CN112118581A - Multi-carrier processing method, apparatus, system, and computer-readable storage medium - Google Patents

Abstract

The present application discloses a multi-carrier processing method, apparatus, system and computer-readable storage medium. The method includes: for a target scenario in a multi-carrier coverage scenario, from historical traffic data, acquiring the actual traffic number within a target period within a predetermined number of days before a specified date; processing the acquired actual traffic by using a prediction model component If the predicted traffic number is less than the first preset traffic number threshold, the target time period is regarded as the idle time period in the target scene; Auxiliary carrier in multi-carrier coverage. According to the method of the embodiment of the present application, the auxiliary carrier in the multi-carrier can be managed, and the network energy consumption resources can be saved.

Description

Multi-carrier processing method, apparatus, system, and computer-readable storage medium

technical field

The present application relates to the field of communication technologies, and in particular, to a multi-carrier processing method, apparatus, system, and computer-readable storage medium.

Background technique

In various base station coverage scenarios of a 5th Generation Mobile Networks (5G) network, there is a situation of multi-carrier coverage. That is, one carrier is used to ensure the basic coverage of the signal in the base station coverage scenario, and the other auxiliary carrier is used to enhance the signal coverage capability.

The energy consumption of the base station accounts for a large proportion of the operator's network cost. In the multi-carrier coverage scenario of the base station, when the network is idle, the coverage enhancement capability of the auxiliary carrier may lead to the waste of resources. Therefore, it is necessary to manage the auxiliary carriers in the multi-carrier to save energy consumption resources in network operation.

SUMMARY OF THE INVENTION

To this end, the present application provides a multi-carrier processing method, apparatus, system and computer-readable storage medium to solve the waste of energy consumption resources of base stations when the network is idle due to the coverage enhancement capability of auxiliary carriers in the prior art The problem.

In order to achieve the above purpose, a first aspect of the present application provides a multi-carrier processing method, the method includes: for a target scenario in a multi-carrier coverage scenario, from historical traffic data, acquiring the daily target data within a predetermined number of days before a specified date. The actual number of traffic in the time period; use the prediction model component to process the obtained actual number of traffic, and obtain the predicted number of traffic in the target time period on the specified date; if the predicted number of traffic is less than the first preset traffic number threshold, then The target period is taken as the idle period in the target scene; the auxiliary carrier in the multi-carrier coverage of the idle period in the target scene is turned off.

A second aspect of the present application provides a multi-carrier processing device, the device comprising: a historical statistics module, configured to obtain, from historical traffic data, for a target scenario in a multi-carrier coverage scenario, the The actual number of traffic in the target period; the prediction module is used to process the obtained actual traffic number by using the prediction model component to obtain the predicted number of traffic in the target period on the specified date; the idle period judgment module is used to predict the number of traffic If the number of traffic is less than the first preset traffic number threshold, the target period is used as the idle period in the target scene; the auxiliary carrier closing module is used to close the auxiliary carrier in the multi-carrier coverage of the idle period in the target scene.

A third aspect of the present application provides a multi-carrier processing system, including a memory and a processor; the memory is used for storing executable program codes; the processor is used for reading the executable program codes stored in the memory to execute any one of the above aspects multi-carrier processing method.

A fourth aspect of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, make the computer execute the multi-carrier processing method of any one of the foregoing aspects.

The present application has the following advantages: according to the multi-carrier processing method, device, system, and computer-readable storage medium in the embodiments of the present application, by counting the time-segmented traffic data of the multi-carrier coverage scenario, using the preset model component to specify the statistics The number of traffic in the target period is processed to obtain the forecast result of the number of traffic, so as to determine whether the specified target period is an idle period in the target scene according to the forecast result of the number of traffic, so as to target the idle period in the target scene. , turn off the auxiliary carrier in the multi-carrier, realize the different coverage scenarios, reduce the energy consumption of the base station and save the operation cost under the condition of ensuring the coverage capability of the base station.

Description of drawings

The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, and together with the following specific embodiments, are used to explain the present application, but do not constitute a limitation to the present application.

FIG. 1 is a flowchart of a carrier processing method provided by an embodiment of the present application;

FIG. 2 is a schematic structural diagram of a multi-carrier processing apparatus provided by an embodiment of the present application;

FIG. 3 is a structural diagram of an exemplary hardware architecture of a computing device capable of implementing the multi-carrier processing method and apparatus according to the embodiments of the present application, provided by the embodiments of the present application.

Detailed ways

The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present application, but not to limit the present application. It will be apparent to those skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating examples of the present application.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprises" does not preclude the presence of additional identical elements in a process, method, article, or device that includes the element.

The communication network system in the embodiments of the present application may be a fifth-generation mobile communication technology (5th Generation wireless systems, 5G) mobile communication system, or a communication network system supporting 5G mobile communication.

Since the energy consumption of the base station accounts for a large proportion of the operator's network cost, in order to save the energy consumption resources in the network operation, it is necessary to manage the auxiliary carrier in the multi-carrier coverage scenario of the base station. The existing management strategies include: most of the auxiliary carriers run continuously for 24 hours, and only a small number of carriers will be temporarily shut down according to a fixed period as a maintenance method. The closing cycle of this method is an initially set fixed time, which lacks flexibility and does not perform closing operations according to actual user needs.

The present application proposes a multi-carrier processing method for a 5G base station, which implements statistical prediction based on the number of traffic in multiple coverage areas, so that auxiliary carriers are turned off when idle to save a lot of energy consumption resources.

For a better understanding of the present application, the multi-carrier processing method according to the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that these embodiments are not intended to limit the scope of the disclosure of the present application.

FIG. 1 is a flowchart illustrating a multi-carrier processing method according to an embodiment of the present application. As shown in FIG. 1 , the multi-carrier processing method in this embodiment of the present application may include the following steps:

Step S110, for the target scenario in the multi-carrier coverage scenario, from the historical traffic data, obtain the actual traffic count per day within the target time period within the predetermined number of days before the specified date.

Step S120, using the prediction model component to process the acquired actual traffic numbers to obtain the predicted traffic numbers in the target time period on the specified date.

Step S130, if the predicted traffic number is less than the first preset traffic number threshold, the target time period is taken as the idle time period in the target scene.

In step S140, the auxiliary carrier in the multi-carrier coverage of the idle period in the target scene is turned off.

According to the multi-carrier processing method of the embodiment of the present application, the number of traffic in the multi-carrier coverage area is statistically predicted, so that the auxiliary carrier is turned off when idle to save a lot of energy consumption resources.

In one embodiment, the target scenario in the multi-carrier coverage scenario may include any one of the following scenarios: traffic hotspot areas, dense urban areas, general urban areas, suburban or county towns and rural areas.

In one embodiment, the network management system can periodically count the traffic data of the multi-layer (multi-carrier) coverage scenarios, and can classify the counted traffic data according to different scenario numbers.

In one embodiment, the historical traffic data is traffic data obtained by statistics on the actual traffic numbers of each same period within at least the predetermined number of days before the current date for different scenarios in the multi-carrier coverage scenario.

As an example, the statistical range of historical traffic data is set to be i days before the current date, and i is an integer greater than or equal to 1. The length of the time period during which traffic data statistics are performed every day may be dynamically adjusted, for example, it may be 1 hour or 2 hours.

As a specific example, in this embodiment of the present application, a historical statistics module may be used to periodically summarize the user's traffic situation in each same period of time in the past i days.

For example, {A _j (ti), A _j (t-i+1), ..., A _j (t-2) and A _j (t-1)}, can be used to represent the jth time on the first day The actual number of traffic of the segment, the actual number of traffic of the jth time period of the next day, ..., the actual number of traffic of the jth time period of the first two days of the current date, and the jth of the day before the current date The actual number of traffic in the time period. In this example, A _j (t) can be used to represent the actual traffic number of the j-th time period on the t-th day, where j is an integer greater than 1.

In one embodiment, the prediction model component is a model component obtained by processing the actual traffic volume and the predicted traffic volume in the target period on the day before the specified date by using the adjustment factor parameters obtained in advance.

As an example, the predictive model component can be expressed as the following expression (1):

S _j (t)=S _j (t-1)+α(A _j (t-1)-S _j (t-1)) (1)

In the above expression (1), where α represents the dynamic adjustment factor parameter, where 0≤α≤1, S _j (t) represents the predicted traffic number of the jth time period on the t day, S _j (t-1 ) represents the predicted traffic number in the jth time period on the t-1th day, and A _j (t-1) represents the actual traffic number in the jth time period on the t-1th day.

In one embodiment, the above step S120 may specifically include the following steps.

S121: Calculate the difference between the actual traffic number in the target time period on the day before the specified date and the predicted traffic number in the target time period on the day before the specified date.

S122: Calculate the product of the pre-acquired adjustment factor parameter and the difference in the number of traffic to obtain an adjustment value of the number of traffic.

S123 , taking the sum of the predicted traffic volume in the target time period and the traffic number adjustment value on the day before the specified date as the predicted traffic volume in the target time period on the specified date.

In this embodiment, by using the prediction model component, the actual traffic volume in the target time period is processed within the predetermined number of days before the input specified date, and the predicted traffic volume in the target time period on the specified date can be obtained.

In one embodiment, before step S120, the multi-carrier processing method may further include the following steps.

S11, if the calculated fluctuation value of the number of recent traffic is greater than or equal to the fluctuation value of the number of long-term traffic, determine the adjustment factor parameter as: the ratio of the fluctuation value of the number of long-term traffic to the fluctuation value of the number of recent traffic and the predetermined first ratio the smaller value in between.

S12, if the calculated fluctuation value of the number of recent traffic is smaller than the fluctuation value of the number of long-term traffic, determine the adjustment factor parameter as: the ratio of the fluctuation value of the number of long-term traffic to the fluctuation value of the number of recent traffic and the predetermined first ratio smaller value in between.

The fluctuation value of the recent traffic number is the absolute value of the difference between the actual traffic number in the target period two days before the specified date and the actual traffic number in the target period one day before the specified date.

Wherein, the fluctuation value of the long-term traffic volume is the actual traffic volume in the target period on the first day within the predetermined number of days before the specified date, and the actual traffic volume in the target period on the second day before the specified date. The absolute value of the difference in the number of tasks.

In one embodiment, the actual number of traffic on the first day within the predetermined number of days before the specified date is the average of the number of traffic within N days from the first day within the predetermined number of days before the specified date, wherein N It is an integer greater than or equal to 3 and less than or equal to the predetermined number of days before the specified date.

As an example, in the historical traffic data, the predicted value of the t-ith day (that is, the first day within the predetermined number of days before the specified date) is the t-ith day, and the t-i+1th day (the second day within the predetermined number of days before the specified date) ), the average of the actual traffic on the t-i+2th day (the 3rd day within the predetermined number of days before the specified date).

In this example, if |A _j (t-2)-A _j (t-1)|≥|A _j (ti)-A _j (t-i+1)| The fluctuation value of the number of forward calls.

Among them, |A _j (t-2)-A _j (t-1)| represents the recent traffic volume fluctuation value, that is: the actual traffic volume A _j in the target period (jth time period) two days before the specified date (t-2), the absolute value of the difference between the actual traffic number A _j (t-1) in the target period (jth time period) one day before the specified date.

Among them, |A _j (ti)-A _j (t-i+1)| is the future traffic volume fluctuation value, that is: the first day in the target period (jth time period) within the predetermined number of days before the specified date The absolute value of the difference between the actual traffic number A _j (ti) and the actual traffic number A _j (t-i+1) in the target period (jth time period) on the second day before the specified date .

In this embodiment, if the calculated fluctuation value of the number of recent traffic is greater than or equal to the fluctuation value of the number of long-term traffic, the adjustment factor parameter α can be expressed as the following expression (2).

In the above expression (2), if the calculated fluctuation value of the number of recent traffic is equal to the fluctuation value of the number of long-term traffic, the adjustment factor parameter α takes a value of 0.5. It should be understood that in the above expression (2), the expressions A _j (t-1), A (t-2), A _j (ti), and A _j (t-i+1), which are the same as the above-mentioned embodiments The same expressions have the same meanings, and are not repeated in this embodiment of the present application.

In this example, if |A _j (t-2)-A _j (t-1)|<|A _j (ti)-A _j (ti-1)| The fluctuation value of the number of traffic, at this time, the adjustment factor parameter α can be expressed as the following expression (3).

In the above-mentioned expression (3), the expressions A _j (t-1), A(t-2), A _j (ti), and A _j (t-i+1), the same as those in the above-mentioned embodiment The expressions have the same meaning, and are not repeated in this embodiment of the present application.

In this embodiment, the adjustment factor parameter in the prediction model component can be dynamically adjusted according to the fluctuation of the number of recent traffic and the fluctuation of the number of long-term traffic, so that the prediction result of the traffic number by the prediction model component is more in line with the actual application scenario The actual traffic situation is obtained, so as to obtain a more accurate traffic number prediction result.

In one embodiment, the target time period is a time period selected from the suspected idle time period; in this embodiment, before step S110, the multi-carrier processing method may further include the following steps.

S21, for a target scenario in a multi-carrier coverage scenario, determine a busy hour period within a predetermined number of days before a specified date, wherein the actual traffic number in the busy hour period is greater than a second preset traffic number threshold.

S22 , taking a time period other than the busy time period as a suspected free time period within a predetermined number of days before a specified date in the target scene, and selecting the target time period from the suspected free time period.

In this embodiment, historical traffic data can be preliminarily screened, and if the number of traffic within a predetermined number of days in a certain time period is greater than the second preset traffic number threshold, it can be determined that the time period is busy time , the number of traffic in the busy hour period does not need to be processed by the prediction model. The remaining time periods outside the busy hour period can be judged as suspected idle hours. The data after preliminary screening is sent to the prediction model component for processing. Reduce unnecessary computation and improve the processing efficiency of predictive model components.

In this embodiment, after predicting the number of traffic in the target period in the suspected idle time period on a specified date (for example, the t-th day), the predicted traffic number is compared with a preset first traffic number threshold , if the predicted traffic count is lower than the preset first traffic count threshold, the target period is determined to be an idle time period, and the time code of the idle time period and the corresponding scene code are fed back to the network management system, and the network management system can A shutdown operation of the secondary carrier of the target scene of the target time period is performed according to the time code.

According to the multi-carrier processing method of the embodiment of the present application, in the multi-carrier coverage scenario of the 5G base station, in the target scenario of the multi-carrier coverage area, the number of traffic in the target period of the specified date can be predicted, and the idle time can be determined according to the prediction result. In the determined idle time period, the auxiliary carrier is turned off, so that a large amount of energy consumption resources can be saved under the condition of ensuring the coverage capability of the base station.

The multi-carrier processing apparatus according to the embodiments of the present application will be described in detail below with reference to the accompanying drawings. FIG. 2 shows a schematic structural diagram of a multi-carrier processing apparatus provided according to an embodiment of the present application. As shown in FIG. 2 , the multi-carrier processing apparatus may include the following modules.

The historical statistics module 210 is configured to obtain, for a target scenario in a multi-carrier coverage scenario, from historical traffic data, the actual number of traffic per day within the target period within a predetermined number of days before the specified date.

The prediction module 220 is configured to process the acquired actual traffic volume by using the prediction model component to obtain the predicted traffic volume in the target period of the specified date.

The idle period determination module 230 is configured to use the target period as the idle period in the target scene if the predicted traffic count is less than a first preset traffic count threshold.

The auxiliary carrier closing module 240 is configured to close the auxiliary carrier in the multi-carrier coverage of the idle period in the target scenario.

In one embodiment, the historical traffic data is the traffic obtained by statistics on the actual traffic number of each same period within at least the predetermined number of days before the current date for different scenarios in the multi-carrier coverage scenario data.

In one embodiment, the prediction model component is a model component obtained by processing the actual traffic volume and the predicted traffic volume in the target period on the day before the specified date by using the adjustment factor parameters obtained in advance, wherein, The value of the adjustment factor parameter is greater than or equal to 0 and less than or equal to 1.

In one embodiment, the prediction module 220 is specifically configured to: calculate the actual traffic volume in the target time period on the day before the specified date and the predicted traffic volume in the target time period on the day before the specified date difference value; calculate the product of the pre-acquired adjustment factor parameter and the difference value of the traffic number to obtain the traffic number adjustment value; compare the predicted traffic number in the target period on the day before the specified date with the said traffic number The sum of the traffic volume adjustment values is taken as the predicted traffic volume for the target period on the specified date.

In one embodiment, the multi-carrier processing apparatus may further include: a first adjustment factor parameter determination unit, configured to determine that the adjustment factor parameter is: : the smaller value between the ratio of the long-term traffic volume fluctuation value and the recent traffic volume fluctuation value and the predetermined first ratio; the second adjustment factor parameter determination unit is used for if the calculated recent traffic volume fluctuation value is greater than If the fluctuation value of the long-term traffic volume is less than or equal to the long-term traffic volume fluctuation value, the adjustment factor parameter is determined as: the ratio between the long-term traffic volume fluctuation value and the recent traffic volume fluctuation value and the predetermined first ratio, whichever is smaller value.

In this embodiment, the fluctuation value of the recent traffic volume is the difference between the actual traffic volume in the target period two days before the specified date and the actual traffic volume in the target period one day before the specified date Absolute value; the fluctuation value of the long-term traffic number is the actual traffic number in the target period on the first day within the predetermined number of days before the specified date, and the actual traffic number in the target period on the second day before the specified date. The absolute value of the difference in the number of traffic.

In one embodiment, the actual number of traffic on the first day within the predetermined number of days before the specified date is the average of the number of traffic within N days from the first day within the predetermined number of days before the specified date, wherein , N is an integer greater than or equal to 3 and less than or equal to the predetermined number of days before the specified date.

In one embodiment, the target time period is a time period selected from the suspected idle time period; in this embodiment, the multi-carrier processing apparatus may further include: a busy time period determination unit for The target scenario is to determine the busy time period within a predetermined number of days before the specified date, wherein the actual traffic number in the busy time period is greater than the second preset traffic number threshold; the target time period determination unit is used to determine the busy time period A time period other than the time period is taken as a suspected free time period within a predetermined number of days before the specified date in the target scene, and the target time period is selected from the suspected free time period.

According to the multi-carrier processing device of the embodiment of the present application, in a multi-carrier coverage scenario of a 5G base station, in a target scenario of a multi-carrier coverage area, the number of traffic in a target period of a specified date can be predicted, and the idle time can be determined according to the prediction result. In the determined idle time period, the auxiliary carrier is turned off, so that a large amount of energy consumption resources can be saved under the condition of ensuring the coverage capability of the base station.

It should be clear that the present application is not limited to the specific configurations and processes described in the above embodiments and shown in the figures. For the convenience and brevity of the description, the detailed description of the known method is omitted here, and the specific working process of the system, module and unit described above may refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

FIG. 3 is a structural diagram illustrating an exemplary hardware architecture of a computing device capable of implementing the multi-carrier processing method and apparatus according to the embodiments of the present application.

As shown in FIG. 3 , the computing device 300 includes an input device 301 , an input interface 302 , a central processing unit 303 , a memory 304 , an output interface 305 , and an output device 306 . The input interface 302, the central processing unit 303, the memory 304, and the output interface 305 are connected to each other through the bus 310, and the input device 301 and the output device 306 are respectively connected to the bus 310 through the input interface 302 and the output interface 305, and then to the computing device 300. connections to other components.

Specifically, the input device 301 receives input information from the outside, and transmits the input information to the central processing unit 303 through the input interface 302; the central processing unit 303 processes the input information based on the computer-executable instructions stored in the memory 304 to generate output information, store the output information temporarily or permanently in the memory 304, and then transmit the output information to the output device 306 through the output interface 305; the output device 306 outputs the output information to the outside of the computing device 300 for the user to use.

In one embodiment, the computing device 300 shown in FIG. 3 may be implemented as a multi-carrier processing system, and the multi-carrier processing system may include: a memory configured to store a program; a processor configured to run in the memory The stored program is used to execute the multi-carrier processing method described in the above embodiments.

According to embodiments of the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for performing the method illustrated in the flowchart. In such embodiments, the computer program may be downloaded and installed from a network, and/or installed from a removable storage medium.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can 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 that, when executed on a computer, cause the computer to perform the methods described in the various embodiments above. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission to another website site, computer, server, or data center is by wire (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state drives), and the like.

The device embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

It should be understood that the above embodiments are only exemplary embodiments adopted to illustrate the principles of the present application, but the present application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present application, and these modifications and improvements are also regarded as the protection scope of the present application.

Claims (10)

1. a multi-carrier processing method, is characterized in that, comprises: For the target scenario in the multi-carrier coverage scenario, from the historical traffic data, obtain the actual traffic count per day within the target period within the predetermined number of days before the specified date; Utilize the predictive model component to process the acquired actual traffic numbers to obtain the predicted traffic numbers for the target time period on the specified date; If the predicted traffic count is less than the first preset traffic count threshold, the target time period is used as the idle time period in the target scene; The auxiliary carrier in the multi-carrier coverage of the idle period in the target scenario is turned off. 2. The method according to claim 1, wherein The historical traffic data is the traffic data obtained by statistics on the actual traffic numbers of each same time period within at least the predetermined number of days before the current date for different scenarios in the multi-carrier coverage scenario; The prediction model component is a model component obtained by processing the actual traffic volume and the predicted traffic volume in the target period on the day before the specified date by using the pre-acquired adjustment factor parameter, wherein the adjustment factor parameter is The value is greater than or equal to 0 and less than or equal to 1. 3. The method according to claim 1, characterized in that, using the predictive model component to process the obtained actual traffic numbers to obtain the predicted traffic numbers in the target period of time on the specified date, comprising: Calculate the difference between the actual traffic number in the target period on the day before the specified date and the predicted traffic number in the target period on the day before the specified date; Calculate the product of the pre-acquired adjustment factor parameter and the difference value of the traffic number to obtain the traffic number adjustment value; Taking the sum of the predicted traffic volume in the target time period and the traffic number adjustment value on the day before the designated date as the predicted traffic volume in the target time period on the designated date. 4. The method according to claim 1, characterized in that, before the actual traffic number obtained by using the prediction model component is processed to obtain the predicted traffic number in the target time period on the specified date, the The method also includes: If the calculated fluctuation value of the number of recent traffic is greater than or equal to the fluctuation value of the number of long-term traffic, the adjustment factor parameter is determined as: the ratio between the fluctuation value of the number of long-term traffic and the fluctuation value of the number of recent traffic and the predetermined first ratio the smaller value of ; If the calculated fluctuation value of the number of recent traffic is greater than the fluctuation value of the number of long-term traffic is less than or equal to the fluctuation value of the number of long-term traffic, the adjustment factor parameter is determined as: the fluctuation value of the number of long-term traffic and the fluctuation value of the number of recent traffic the smaller of the ratio and the predetermined first ratio; Wherein, the fluctuation value of the recent traffic number is the absolute value of the difference between the actual traffic number in the target period two days before the specified date and the actual traffic number in the target period one day before the specified date; The fluctuation value of the long-term traffic number is the actual traffic number in the target period on the first day within the predetermined number of days before the specified date, and the actual traffic number in the target period on the second day before the specified date. The absolute value of the difference. 5. The method of claim 4, wherein The actual number of traffic on the first day within the predetermined number of days before the specified date is the average of the number of traffic within N days from the first day within the predetermined number of days before the specified date, where N is greater than or equal to 3 , an integer less than or equal to the predetermined number of days before the specified date. 6. The method according to any one of claims 1-5, wherein the target period is a period selected from suspected idle time periods; in the target scene for the multi-carrier coverage scene, from In the historical traffic data, before obtaining the actual number of traffic within the target period within the predetermined number of days before the specified date, including: For the target scenario in the multi-carrier coverage scenario, determine a busy hour period within a predetermined number of days before the specified date, wherein the actual traffic number in the busy hour period is greater than a second preset traffic number threshold; A time period other than the busy time period is regarded as a suspected free time period within a predetermined number of days before a specified date in the target scene, and the target time period is selected from the suspected free time period. 7. A multi-carrier processing device, comprising: The historical statistics module is used to obtain, from the historical traffic data, the actual number of traffic within the target period within the predetermined number of days before the specified date for the target scene in the multi-carrier coverage scenario; a forecasting module, used to process the obtained actual traffic numbers by using the predictive model component to obtain the predicted traffic numbers in the target period of time on the specified date; an idle period determination module, configured to use the target period as an idle period in the target scene if the predicted traffic count is less than a first preset traffic count threshold; A supplementary carrier closing module, configured to turn off the supplementary carrier in the multi-carrier coverage of the idle period in the target scenario. 8. The device of claim 7, wherein The historical traffic data is the traffic data obtained by statistics on the actual traffic numbers of each same time period within at least the predetermined number of days before the current date for different scenarios in the multi-carrier coverage scenario; The prediction model component is a model component obtained by processing the actual traffic volume and the predicted traffic volume in the target period on the day before the specified date by using the pre-acquired adjustment factor parameter, wherein the adjustment factor parameter is The value is greater than or equal to 0 and less than or equal to 1. 9. A multi-carrier processing system, comprising a memory and a processor; the memory is used for storing executable program codes; The processor is configured to read the executable program code stored in the memory to execute the multi-carrier processing method according to any one of claims 1 to 6 . 10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the multi-carrier processing method of any one of claims 1 to 6.

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