CN114168313A - Computing power dispatching system - Google Patents
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Abstract
The application discloses a computing power scheduling system. Wherein, this system includes: the physical machine equipment is at least used for providing physical machine equipment for the operation of the force calculating unit, and the force calculating unit is used for respectively configuring the charging strategy generation model and the charging network simulation environment into a first target task and a second target task; the user management module is at least used for calling a first number of the physical machine devices according to the target object configuration, wherein the first number is the number of the physical machine devices which can be used at most when the force calculation unit is operated; and the force calculation scheduling module is used for adjusting the second quantity of the physical machine equipment in real time according to the running state of the force calculation unit, wherein the first quantity is greater than the second quantity. The method and the device solve the technical problems of poor real-time performance and low efficiency caused by the fact that the physical machine equipment resources are distributed in a manual mode aiming at the force calculation unit in the related technology.
Description
Technical Field
The application relates to the field of computers, in particular to a computing power scheduling system.
Background
Under the background of a policy that the development of electric vehicles is accelerated to promote the replacement of traditional fuel oil in China, the new energy vehicle industry develops rapidly, and the market demand of the charging pile in the city is strongly supported. The special planning and file of the charging pile are sequentially made in the national and local levels, and the system has an instructive effect on the development of urban charging facilities. As an important strategic measure for relieving energy and environmental pressure and promoting transformation and upgrading of the automobile industry, new energy automobiles are developed in China, and the construction and operation of new energy automobile charging piles are important infrastructure guarantees for supporting the development of the new energy automobiles. The method has the advantages that encouragement policies are continuously issued in the country for several years, related special plans are compiled, the method becomes a large-scale market for applying new energy vehicles in the world at present, the urban charging pile network in the largest world is built, and a charging pile system with balanced development of public piles, special piles and private piles is built.
However, when a learning model or a simulation environment related to a charging strategy of a new energy vehicle runs, corresponding calculation power (the amount of called resources) needs to be provided for the related learning model or the simulation environment, and in the related art, calculation power distribution is often performed in a manual mode, for example, when the simulation environment runs, a fixed number of CPUs are manually configured to execute the simulation task, and obviously, the method has the technical problems of poor real-time performance, manpower waste and low efficiency when physical machine and equipment resources are distributed.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a calculation scheduling system, which is used for at least solving the technical problems of poor real-time performance and low efficiency caused by the fact that physical machine equipment resources are manually allocated for a calculation unit in the related technology.
According to an aspect of an embodiment of the present application, there is provided a computing power scheduling system, including: the physical machine equipment is at least used for providing physical machine equipment for the operation of the force calculation unit, the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, wherein the charging strategy generation model is obtained through a plurality of groups of training data, and each group of data in the plurality of groups of training data comprises: the charging behavior characteristic parameters of the sample object and the labels for identifying the charging price strategies corresponding to the charging behavior characteristic parameters of the sample object are obtained, wherein the charging network simulation environment is used for providing the labels for the charging strategy generation model; the user management module is at least used for calling a first number of the physical machine devices according to the target object configuration, wherein the first number is the number of the physical machine devices which can be used at most when the force calculation unit is operated; and the force calculation scheduling module is used for adjusting the second quantity of the physical machine equipment in real time according to the running state of the force calculation unit, wherein the first quantity is greater than the second quantity.
Optionally, the force calculating units correspond to sandboxes in a one-to-one correspondence, and the sandboxes are used for running the first target task and the second target task.
Optionally, adjusting the second number of the physical machine devices in real time according to the operation state of the force calculating unit includes: acquiring the running state of a first target task, and distributing a third number of physical machine devices corresponding to the first target task to a second target task under the condition that the first target task is in an idle state; and acquiring the running state of the second target task, and distributing a fourth quantity of physical machine equipment corresponding to the second target task to the first target task under the condition that the second target task is in an idle state.
Optionally, the computation scheduling module is further configured to quit the first target task and/or the second target task from the scheduling sequence when the first target task and/or the second target task is finished running, where the scheduling sequence includes a plurality of tasks to be executed and the physical machine devices corresponding to the tasks to be executed.
Optionally, the computing power scheduling module is further configured to determine a third target task according to the priority of each system module when the remaining number corresponding to the physical machine device is less than a predetermined threshold, and suspend the third target task temporarily, where the system module includes: the system comprises a calculation scheduling module, a user management module and a data analysis module.
Optionally, the determining, by the user management module having the highest priority, a third target task according to the priority of each system module, and temporarily suspending the third target task includes: receiving an input instruction of a target object, wherein the input instruction is used for indicating the priority of a calculation scheduling module or a data analysis module; when the input instruction indicates that the priority of the calculation force scheduling module is lower than that of the data analysis module, taking the running task of the calculation force module as a third target task, and temporarily suspending the third target task; and when the input instruction indicates that the priority of the computing power scheduling module is higher than that of the data analysis module, taking the running task of the data analysis module as a third target task, and temporarily suspending the third target task.
Optionally, the physical machine device comprises: a Central Processing Unit (CPU) and a Graphics Processing Unit (GPU).
According to another aspect of the embodiments of the present application, there is also provided a computing power scheduling method, which is applied to a distributed platform, and includes: acquiring the number of currently available physical machine equipment; determining the target number of physical machine equipment required by the force calculating unit; the method comprises the following steps of distributing physical machine equipment of a target quantity required by a force calculation unit to the force calculation unit, wherein the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, the charging strategy generation model is obtained through multiple groups of training data, and each group of data in the multiple groups of training data comprises: the charging system comprises charging behavior characteristic parameters of sample objects and labels used for identifying charging price strategies corresponding to the charging behavior characteristic parameters of the sample objects, wherein the charging network simulation environment is used for providing the labels for charging strategy generation models.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and a device in which the non-volatile storage medium is controlled to execute the computing power scheduling method when the program runs.
According to another aspect of the embodiments of the present application, there is also provided a processor, configured to execute a program, where the program executes the computing power scheduling method.
In this embodiment of the present application, a manner of automatically scheduling hardware resources is adopted, and the physical machine device is at least configured to provide a physical machine device for a power calculating unit to operate, and the power calculating unit is configured to configure a charging policy generation model and a charging network simulation environment as a first target task and a second target task, respectively, where the charging policy generation model is obtained through multiple sets of training data, and each set of data in the multiple sets of training data includes: the charging behavior characteristic parameters of the sample object and the labels for identifying the charging price strategies corresponding to the charging behavior characteristic parameters of the sample object are obtained, wherein the charging network simulation environment is used for providing the labels for the charging strategy generation model; the user management module is at least used for calling a first number of the physical machine devices according to the target object configuration, wherein the first number is the number of the physical machine devices which can be used at most when the force calculation unit is operated; and the computing power scheduling module is used for adjusting the second quantity of the physical machine equipment in real time according to the running state of the computing power unit, wherein the first quantity is greater than the second quantity, so that the technical effect of automatically adjusting the physical machine equipment resources in real time according to the computing power unit is achieved, and the technical problems of poor real-time performance and low efficiency caused by the fact that the computing power unit adopts a manual mode to allocate the physical machine equipment resources in the related technology are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of an alternative computational power scheduling system according to an embodiment of the present application;
fig. 2 is a flowchart illustrating an alternative computational power scheduling method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided an embodiment of a computational power scheduling system, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a computational force scheduling system according to an embodiment of the present application, and as shown in fig. 1, the computational force scheduling system includes:
the physical machine equipment 01 is at least used for providing physical machine equipment for the operation of a force calculation unit, the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, wherein the charging strategy generation model is obtained through a plurality of groups of training data, and each group of data in the plurality of groups of training data comprises: the charging behavior characteristic parameters of the sample object and the labels for identifying the charging price strategies corresponding to the charging behavior characteristic parameters of the sample object are obtained, wherein the charging network simulation environment is used for providing the labels for the charging strategy generation model;
the user management module 02 is at least used for calling a first number of physical machine equipment according to the target object configuration, wherein the first number is the number of the most physical machine equipment which can be used when the force calculation unit is operated;
and the computing power scheduling module 03 is configured to adjust a second number of the physical machine devices in real time according to the operating state of the computing power unit, where the first number is greater than the second number.
In the calculation scheduling system, a physical machine device 01 is at least used for providing a physical machine device for the operation of a calculation unit, the calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, wherein the charging strategy generation model is obtained through a plurality of groups of training data, and each group of data in the plurality of groups of training data comprises: the charging behavior characteristic parameters of the sample object and the labels for identifying the charging price strategies corresponding to the charging behavior characteristic parameters of the sample object are obtained, wherein the charging network simulation environment is used for providing the labels for the charging strategy generation model; the user management module 02 is at least used for calling a first number of physical machine equipment according to the target object configuration, wherein the first number is the number of the most physical machine equipment which can be used when the force calculation unit is operated; the computing power scheduling module 03 is configured to adjust the second number of the physical machine devices in real time according to the operating state of the computing power unit, where the first number is greater than the second number, so as to achieve a technical effect of automatically adjusting the physical machine device resources in real time according to the computing power unit, and further solve technical problems of poor real-time performance and low efficiency caused by manual allocation of the physical machine device resources for the computing power unit in the related art.
It should be noted that the force calculating units are in one-to-one correspondence with the sandboxes, and it is understood that the sandboxes are used for running the first target task and the second target task.
In some embodiments of the present application, the second number of physical machine devices may be adjusted in real time according to the operating state of the force calculating unit, specifically: acquiring the running state of a first target task, and distributing a third number of physical machine devices corresponding to the first target task to a second target task under the condition that the first target task is in an idle state; and acquiring the running state of the second target task, and distributing a fourth quantity of physical machine equipment corresponding to the second target task to the first target task under the condition that the second target task is in an idle state.
In some optional embodiments of the application, the computation scheduling module is further configured to quit the first target task and/or the second target task from the scheduling sequence when the first target task and/or the second target task is finished running, where the scheduling sequence includes a plurality of tasks to be executed and physical machine devices corresponding to the tasks to be executed.
In some embodiments of the present application, the computing power scheduling module is further configured to determine a third target task according to a priority of each system module when the remaining number corresponding to the physical machine device is less than a predetermined threshold, and suspend the third target task temporarily, where the system modules include: the system comprises a calculation scheduling module, a user management module and a data analysis module.
In some optional embodiments of the present application, the user management module has the highest priority, and may determine the third target task according to the priority of each system module, and suspend the third target task temporarily, specifically: receiving an input instruction of a target object, wherein the input instruction is used for indicating the priority of a calculation scheduling module or a data analysis module; when the input instruction indicates that the priority of the calculation force scheduling module is lower than that of the data analysis module, taking the running task of the calculation force module as a third target task, and temporarily suspending the third target task; and when the input instruction indicates that the priority of the computing power scheduling module is higher than that of the data analysis module, taking the running task of the data analysis module as a third target task, and temporarily suspending the third target task.
It should be noted that the physical machine device includes, but is not limited to: a Central Processing Unit (CPU) and a Graphics Processing Unit (GPU).
The computational power scheduling system in some embodiments of the present application may be a distributed computing platform, and specifically, the tasks executed by the computational power scheduling module are respectively completed on different physical machine devices, for example, the training and simulation environments of the charging model are respectively completed on different physical machine devices, or the tasks corresponding to each of the computational power scheduling module, the data analysis module, and the user management module are respectively completed on different physical machine devices. The charging strategy model and the charging network simulation environment are planned into independently executable programs which serve as a force calculating unit capable of being dispatched, the priority of the force calculating unit is distributed by the system, and then a plurality of sandboxes are started for verification of the charging strategy, wherein each sandbox corresponds to one force calculating unit.
The computing power scheduling module is responsible for allocating resources responding to computing power, the computing power unit which has completed work exits from the scheduling sequence, and returns processing results to the upper-layer data analysis module, wherein the processing results include but are not limited to: the convergence condition of the target function and the simulation effect of the trained model. When the computing power resources are insufficient, the computing power scheduling system can automatically (temporarily) move the computing power units with low priority out of the scheduling sequence, and the computing power requirements of events with high priority are guaranteed. The user management module may assign the maximum computational power required, i.e., the maximum number threshold of physical machine devices, to different users.
It can be understood that the computational power scheduling system in the embodiment of the present application has a distributed computing platform, and includes server hardware, a computational power scheduling module, a data analysis module and a user management module, where the charging policy model and the charging network simulation environment are programmed into an independently operable program, which is used as a computational power unit that can be scheduled, and the priority of the computational power unit is allocated by the system, and when the computational power resources are insufficient, the computational power scheduling system will automatically move the computational power unit with a low priority out of the scheduling sequence, so that the computational power requirements of the charging policy model and the simulation environment can be reasonably allocated and optimized, and the use efficiency of the hardware system is improved.
Fig. 2 is a computational power scheduling method according to an embodiment of the present application, which can be applied to a distributed platform, as shown in fig. 2, and includes:
s102, acquiring the number of currently available physical machine devices;
s104, determining the target number of the physical machine equipment required by the force calculating unit;
s106, distributing the physical machine equipment with the target quantity required by the force calculating unit to the force calculating unit, wherein the force calculating unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, the charging strategy generation model is obtained through multiple groups of training data, and each group of data in the multiple groups of training data comprises: the charging system comprises charging behavior characteristic parameters of sample objects and labels used for identifying charging price strategies corresponding to the charging behavior characteristic parameters of the sample objects, wherein the charging network simulation environment is used for providing the labels for charging strategy generation models.
In the calculation power scheduling method, the number of currently available physical machine equipment is obtained; determining the target number of physical machine equipment required by the force calculating unit; the method comprises the following steps of distributing physical machine equipment of a target quantity required by a force calculation unit to the force calculation unit, wherein the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, the charging strategy generation model is obtained through multiple groups of training data, and each group of data in the multiple groups of training data comprises: the charging behavior characteristic parameters of the sample object and the labels used for identifying the charging price strategies corresponding to the charging behavior characteristic parameters of the sample object are provided, wherein the charging network simulation environment is used for providing the labels for the charging strategy generation model, the technical effect of automatically adjusting the physical machine equipment resources in real time according to the power calculating unit is achieved, and the technical problems of poor real-time performance and low efficiency caused by the fact that the power calculating unit is manually allocated to the physical machine equipment resources in the related technology are solved.
In this embodiment of the present application, a manner of automatically scheduling hardware resources is adopted, and the physical machine device is at least configured to provide a physical machine device for a power calculating unit to operate, and the power calculating unit is configured to configure a charging policy generation model and a charging network simulation environment as a first target task and a second target task, respectively, where the charging policy generation model is obtained through multiple sets of training data, and each set of data in the multiple sets of training data includes: the charging behavior characteristic parameters of the sample object and the labels for identifying the charging price strategies corresponding to the charging behavior characteristic parameters of the sample object are obtained, wherein the charging network simulation environment is used for providing the labels for the charging strategy generation model; the user management module is at least used for calling a first number of the physical machine devices according to the target object configuration, wherein the first number is the number of the physical machine devices which can be used at most when the force calculation unit is operated; and the computing power scheduling module is used for adjusting the second quantity of the physical machine equipment in real time according to the running state of the computing power unit, wherein the first quantity is greater than the second quantity, so that the technical effect of automatically adjusting the physical machine equipment resources in real time according to the computing power unit is achieved, and the technical problems of poor real-time performance and low efficiency caused by the fact that the computing power unit adopts a manual mode to allocate the physical machine equipment resources in the related technology are solved.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and a device in which the non-volatile storage medium is controlled to execute the computing power scheduling method when the program runs.
Specifically, the storage medium is used for storing program instructions for executing the following functions, and the following functions are realized:
acquiring the number of currently available physical machine equipment; determining the target number of physical machine equipment required by the force calculating unit; the method comprises the following steps of distributing physical machine equipment of a target quantity required by a force calculation unit to the force calculation unit, wherein the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, the charging strategy generation model is obtained through multiple groups of training data, and each group of data in the multiple groups of training data comprises: the charging system comprises charging behavior characteristic parameters of sample objects and labels used for identifying charging price strategies corresponding to the charging behavior characteristic parameters of the sample objects, wherein the charging network simulation environment is used for providing the labels for charging strategy generation models.
According to another aspect of the embodiments of the present application, there is also provided a processor, configured to execute a program, where the program executes the computing power scheduling method.
Specifically, the processor is configured to call a program instruction in the memory, and implement the following functions:
acquiring the number of currently available physical machine equipment; determining the target number of physical machine equipment required by the force calculating unit; the method comprises the following steps of distributing physical machine equipment of a target quantity required by a force calculation unit to the force calculation unit, wherein the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, the charging strategy generation model is obtained through multiple groups of training data, and each group of data in the multiple groups of training data comprises: the charging system comprises charging behavior characteristic parameters of sample objects and labels used for identifying charging price strategies corresponding to the charging behavior characteristic parameters of the sample objects, wherein the charging network simulation environment is used for providing the labels for charging strategy generation models.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, 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.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A computing power scheduling system, comprising:
the physical machine equipment is at least used for providing physical machine equipment for the operation of a force calculation unit, the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment into a first target task and a second target task, wherein the charging strategy generation model is obtained through a plurality of groups of training data, and each group of data in the plurality of groups of training data comprises: the charging system comprises charging behavior characteristic parameters of a sample object and a label used for identifying a charging price strategy corresponding to the charging behavior characteristic parameters of the sample object, wherein the charging network simulation environment is used for providing the label for the charging strategy generation model;
the user management module is at least used for calling a first number of the physical machine equipment according to target object configuration, wherein the first number is the number of the most physical machine equipment which can be used when the force calculation unit is operated;
and the calculation scheduling module is used for adjusting the second quantity of the physical machine equipment in real time according to the running state of the calculation unit, wherein the first quantity is greater than the second quantity.
2. The system of claim 1, wherein the force calculating units are in one-to-one correspondence with sandboxes for running the first and second target tasks.
3. The system of claim 1, wherein adjusting the second number of physical machine devices in real-time based on the operational state of the computing power unit comprises:
acquiring the running state of the first target task, and distributing a third number of physical machine devices corresponding to the first target task to the second target task under the condition that the first target task is in an idle state;
and acquiring the running state of the second target task, and distributing a fourth quantity of physical machine equipment corresponding to the second target task to the first target task under the condition that the second target task is in an idle state.
4. The system according to claim 1, wherein the computing power scheduling module is further configured to quit the scheduling sequence from the first target task and/or the second target task when the first target task and/or the second target task finishes running, where the scheduling sequence includes a plurality of tasks to be executed and the physical machine devices corresponding to the tasks to be executed.
5. The system of claim 4, wherein the computing power scheduling module is further configured to determine a third target task according to the priority of each system module and suspend the third target task temporarily when the remaining number corresponding to the physical machine device is smaller than a predetermined threshold, and wherein the system modules include: the computing power scheduling module, the user management module and the data analysis module.
6. The system of claim 1, wherein the user management module has a highest priority, and wherein determining a third target task and suspending the third target task temporarily according to the priority of each system module comprises:
receiving an input instruction of a target object, wherein the input instruction is used for indicating the priority of the computing power scheduling module or the data analysis module;
when the input instruction indicates that the priority of the computing power scheduling module is lower than that of the data analysis module, taking the running task of the computing power module as the third target task, and temporarily suspending the third target task;
and when the input instruction indicates that the priority of the computing power scheduling module is higher than that of the data analysis module, taking the running task of the data analysis module as the third target task, and temporarily suspending the third target task.
7. The system of any of claims 1-6, wherein the physical machine device comprises: a Central Processing Unit (CPU) and a Graphics Processing Unit (GPU).
8. A computing power scheduling method is applied to a distributed platform and comprises the following steps:
acquiring the number of currently available physical machine equipment;
determining the target number of physical machine equipment required by the force calculating unit;
distributing the physical machine equipment with the target quantity required by the force calculation unit to the force calculation unit, wherein the force calculation unit is used for respectively configuring a charging strategy generation model and a charging network simulation environment as a first target task and a second target task, the charging strategy generation model is obtained through multiple sets of training data, and each set of data in the multiple sets of training data comprises: the charging system comprises charging behavior characteristic parameters of a sample object and a label used for identifying a charging price strategy corresponding to the charging behavior characteristic parameters of the sample object, wherein the charging network simulation environment is used for providing the label for the charging strategy generation model.
9. A non-volatile storage medium, comprising a stored program, wherein a device on which the non-volatile storage medium is located is controlled to perform the computational power scheduling method of claim 8 when the program is run.
10. A processor configured to execute a program, wherein the program executes to perform the computational scheduling method of claim 8.
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