely, a power resource optimization method, which specifically includes the following steps.

It should be noted that the task optimization method provided in the foregoing embodiment of the present specification may also be applied to a power resource optimization scenario to adapt to power resource allocation in the power scenario, and the power resource optimization method may be applied to the cloud scheduling component in the production cloud module configured in the foregoing embodiment.

Step 502: receiving a task optimization request of a power resource optimization task, and acquiring optimization demand data based on a power resource scheduling rule carried in the task optimization request.

The power resource optimization task may be understood as an optimization task for power resource allocation, for example, if the historical total amount of power resources allocated from the place a is a, the historical total amount of power resources allocated from the place B is B, and the historical total amount of power resources allocated from the place C is C, then on the basis, the power resource allocation amount from the place A, B, C is optimized, and a target optimization result may be determined according to task requirements as follows: the quantity of the power resources distributed from the site A is B, the quantity of the power resources distributed from the site B is C, and the quantity of the power resources distributed from the site C is a.

In practical application, after receiving a task optimization request for a power resource optimization task, a cloud scheduling component may obtain optimization demand data corresponding to the task to be optimized according to a power resource scheduling rule carried in the optimization task request, where the power resource scheduling rule may be understood as the task scheduling rule in the above embodiment, and will not be described in detail herein.

Further, the acquiring of the optimization demand data based on the power resource scheduling rule carried in the task optimization request includes:

analyzing the power resource scheduling rule carried in the task optimization request, acquiring optimization demand data corresponding to the power resource optimization task from a cloud database based on the analysis result,

and the power resource scheduling rule is generated based on a grammar command determined by the power resource optimization task.

In practical application, the cloud scheduling component may further analyze the power resource scheduling rule, analyze which syntax commands are generated, and obtain, from the cloud database, optimization demand data required for executing the syntax commands according to the corresponding syntax commands determined in the analysis result, where the syntax commands may refer to the description of the above embodiment, and are not described herein in detail.

Step 504: and executing a power resource optimization modeling task based on the optimization demand data, and generating a power resource optimization task model.

In practical application, after receiving the optimization demand data, the cloud scheduling component may execute an optimization modeling task for solving power resource optimization allocation, and further generate a power resource optimization task model, where specific types of the power resource optimization task model may refer to the description of the optimization task model in the above embodiments, and are not described in detail here.

Step 506: and determining a target optimization result meeting the power resource optimization task based on the power resource optimization task model.

In practical application, the cloud scheduling component also utilizes a cloud solver configured in the production cloud module to perform solution calculation on the power resource optimization task model so as to obtain a target optimization result meeting the power resource optimization task.

According to the power resource optimization method provided by the embodiment of the specification, the cloud scheduling component can be used for uniformly controlling the starting, running, outputting and the like of the tasks of optimizing operation issued to production so as to ensure the realization of an automatic process of power resource allocation, the cloud scheduling component in the production cloud is used for scheduling and guiding the running of modeling and solving, and a plurality of tasks of optimizing solution are connected in series so as to form a scheme for systematically solving the scheduling of power resources.

Corresponding to the above method embodiments, the present specification further provides a scheduler embodiment, and fig. 6 shows a schematic structural diagram of a scheduler provided in an embodiment of the present specification. As shown in fig. 6, the scheduler includes:

a data obtaining module 602, configured to receive a task optimization request of a task to be optimized, and obtain optimization demand data based on a task scheduling rule carried in the task optimization request;

a model generation module 604 configured to execute an optimization modeling task based on the optimization requirement data and generate an optimization task model;

an optimization result determination module 606 configured to determine a target optimization result satisfying the task to be optimized based on the optimization task model.

Optionally, the data obtaining module 602 is further configured to:

Optionally, the model generation module 604 is further configured to:

Optionally, the optimization task model comprises n sub-optimization task models, where n is greater than or equal to 1 and n is a positive integer,

optionally, the optimization result determining module 606 is further configured to:

Optionally, the apparatus further comprises:

and the data storage module is configured to record an optimization log of the task to be optimized and store the optimization log and the target optimization result to the cloud database.

Optionally, the data obtaining module 602 is configured to:

The scheduler provided in the embodiment of the present description automatically obtains optimization demand data required for modeling, executes a modeling task, and generates an optimization task model according to a task scheduling rule carried in a task optimization request, thereby determining a target optimization result of the task to be optimized, implementing automatic modeling processing on the task to be optimized, and avoiding repeated modeling according to different application requirements, thereby not only avoiding the problem of large amount of computing resource consumption, but also improving the processing efficiency of the optimization problem.

The above is an exemplary scheme of a scheduler of the present embodiment. It should be noted that the technical solution of the scheduler and the technical solution of the task optimization method described above belong to the same concept, and details that are not described in detail in the technical solution of the scheduler can be referred to the description of the technical solution of the task optimization method described above.

FIG. 7 illustrates a block diagram of a computing device 700 provided in accordance with one embodiment of the present description. The components of the computing device 700 include, but are not limited to, memory 710 and a processor 720. Processor 720 is coupled to memory 710 via bus 730, and database 750 is used to store data.

Computing device 700 also includes access device 740, access device 740 enabling computing device 700 to communicate via one or more networks 760. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. Access device 740 may include one or more of any type of network interface, e.g., a Network Interface Card (NIC), wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.

In one embodiment of the present description, the above-described components of computing device 700, as well as other components not shown in FIG. 7, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 7 is for purposes of example only and is not limiting as to the scope of the present description. Those skilled in the art may add or replace other components as desired.

Computing device 700 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smartphone), wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 700 may also be a mobile or stationary server.

Wherein the processor 720 is configured to execute computer-executable instructions that, when executed by the processor, implement the steps of the task optimization method described above.

The above is an illustrative scheme of a computing device of the present embodiment. It should be noted that the technical solution of the computing device and the technical solution of the task optimization method described above belong to the same concept, and details that are not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the method described above.

An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described method.

The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the above method belong to the same concept, and details that are not described in detail in the technical solution of the storage medium can be referred to the description of the technical solution of the above method.

An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above method.

The above is an illustrative scheme of a computer program of the present embodiment. It should be noted that the technical solution of the computer program and the technical solution of the above method belong to the same concept, and details that are not described in detail in the technical solution of the computer program can be referred to the description of the technical solution of the above method.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of acts, but those skilled in the art should understand that the present embodiment is not limited by the described acts, because some steps may be performed in other sequences or simultaneously according to the present embodiment. Further, those skilled in the art should also appreciate that the embodiments described in this specification are preferred embodiments and that acts and modules referred to are not necessarily required for an embodiment of the specification.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

The preferred embodiments of the present specification disclosed above are intended only to aid in the description of the specification. Alternative embodiments are not exhaustive and do not limit the invention to the precise embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the embodiments and the practical application, to thereby enable others skilled in the art to best understand and utilize the embodiments. The specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method of task optimization, comprising:

2. The task optimization method according to claim 1, wherein the acquiring optimization demand data based on the task scheduling rule carried in the task optimization request includes:

3. The task optimization method of claim 1, wherein performing an optimization modeling task based on the optimization requirement data and generating an optimization task model comprises:

4. The task optimization method according to claim 3, after generating the optimization task model, further comprising:

5. The task optimization method according to claim 1, wherein the optimization task model comprises n sub-optimization task models, wherein n is greater than or equal to 1 and n is a positive integer,

6. The task optimization method according to claim 2, after determining that the target optimization result of the task to be optimized is satisfied based on the optimization task model, further comprising:

7. A power resource optimization method, comprising:

8. The power resource optimization method according to claim 7, wherein the obtaining of the optimization demand data based on the power resource scheduling rule carried in the task optimization request includes:

9. A task optimization system comprises a development module and a production cloud module, wherein the production cloud module comprises a cloud scheduling component, a cloud modeling component and a cloud solver,

10. The task optimization system of claim 9, the production cloud module further comprising a cloud database,

11. The task optimization system of claim 10, the production cloud module further comprising a management component,

12. A scheduler, comprising:

13. A computing device, comprising:

a memory and a processor;

the memory is configured to store computer-executable instructions and the processor is configured to execute the computer-executable instructions, which when executed by the processor implement the steps of the method of any one of claims 1-6, 7-8.

14. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the steps of the method of any one of claims 1-6, 7-8.