CN112867162B

CN112867162B - Slice resource allocation method and device

Info

Publication number: CN112867162B
Application number: CN202110097699.8A
Authority: CN
Inventors: 李静; 周柳杉; 李福昌; 曹亘
Original assignee: China United Network Communications Group Co Ltd
Current assignee: China United Network Communications Group Co Ltd
Priority date: 2021-01-25
Filing date: 2021-01-25
Publication date: 2023-04-07
Anticipated expiration: 2041-01-25
Also published as: CN112867162A

Abstract

The embodiment of the application provides a slice resource configuration method and device, relates to the technical field of communication, and solves the technical problem that the network resource utilization rate of a slice service in the prior art is low. The slice resource allocation method comprises the following steps: obtaining target information of a cell, wherein the target information comprises at least one of the following items: slice identification, service type, user distribution, channel Quality Indicator (CQI) mean value, throughput and time delay; inputting the target information as input information into a preset CQI prediction model to obtain a CQI prediction value of the cell at the next moment; and determining a slice resource allocation scheme of the cell according to the CQI predicted value.

Description

Slice resource allocation method and device

Technical Field

The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for slice resource allocation.

Background

Network slicing is an important network architecture innovation in the fifth generation mobile communication technology, and can realize coexistence of a plurality of isolated and independent virtual networks on the same physical network infrastructure. Network slices have multiple performance advantages. For example, capital expenditures in network deployment and operation may be reduced; service differentiation can be realized and a service level agreement of each service type can be ensured; flexibility and adaptability of network management can be increased.

However, prior art network slices still face some challenges in the application process. For example, since the required amount of the network resources by the slicing service is dynamically changed, if fixed network resources are allocated, the problems that the resource requirements cannot be met or the resources are wasted are caused, so that the resource utilization rate is low.

Disclosure of Invention

The application provides a slice resource configuration method and device, which solve the technical problem of low network resource utilization rate of slice services in the prior art.

In order to achieve the purpose, the technical scheme is as follows:

in a first aspect, a slice resource configuration method is provided, including: obtaining target information of a cell, wherein the target information comprises at least one of the following items: slice identification, service type, user distribution, channel Quality Indicator (CQI) mean value, throughput and time delay; inputting the target information as input information into a preset CQI prediction model to obtain a CQI prediction value of the cell at the next moment; and determining a slice resource configuration scheme of the cell according to the CQI predicted value.

In the embodiment of the present application, target information of a cell may be obtained, where the target information includes at least one of the following: slice identification, service type, user distribution, channel Quality Indicator (CQI) mean value, throughput and time delay; inputting the target information serving as input information into a preset CQI prediction model to obtain a CQI prediction value of the cell at the next moment; and determining a slice resource allocation scheme of the cell according to the CQI predicted value. According to the scheme, the CQI predictive value of the cell at the next moment can be determined through the preset CQI predictive model, and the slice resource allocation scheme of the cell is determined according to the CQI predictive value.

In a second aspect, a slice resource configuration apparatus is provided and includes an obtaining unit, a prediction unit, and a determination unit. The acquiring unit is configured to acquire target information of a cell, where the target information includes at least one of: slice identification, service type, user distribution, channel Quality Indicator (CQI) mean value, throughput and time delay; the prediction unit is used for inputting the target information as input information into a preset CQI prediction model to obtain a CQI prediction value of the cell at the next moment; the determining unit is configured to determine a slice resource allocation scheme of the cell according to the CQI prediction value.

In a third aspect, a slice resource configuration apparatus is provided that includes a memory and a processor. The memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus. When the slice resource allocation apparatus is running, the processor executes computer-executable instructions stored in the memory to cause the slice resource allocation apparatus to perform the slice resource allocation method provided in the first aspect.

In a fourth aspect, a computer-readable storage medium is provided, which includes computer-executable instructions, when the computer-executable instructions are executed on a computer, the computer is caused to execute the slice resource configuration method provided in the first aspect.

In a fifth aspect, a computer program product is provided, which comprises computer instructions that, when executed on a computer, cause the computer to perform the slice resource configuration method as provided in the first aspect and its various possible implementations.

It should be noted that all or part of the computer instructions may be stored on the computer readable storage medium. The computer readable storage medium may be packaged with a processor of the slice resource configuration apparatus, or may be packaged separately from the processor of the slice resource configuration apparatus, which is not limited in this application.

In the description of the second aspect, the third aspect, the fourth aspect, and the fifth aspect in the present application, reference may be made to the detailed description of the first aspect, which is not repeated herein; in addition, for the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to the beneficial effect analysis of the first aspect, and details are not repeated here.

In the present application, the names of the slice resource allocation means do not limit the devices or functional modules themselves, and in actual implementation, the devices or functional modules may appear by other names. Insofar as the functions of the respective devices or functional modules are similar to those of the present application, they fall within the scope of the claims of the present application and their equivalents.

These and other aspects of the present application will be more readily apparent from the following description.

Drawings

Fig. 1 is a schematic structural diagram of a slice resource allocation system according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a slice resource allocation method according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a slice resource allocation apparatus according to an embodiment of the present application;

fig. 4 is a second schematic structural diagram of a slice resource allocation apparatus according to an embodiment of the present disclosure;

fig. 5 is a schematic hardware structure diagram of a slice resource allocation apparatus according to an embodiment of the present disclosure;

fig. 6 is a second hardware structure diagram of a slice resource allocation apparatus according to a second embodiment of the present disclosure.

Detailed Description

The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and 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 in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present relevant concepts in a concrete fashion.

For the convenience of clearly describing the technical solutions of the embodiments of the present application, in the embodiments of the present application, the terms "first", "second", and the like are used to distinguish the same items or similar items with basically the same functions and actions, and those skilled in the art can understand that the terms "first", "second", and the like do not limit the quantity and execution order.

In order to meet the requirements of various and dynamically-changing network slicing services, limited slicing resources need to be flexibly arranged, and rapid adjustment and deployment of slicing configuration are realized, so as to ensure a Service Level Agreement (SLA) of the slicing services. As shown in fig. 1, an embodiment of the present application provides a Slice resource configuration system, which includes an artificial intelligence module, a Network Slice Management Function (NSMF), and a Network Slice Subnet Management module (NSSMF). The artificial intelligence module can comprise a data acquisition sub-module, a Channel Quality Indication (CQI) prediction sub-module, an SLA analysis sub-module and a cache sub-module; the sub-slices in the Network slice subnet management module may include a Radio Access Network (RAN) sub-slice, a Transport Network (TN) sub-slice, and a Core Network (CN).

The artificial intelligence module can be used for performing real-time data analysis and intelligent decision on the operation and resource use state of the slice according to the KPI data, the service type, the network resource state, the slice template information, the configuration parameters and other information fed back by the slice, such as: and deducing an optimal slice resource configuration scheme according to the slice historical information and transmitting the optimal slice resource configuration scheme to the NSMF, or caching the slice resource configuration scheme to a cache submodule. The network slice management module can be used for instantiation management and arrangement of network slices; and converting the requirements of the network slices into the requirements of each domain subslice, and transmitting each domain resource configuration scheme to NSSMF of each subnet. The network slice subnet management module can be used for managing and arranging each domain subnet slice and converting each subnet slice requirement into a requirement of a network function.

As shown in fig. 2, an embodiment of the present application provides a slice resource allocation method, which may be applied to a slice resource allocation device or the slice resource allocation system, and the following describes the slice resource allocation method in detail by taking the example that the slice resource allocation method is applied to the slice resource allocation device. The slice resource allocation method may include S201 to S203 described below.

S201, the slice resource allocation device acquires target information of a cell.

Wherein, the target information may include at least one of the following: slice identification, traffic type, user distribution, channel quality indication CQI mean, throughput, and latency.

The slice resource allocation device can collect data information fed back by users and slices, and can perform simple cleaning and preprocessing work before importing the data into the scheme database because of a lot of collected data, and finally import the data into the original database, and extract corresponding data information according to different intelligent analysis work. In an embodiment of the present application, the slice resource configuration means may extract target information related to a slice from the original database. Such as traffic type, user distribution (time and spatial information), average CQI value, throughput, and latency. As shown in table 1, cell target information that can be acquired by the device is configured for slice resources.

TABLE 1

Section mark

Type of service

User distribution

Average CQI value

Throughput capacity

Time delay

1

2

……

k

S202, the slice resource allocation device inputs the target information as input information into a preset CQI prediction model to obtain a CQI prediction value of the cell at the next moment.

Optionally, before inputting the target information as input information into a preset CQI prediction model, the slice resource configuration device may further acquire historical CQI data of the cell, and determine a training data set and a test data set according to the historical CQI data; then, training a CQI prediction model by using a training data set through an XGboost algorithm; and adopting a test data set to test and evaluate the CQI prediction model so as to obtain the preset CQI prediction model.

The XGBoost algorithm is a typical learning method with an integrated idea, and can combine multiple associated decision trees to make a decision. Based on the superiority of ensemble learning, the XGBoost algorithm is selected to establish the CQI prediction model in this embodiment.

In order to improve the diversity of a learner in ensemble learning and improve the generalization capability of a model, data sample disturbance and attribute disturbance are added in the model training process, so that a plurality of good and different XGboost models are established and integrated. For sample x _i By XGboost model

The integration carries out prediction, and the prediction result can be expressed as:

wherein N is the number of XGboost models.

Specifically, the training process of the CQI prediction model based on the XGBoost algorithm may include the following steps:

the method comprises the following steps: and constructing a sample set by utilizing the collected historical CQI data, and dividing the samples into a training set and a testing set.

Step two: and establishing a CQI prediction model based on the XGboost algorithm by utilizing the training set. Adding sample disturbance and attribute disturbance to carry out random sampling on the training set samples to generate a plurality of sampling sets, and repeating the following steps a to c for each sampling set.

a. Starting from a root node, recursively selecting an optimal feature of a tree structure, dividing a data set according to the feature until a preset division stop condition (such as the maximum depth of the tree) is reached, and distributing all samples to leaf nodes so as to generate a CART tree.

b. And (d) repeating the step a, and sequentially establishing a plurality of CART decision trees in the gradient descending direction of the loss function.

c. And combining a plurality of CART decision trees to establish a CQI prediction model based on the XGboost algorithm.

Step three: and testing and evaluating the CQI prediction model by using the test set. And for each sample of the test set, respectively calculating the sum of the prediction scores of all the leaf nodes of the CART tree by using the XGboost model generated in the second step. And integrating the prediction results of the plurality of XGboost models to obtain the CQI prediction result.

And step four, evaluating and analyzing the accuracy of the prediction result, and adjusting corresponding parameters of the model to obtain an optimal CQI prediction model.

It should be noted that, since the CQI is an important index for measuring the downlink channel quality of the cell, the measurement quantity is measured and reported by the terminal device. And determining whether the modulation mode of the terminal equipment and the relevant configuration of resource allocation need to be adjusted or not according to the CQI measurement report reported by the terminal equipment and by combining the current resource condition. However, the CQI changes due to different distribution of users, and therefore, the predictive analysis of the CQI is helpful for the management of network slices.

S203, the slice resource allocation device determines the slice resource allocation scheme of the cell according to the CQI predicted value.

The slice resource allocation device can determine whether a first slice resource allocation scheme matched with the CQI predicted value exists in the scheme database or not by traversing the scheme database; and if so, determining the first slice resource allocation scheme as the slice resource allocation scheme of the cell. If the scheme database does not have a slice resource allocation scheme matched with the CQI predicted value; a second slice resource allocation scheme is determined by SLA analysis and determined as a slice resource allocation scheme for the cell and cached to a scheme database.

Specifically, the slice resource allocation device can perform modeling analysis on data information collected in an original database, analyze an incidence relation among a slice template, resource allocation parameters and SLA indexes, infer an optimal SLA splitting strategy and resource allocation of each domain, and output a result to the cache submodule and the NSMF, so that the NSMF can allocate resources and paths according to a scheme.

By predictive analysis of the CQI, the slice resource allocation apparatus can adjust the resources of each domain network sub-slice in accordance with the performance of the slice. The capacity expansion is carried out on the slices with limited resources, and the capacity reduction is carried out on the slices with sufficient resources, so that the network resources are multiplexed to the maximum extent by a plurality of slices, and the resource conflict among the slices can be avoided.

It should be noted that the scheme database includes a corresponding relationship between resource configuration schemes and CQI prediction values, and as shown in table 2, one configuration scheme may correspond to at least one CQI prediction value.

TABLE 2

Optionally, the slice resource configuration device may further monitor a network performance parameter of the current slice running state in real time, analyze the network performance parameter of the slice, determine a contract condition of the slice SLA, and transmit a result to the NSMF. If the current network resource excess meets the slice SLA requirement, the NSMF can reduce the resource allocation of the slice; the NSMF may increase the resource configuration of a slice if the current network resources cannot meet slice SLA requirements.

Optionally, the slice resource configuration may delete a slice resource configuration scheme that is not called within a preset time period in the scheme database. One implementation manner is that after the slice resource allocation device determines the second slice resource allocation scheme through SLA analysis, the slice resource allocation may allocate a slice resource allocation scheme that is not called in the second slice resource allocation scheme replacement scheme database within a preset time period; another implementation manner is that the slice resource configuration may directly delete a slice resource configuration scheme that is not called in the scheme database within a preset time period.

The embodiment of the application provides a slice resource allocation method, which can determine a CQI predicted value of a cell at the next moment through a preset CQI prediction model, and determine a slice resource allocation scheme of the cell according to the CQI predicted value.

The scheme provided by the embodiment of the application is mainly introduced from the perspective of a method. To implement the above functions, it includes hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed in hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the slice resource allocation method provided by the embodiment of the present application, the execution subject may be a slice resource allocation device, or a control module for slice resource allocation in the slice resource allocation device. The slice resource allocation device provided in the embodiment of the present application is described with an example in which a slice resource allocation device executes a slice resource allocation method.

In addition, in the embodiment of the present application, the slice resource configuration apparatus may be divided into the functional modules according to the above method example, for example, each functional module may be divided according to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. Optionally, the division of the modules in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.

As shown in fig. 3, an apparatus for configuring slice resources according to an embodiment of the present application is provided. The slice resource configuration apparatus 300 may include an acquisition unit 301, a prediction unit 302, and a determination unit 303. The acquiring unit 301 may be configured to acquire target information of a cell, where the target information includes at least one of the following: slice identity, traffic type, user distribution, channel quality indicator CQI mean, throughput, and latency. The predicting unit 302 may be configured to input the target information as input information into a preset CQI prediction model, so as to obtain a CQI prediction value of the cell at the next time. The determining unit 303 may be configured to determine a slice resource allocation scheme of a cell according to the CQI prediction value. For example, with reference to fig. 2, the obtaining unit 301 may be configured to perform S201, the predicting unit 302 may be configured to perform S202, and the determining unit 303 may be configured to perform S203.

Optionally, with reference to fig. 3, as shown in fig. 4, the slice resource configuration apparatus 300 may further include a training unit 304, where the training unit 304 may be configured to, before inputting the target information as input information into a preset CQI prediction model, acquire historical CQI data of the cell, and determine a training data set and a test data set according to the historical CQI data; training a CQI prediction model by adopting the training data set through an XGboost algorithm; and performing test evaluation on the CQI prediction model by adopting the test data set to obtain the preset CQI prediction model.

Optionally, the determining unit 303 is specifically configured to determine, by traversing a scheme database, whether a first slice resource configuration scheme matching the CQI prediction value exists in the scheme database; and if so, determining the first slice resource configuration scheme as the slice resource configuration scheme of the cell.

Optionally, with reference to fig. 3, as shown in fig. 4, the slice resource configuration apparatus 300 may further include a buffer unit 305. The determining unit 303 may be further configured to, if no slice resource allocation scheme matching the CQI prediction value exists in the scheme database; determining a second slice resource allocation scheme through service level agreement SLA analysis; determining the second slice resource configuration scheme as a slice resource configuration scheme of the cell. The caching unit 305 may be configured to cache the second slice resource allocation scheme in the scheme database.

Optionally, with reference to fig. 3, as shown in fig. 4, the slice resource configuration apparatus 300 may further include a deleting unit 306. The deleting unit 306 may be configured to delete a slice resource allocation scheme that is not called in the scheme database within a preset time period.

Of course, the slice resource allocation apparatus 300 provided in the embodiment of the present application includes, but is not limited to, the above modules.

The embodiment of the present application further provides a slice resource configuration apparatus as shown in fig. 5, where the slice resource configuration apparatus includes a processor 11, a memory 12, a communication interface 13, and a bus 14. The processor 11, the memory 12 and the communication interface 13 may be connected by a bus 14.

The processor 11 is a control center of the slice resource allocation apparatus, and may be a single processor or a collective term for multiple processing elements. For example, the processor 11 may be a general-purpose Central Processing Unit (CPU), or may be another general-purpose processor. Wherein a general purpose processor may be a microprocessor or any conventional processor or the like.

For one embodiment, processor 11 may include one or more CPUs, such as CPU 0 and CPU 1 shown in FIG. 5.

The memory 12 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

In a possible implementation, the memory 12 may be present separately from the processor 11, and the memory 12 may be connected to the processor 11 via a bus 14 for storing instructions or program code. The processor 11 can implement the slice resource allocation method provided by the embodiment of the present application when calling and executing the instructions or program codes stored in the memory 12.

In another possible implementation, the memory 12 may also be integrated with the processor 11.

And a communication interface 13 for connecting with other devices through a communication network. The communication network may be an ethernet network, a radio access network, a Wireless Local Area Network (WLAN), or the like. The communication interface 13 may comprise a receiving unit for receiving data and a transmitting unit for transmitting data.

The bus 14 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 5, but that does not indicate only one bus or one type of bus.

It should be noted that the structure shown in fig. 5 does not constitute a limitation of the slice resource configuration apparatus. The slice resource configuration means may comprise more or fewer components than shown in fig. 5, or some components may be combined, or a different arrangement of components, in addition to the components shown in fig. 5.

Fig. 6 shows another hardware configuration of the slice resource configuration apparatus in the embodiment of the present application. As shown in fig. 6, the slice resource configuration means may include a processor 21 and a communication interface 22. The processor 21 is coupled to a communication interface 22.

The function of the processor 21 may refer to the description of the processor 11 above. The processor 21 also has a memory function, and the function of the memory 12 can be referred to.

The communication interface 22 is used to provide data to the processor 21. The communication interface 22 may be an internal interface of the slice resource allocation apparatus, or may be an external interface (corresponding to the communication interface 13) of the slice resource allocation apparatus.

It is noted that the structure shown in fig. 5 (or fig. 6) does not constitute a limitation of the slice resource configuration apparatus, which may include more or less components than those shown in fig. 5 (or fig. 6), or combine certain components, or arrange different components, in addition to the components shown in fig. 5 (or fig. 6).

Embodiments of the present application also provide a computer-readable storage medium, which includes computer-executable instructions. When the computer executes the instructions to run on the computer, the computer is enabled to execute the steps executed by the slice resource allocation device in the slice resource allocation method provided by the embodiment.

The embodiment of the present application further provides a computer program product, where the computer program product may be directly loaded into the memory and contains software codes, and after the computer program product is loaded and executed by the computer, the computer program product can implement each step executed by the slice resource configuration device in the slice resource configuration method provided in the foregoing embodiment.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The processes or functions according to the embodiments of the present application are generated in whole or in part when the computer-executable instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that can be accessed by a computer or can comprise one or more data storage devices, such as servers, data centers, and the like, that can be integrated with the media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.

Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the above modules or units is only one logical function division, and there may be other division ways in actual implementation. For example, various elements or components may be combined or may be integrated into another device, or some features may be omitted, or not implemented. 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, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. 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 may be implemented in the form of hardware, or may also be implemented in the 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 readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media capable of storing program codes.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for slice resource allocation, comprising:

obtaining target information of a cell, wherein the target information comprises at least one of the following items: slice identification, service type, user distribution, channel Quality Indicator (CQI) mean value, throughput and time delay;

inputting the target information as input information into a preset CQI prediction model to obtain a CQI prediction value of the cell at the next moment;

determining whether a first slice resource configuration scheme matched with the CQI predicted value exists in a scheme database or not by traversing the scheme database;

and if so, determining the first slice resource configuration scheme as the slice resource configuration scheme of the cell.

2. The slice resource allocation method according to claim 1, wherein before inputting the target information as input information into a preset CQI prediction model, the method further comprises:

acquiring historical CQI data of the cell, and determining a training data set and a testing data set according to the historical CQI data;

training a CQI prediction model by adopting the training data set through an XGboost algorithm;

and performing test evaluation on the CQI prediction model by adopting the test data set to obtain the preset CQI prediction model.

3. The method for slice resource allocation according to claim 1, wherein the method further comprises:

if the scheme database does not have a slice resource allocation scheme matched with the CQI predicted value; determining a second slice resource allocation scheme through service level agreement SLA analysis;

determining the second slice resource allocation scheme as the slice resource allocation scheme of the cell, and caching the second slice resource allocation scheme to the scheme database.

4. The slice resource configuration method of claim 3, wherein the method further comprises:

and deleting the slice resource configuration scheme which is not called in the scheme database within a preset time period.

5. A slice resource allocation apparatus, comprising: an acquisition unit, a prediction unit, and a determination unit;

the acquiring unit is configured to acquire target information of a cell, where the target information includes at least one of: slice identification, service type, user distribution, channel Quality Indicator (CQI) mean value, throughput and time delay;

the prediction unit is used for inputting the target information serving as input information into a preset CQI prediction model to obtain a CQI prediction value of the cell at the next moment;

the determining unit is used for determining whether a first slice resource configuration scheme matched with the CQI predicted value exists in a scheme database or not by traversing the scheme database; and if so, determining the first slice resource configuration scheme as the slice resource configuration scheme of the cell.

6. The slice resource configuration apparatus of claim 5, further comprising a training unit configured to:

before the target information is input into a preset CQI prediction model as input information, acquiring historical CQI data of the cell, and determining a training data set and a testing data set according to the historical CQI data;

training a CQI prediction model by using the training data set through an XGboost algorithm;

7. The slice resource configuration apparatus according to claim 5, further comprising a buffer unit;

the determining unit is further configured to: if the scheme database does not have a slice resource configuration scheme matched with the CQI predicted value; determining a second slice resource allocation scheme through service level agreement SLA analysis; determining the second slice resource configuration scheme as a slice resource configuration scheme for the cell;

the cache unit is configured to cache the second slice resource allocation scheme in the scheme database.

8. The slice resource configuration apparatus according to claim 7, further comprising a deletion unit; the deleting unit is configured to delete the slice resource configuration scheme that is not called in the scheme database within a preset time period.

9. A slice resource allocation apparatus comprising a memory and a processor; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus;

the processor executes the computer-executable instructions stored by the memory when the slice resource configuration device is run to cause the slice resource configuration device to perform the slice resource configuration method of any of claims 1-4.

10. A computer-readable storage medium comprising computer-executable instructions that, when executed on a computer, cause the computer to perform the slice resource configuration method of any of claims 1-4.