CN114548471A - Resource allocation method, device, readable medium and electronic equipment - Google Patents
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Abstract
The invention discloses a resource allocation method, a device, a readable medium and electronic equipment, wherein the method comprises the following steps: determining the providing time and the providing data amount of the load data provided by the target user; determining a user identification of the target user based on the providing time and the providing data amount; determining a load prediction model based on the provided data volume and the load data provided by the target user; determining a resource allocation scheme for the target user based on the user identification and the load prediction model. And determining a user identifier through the providing time and the providing data volume of the load data provided by the target user, and determining different resource allocation schemes for different target users according to the user identifier and the load prediction model to realize reasonable allocation of resources.
Description
Technical Field
The present invention relates to the field of energy, and in particular, to a resource allocation method, device, readable medium, and electronic device.
Background
With the rapid development of the internet technology, user data becomes more and more important resources, various prediction models can be trained based on the user data, and an accurate prediction result is the basis for efficient operation of an energy system. However, not every energy user can collect massive user data, and an accurate prediction model is trained, so that federal learning becomes a trend, and in order to encourage each energy user to participate in federal learning, it is crucial to determine a reasonable resource allocation method.
Disclosure of Invention
The invention provides a resource allocation method, a device, a readable medium and electronic equipment, which determine a user identifier through the providing time and the providing data volume of load data provided by a target user, determine different resource allocation schemes for different target users according to the user identifier and a load prediction model, and realize reasonable allocation of resources.
In a first aspect, the present invention provides a resource allocation method, including:
determining the providing time and the providing data amount of the load data provided by the target user;
determining a user identification of the target user based on the providing time and the providing data amount;
determining a load prediction model based on the provided data volume and the load data provided by the target user;
determining a resource allocation scheme for the target user based on the user identification and the load prediction model.
Preferably, the first and second electrodes are formed of a metal,
determining a load prediction model based on the provided data volume and the load data provided by the target user, including:
judging whether the provided data volume meets a preset condition or not;
if the provided data volume meets the preset condition, determining an individual load prediction model corresponding to the target user based on the load data provided by the target user;
said determining a resource allocation scheme for said target user based on said user identification and said load prediction model comprises:
and determining a resource allocation scheme of the target user based on the user identification of the target user and the individual load prediction model.
Preferably, the first and second electrodes are formed of a metal,
the method further comprises the following steps:
if the provided data volume does not meet the preset condition, determining a matched load prediction model for the target user based on the data characteristics of the load data provided by the target user;
said determining a resource allocation scheme for said target user based on said user identification and said load prediction model comprises:
and determining a resource allocation scheme of the target user based on the user identification of the target user and the matched load prediction model.
Preferably, the first and second electrodes are formed of a metal,
if the provided data volume does not meet the preset condition, determining a matched load prediction model for the target user based on the data characteristics of the load data provided by the target user, including:
if the provided data quantity does not meet the preset condition, determining the data characteristics of the load data provided by the target user;
determining the similarity between the data characteristics of the load data corresponding to the stored load prediction model and the data characteristics of the load data provided by the target user;
selecting the storage load prediction model with the maximum data characteristic similarity with the load data provided by the target user as a selected load prediction model;
and determining a matched load prediction model corresponding to the target user based on the load data provided by the target user and the selected load prediction model.
Preferably, the first and second electrodes are formed of a metal,
the selecting the storage load prediction model with the maximum data characteristic similarity with the load data provided by the target user is a selected load prediction model, and the selecting comprises the following steps:
judging whether the maximum value of the similarity between the data characteristics of the load data corresponding to the stored load prediction model and the data characteristics of the load data provided by the target user is greater than a preset threshold value or not;
and if so, selecting the storage load prediction model with the maximum data characteristic similarity with the load data provided by the target user as a selected load prediction model.
Preferably, the first and second electrodes are formed of a metal,
the method further comprises the following steps:
if not, determining a matched user for the target user based on the data characteristics of the load data provided by the target user;
determining the similarity between the load data provided by the target user and the load data provided by the matched user;
determining selected load data in the load data provided by the matched user based on the similarity between the load data provided by the target user and the load data provided by the matched user;
and determining a matched load prediction model of the target user based on the load data provided by the target user and the selected load data.
Preferably, the first and second electrodes are formed of a metal,
the method further comprises the following steps:
determining the updating times and the updating data volume of the target user updating load data;
updating the user identification of the target user based on the updating times and the updating data volume;
updating a load prediction model of the target user based on the updated load data.
In a second aspect, the present invention provides a resource allocation apparatus, including:
the data determination module is used for determining the providing time and the providing data volume of the load data provided by the target user;
an identification determination module, configured to determine a user identification of the target user based on the provision time and the provision data amount;
the model determining module is used for determining a load prediction model based on the provided data volume and the load data provided by the target user;
and the scheme determining module is used for determining the resource allocation scheme of the target user based on the user identification and the load prediction model.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides a resource allocation method, a device, a readable medium and electronic equipment, wherein the method determines a user identifier of a target user according to the providing time and the providing data volume of load data provided by the target user, namely the load data provided by the target user in different time periods and the data volume of the provided load data are reflected in the user identifier, determines a load prediction model according to the load data and the data volume thereof, and further determines a resource allocation scheme of the target user according to the user identifier and the load prediction model, wherein the determined resource allocation scheme fully considers the load data of the target user, the providing time and the providing data volume of the load data, has rationality and can encourage more target users to participate in federal learning.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a first resource allocation method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a second resource allocation method according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a third resource allocation method according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating step 133 of a resource allocation method according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a step 1333 of a resource allocation method according to an embodiment of the present invention;
fig. 6 is a flowchart illustrating a fourth resource allocation method according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a resource allocation apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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 invention.
As shown in fig. 1, an embodiment of the present invention provides a resource allocation method, where the method includes:
and 14, determining a resource allocation scheme of the target user based on the user identification and the load prediction model.
In the above embodiment, the user identifier of the target user is determined according to the providing time and the providing data volume of the load data provided by the target user, that is, the load data provided by the target user in different time periods and the data volume of the provided load data are both reflected in the user identifier, the load prediction model is determined according to the load data and the data volume thereof, and the resource allocation scheme of the target user is further determined according to the user identifier and the load prediction model, and the determined resource allocation scheme fully considers the load data of the target user, the providing time of the load data and the providing data volume, so that the method has rationality and can encourage more target users to participate in federal learning.
It should be noted that the resource allocation scheme includes the content and amount of resources allocated by the federal center to the target user, and also includes the content and amount of resources transferred by the target user to the federal center.
Specifically, the providing time may be divided, for example, the providing time includes a front stage, a middle stage and a rear stage, and different providing times correspond to different resource allocation amounts; when the data volume is determined to be provided, preprocessing the data to determine the effective data volume, and determining the resource allocation quantity according to the data grade division corresponding to the effective data volume; and integrating the determined load prediction models, determining an integrated load prediction model, determining the contribution ratio of each load prediction model to the integrated load prediction model, and determining the resource distribution quantity according to the contribution ratio, so that the resource distribution is reasonable by comprehensively considering the user identification and the load prediction model.
In one embodiment of the present invention, step 13, comprises:
if the provided data amount meets the preset condition, as shown in fig. 2, executing step 132 and step 141;
step 132 includes determining an individual load prediction model corresponding to the target user based on the load data provided by the target user;
step 141 includes: and determining a resource allocation scheme of the target user based on the user identification of the target user and the individual load prediction model.
If the provided data amount does not meet the preset condition, as shown in fig. 3, executing step 133 and step S142;
step 133 comprises determining a matching load prediction model for a target user based on data characteristics of load data provided by the target user;
step 142 includes: and determining a resource allocation scheme of the target user based on the user identification of the target user and the matched load prediction model.
In the above embodiment, because the data volume of the load data provided by the target user is not enough to train an individual load prediction model, in order to meet the requirements of various target users, after the load data provided by the target user is acquired, it is determined whether the provided data volume meets a preset condition, where the preset condition may be greater than a set threshold, and if the preset condition is greater than the set threshold, the individual load prediction model may be trained based on the load data provided by the target user. After the individual load prediction model is trained, the target user of the individual load prediction model can use the model for free, and the federal center can also use the model. Further, a resource allocation scheme of the target user is determined according to the user identification and the individual load prediction model of the target user, specifically, the individual load prediction model is integrated, the integrated load prediction model is determined, and the content and the quantity of resources transferred to the target user by the federal center are determined according to the contribution ratio of the individual load prediction model to the integrated load prediction model, wherein the content of the resources can be points.
In the above embodiment, if the data size of the load data provided by the target user does not meet the preset condition, that is, the individual load prediction model cannot be trained independently according to the load data of the target user, the matching load prediction model may be determined for the target user according to the data characteristics of the load data provided by the target user. And further, determining a resource allocation scheme of the target user according to the user identification of the target user and the matched load prediction model. Specifically, the target user is required to transfer resources to the federal center because the matched load prediction model is owned by the federal center, and if the time for providing load data by the target user is later, the higher the accuracy of the determined matched load prediction model is, the more resources are transferred to the federal center by the target user.
In a possible implementation manner, after the individual load prediction models of the target users are determined, the individual load prediction models are integrated to determine an integrated load prediction model, and when the data volume provided by a certain target user is determined to be not in accordance with the preset condition, the integrated load prediction model is called as a matched load prediction model. When the target user needs to call the integrated load prediction model, the target user needs to transfer resources to the federal center, and the federal center determines to transfer the resources to the target user participating in the integrated load prediction model according to the number of times of the integrated load prediction model, so that reasonable allocation of the resources is realized.
It should be noted that after the individual load prediction models of the target users are integrated into the integrated load model, the individual load prediction model and the matched load prediction model are updated by using the integrated load prediction model, so as to improve the accuracy of the individual load prediction model and the matched load prediction model.
As shown in FIG. 4, in one embodiment of the present invention, step 133 comprises:
In the above embodiment, when the provided data amount does not meet the preset condition, the data characteristics of the load data provided by the target user are determined, where the data characteristics may include, but are not limited to, the location of the target user, the climate at the location of the target user, the data amount of the target user, and the like. And determining the similarity between the data characteristics of the load data corresponding to the storage load prediction model and the data characteristics of the load data provided by the target user, and taking the storage load prediction model with the maximum similarity as a selected load prediction model, wherein the selected load prediction model is used as an initial model of the target user because the data characteristics of the load data of the target user are similar to the data characteristics of the preset selected load model. Further, in order to determine a matching load prediction model corresponding to the target user, the selected load prediction model is updated by using load data provided by the target user, so that the matching load prediction model which can be used for load prediction of the target user is determined, and the matching load prediction model is obtained according to the selected load prediction model which has the most similar data characteristics with the target user and the load data provided by the target user, so that the accuracy is high.
As depicted in FIG. 5, in one embodiment of the present invention, step 1333 includes:
In the above embodiment, there is a possible case that the data characteristics of the load data corresponding to the storage load prediction model and the data characteristics of the load data provided by the target user have a large difference, and at this time, it is not suitable to use any one of the storage load prediction models as the initial model of the target user, and when the data characteristics of the load data corresponding to the storage load prediction model and the data characteristics of the load data provided by the target user are determined to be similar, a maximum value always exists, so that in order to ensure the accuracy of the determined selected load prediction model, a predetermined threshold is used for performing a predetermined determination, and whether the maximum value of the data characteristics of the load data corresponding to the storage load prediction model and the data characteristics of the load data provided by the target user is greater than the predetermined threshold is determined, and if so, the storage load prediction model with the largest similarity to the data characteristics of the load data provided by the target user can be selected as the selected load prediction model. If not, selecting in the stored load prediction model, determining a matched user for the target user according to the data characteristics of the load data provided by the target user, wherein the matched user is a user similar to the target user, then determining selected load data in the load data provided by the matched user according to the similarity between the load data provided by the target user and the load data provided by the matched user, and the selected load data is similar to the load data of the target user, then serving as the supplement of the load data provided by the target user, and determining the matched load prediction model of the target user after fusing the load data provided by the target user and the selected load data.
As illustrated in fig. 6, in an embodiment of the present invention, the method further includes:
and step 17, updating the load prediction model of the target user based on the updated load data.
In the above embodiment, as time goes on, in order to obtain a more accurate load prediction model, the target user needs to continuously update the load data, and the user identifier of the target user is updated according to the update times and the update data volume of the target user, so that the target user with a large update time and a large update data volume can obtain more resources in the federal center. And after the updated load data exists, updating the load prediction model of the target user according to the updated load data, so that the accuracy of the load prediction model is higher and higher.
Based on the same inventive concept as the method described above, as shown in fig. 7, an embodiment of the present invention provides a resource allocation apparatus, including:
a data determination module 71, configured to determine a providing time and a providing data amount for providing the load data by the target user;
an identification determination module 72, configured to determine a user identification of the target user based on the providing time and the providing data amount;
a model determining module 73, configured to determine a load prediction model based on the provided data amount and the load data provided by the target user;
a scheme determining module 74, configured to determine a resource allocation scheme of the target user based on the user identifier and the load prediction model.
For convenience of description, the above embodiments of the apparatus are described as functionally separated into various units or modules, and the functions of the units or modules may be implemented in one or more of software and/or hardware in implementing the present invention.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device includes a processor 801 and a memory 802 storing execution instructions, and optionally further includes an internal bus 803 and a network interface 804. The Memory 802 may include a Memory 8021, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory 8022 (e.g., at least 1 disk Memory); the processor 801, the network interface 804, and the memory 802 may be connected to each other by an internal bus 803, and the internal bus 803 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like; the internal bus 803 may be divided into an address bus, a data bus, a control bus, etc., which are indicated by only one double-headed arrow in fig. 8 for convenience of illustration, but do not indicate only one bus or one type of bus. Of course, the electronic device may also include hardware required for other services. When the processor 801 executes execution instructions stored by the memory 802, the processor 801 performs the method of any of the embodiments of the present invention and at least is used to perform the method as shown in fig. 1-6.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the execution instruction, and may also obtain the corresponding execution instruction from other devices, so as to form a resource allocation apparatus on a logic level. The processor executes the execution instructions stored in the memory to implement a resource allocation method provided in any embodiment of the invention through the executed execution instructions.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Embodiments of the present invention further provide a computer-readable storage medium, which includes an execution instruction, and when a processor of an electronic device executes the execution instruction, the processor executes a method provided in any one of the embodiments of the present invention. The electronic device may specifically be the electronic device shown in fig. 8; the execution instruction is a computer program corresponding to the resource allocation device.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
All the embodiments in the invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or boiler that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or boiler. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or boiler that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for resource allocation, comprising:
determining the providing time and the providing data amount of the load data provided by the target user;
determining a user identification of the target user based on the providing time and the providing data amount;
determining a load prediction model based on the provided data volume and the load data provided by the target user;
determining a resource allocation scheme for the target user based on the user identification and the load prediction model.
2. The method according to claim 1, wherein the determining a load prediction model based on the provided data amount and the load data provided by the target user comprises:
judging whether the provided data volume meets a preset condition or not;
if the provided data volume meets the preset condition, determining an individual load prediction model corresponding to the target user based on the load data provided by the target user;
said determining a resource allocation plan for said target user based on said user identification and said load prediction model comprises:
and determining a resource allocation scheme of the target user based on the user identification of the target user and the individual load prediction model.
3. The method of claim 2, further comprising:
if the provided data volume does not meet the preset condition, determining a matched load prediction model for the target user based on the data characteristics of the load data provided by the target user;
said determining a resource allocation scheme for said target user based on said user identification and said load prediction model comprises:
and determining a resource allocation scheme of the target user based on the user identification of the target user and the matched load prediction model.
4. The method according to claim 3, wherein if the provided data amount does not meet the preset condition, determining a matching load prediction model for the target user based on the data characteristics of the load data provided by the target user comprises:
if the provided data quantity does not meet the preset condition, determining the data characteristics of the load data provided by the target user;
determining the similarity between the data characteristics of the load data corresponding to the stored load prediction model and the data characteristics of the load data provided by the target user;
selecting the storage load prediction model with the maximum data characteristic similarity with the load data provided by the target user as a selected load prediction model;
and determining a matched load prediction model corresponding to the target user based on the load data provided by the target user and the selected load prediction model.
5. The method according to claim 4, wherein the selecting the storage load prediction model with the largest similarity to the data characteristics of the load data provided by the target user is a selecting load prediction model, and comprises:
judging whether the maximum value of the similarity between the data characteristics of the load data corresponding to the stored load prediction model and the data characteristics of the load data provided by the target user is greater than a preset threshold value or not;
and if so, selecting the storage load prediction model with the maximum data characteristic similarity with the load data provided by the target user as a selected load prediction model.
6. The method of claim 5, further comprising:
if not, determining a matched user for the target user based on the data characteristics of the load data provided by the target user;
determining the similarity between the load data provided by the target user and the load data provided by the matched user;
determining selected load data in the load data provided by the matched user based on the similarity between the load data provided by the target user and the load data provided by the matched user;
and determining a matched load prediction model of the target user based on the load data provided by the target user and the selected load data.
7. The method of claim 1, further comprising:
determining the updating times and the updating data volume of the target user updating load data;
updating the user identification of the target user based on the updating times and the updating data volume;
updating a load prediction model of the target user based on the updated load data.
8. A resource allocation apparatus, comprising:
the data determination module is used for determining the providing time and the providing data volume of the load data provided by the target user;
an identification determination module, configured to determine a user identification of the target user based on the providing time and the providing data amount;
the model determining module is used for determining a load prediction model based on the provided data volume and the load data provided by the target user;
and the scheme determining module is used for determining the resource allocation scheme of the target user based on the user identification and the load prediction model.
9. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 7.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-7 when the processor executes the execution instructions stored by the memory.
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