CN112118581B - Multi-carrier processing method, device, system and computer readable storage medium - Google Patents

Abstract

The application discloses a multi-carrier processing method, device, system and computer-readable storage medium. The method includes: for a target scene in a multi-carrier coverage scenario, from historical traffic data, obtaining the actual traffic number in a target period within a predetermined number of days before a specified date; using a predictive model component to process the obtained actual traffic Traffic number, get the predicted traffic number in the target time period of the specified date; if the predicted traffic number is less than the first preset traffic number threshold, then use the target time period as the idle time period in the target scene; turn off the idle time period in the target scene Supplementary 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 to save network energy consumption resources.

Description

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

Background technique

In various base station coverage scenarios of the fifth generation mobile communication technology (5th Generation Mobile Networks, 5G) network, there is a case of multi-carrier coverage. That is, one carrier is used to ensure the basic coverage of the signal in the coverage scenario of the base station, 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 cause waste of resources. Therefore, it is necessary to manage the auxiliary carrier in the multi-carrier, so as to save energy consumption resources in network operation.

Contents of the invention

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

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

The second aspect of the present application provides a multi-carrier processing device, which includes: a historical statistics module, for a target scenario in a multi-carrier coverage scenario, from historical traffic data, obtain the daily traffic data within a predetermined number of days before the specified date The actual traffic number in the target time period; the prediction module is used to process the acquired actual traffic number by using the prediction model component, and obtains the predicted traffic number in the target time period of the specified date; the idle period determination module is used for if the predicted If the number of traffic is less than the first preset traffic number threshold, the target period is used as an 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.

The third aspect of the present application provides a multi-carrier processing system, including a memory and a processor; the memory is used to store executable program code; the processor is used to read the executable program code stored in the memory to perform any 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 run on a computer, the computer is made to execute the multi-carrier processing method of any aspect above.

The present application has the following advantages: according to the multi-carrier processing method, device, system and computer-readable storage medium in the embodiment of the present application, by counting the segmented traffic data of the multi-carrier coverage scene, using the preset model component to specify the statistics The traffic number in the target time period is processed to obtain the prediction result of the traffic number, so as to determine whether the specified target time period is an idle time period in the target scene according to the prediction result of the traffic number, so as to target the idle time period in the target scene , Turn off the auxiliary carrier in the multi-carrier, realize different coverage scenarios, reduce the energy consumption of the base station and save operating costs under the condition of ensuring the coverage 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 are used together with the following specific embodiments 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 in an embodiment of the present application;

FIG. 2 is a schematic structural diagram of a multi-carrier processing device provided in 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 implementation manners of the present application will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementations described here are only used to illustrate and explain the present application, and are not intended to limit the present application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

It should be noted that, in this document, the terms "comprising", "comprising" or any other variation thereof are intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or apparatus. Without further limitations, an element defined by the statement "comprising..." does not exclude the presence of additional same elements in the process, method, article or device comprising said element.

The communication network system in the embodiment 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.

Because base station energy consumption accounts for a large proportion of the operator's network cost, in order to save energy consumption resources in 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 part of the carriers will be temporarily shut down according to a fixed period as a means of maintenance. The shutdown period of this method is an initially set fixed time, which lacks flexibility and does not perform a shutdown operation according to actual user needs.

This application proposes a multi-carrier processing method for 5G base stations, which realizes statistical prediction based on traffic numbers in multiple coverage areas, so that auxiliary carriers are turned off during idle times to save a lot of energy consumption resources.

In order to better understand 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 Figure 1, the multi-carrier processing method in the embodiment of the present application may include the following steps:

Step S110, for the target scene in the multi-carrier coverage scene, from the historical traffic data, the actual traffic number in the target time period in a predetermined number of days before the specified date is obtained every day.

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 an idle time period in the target scene.

Step S140, turning off the auxiliary carrier in the multi-carrier coverage of the idle period in the target scene.

According to the multi-carrier processing method of the embodiment of the present application, statistical prediction is made on the number of traffic in the coverage area of the multi-carrier, so that auxiliary carriers are turned off during idle times to save a lot of energy consumption resources.

In an embodiment, the target scene in the multi-carrier coverage scene may include any one of the following scenes: traffic hot spots, dense urban areas, general urban areas, suburbs or county towns, and rural areas.

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

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

As an example, the statistics range of the historical traffic data is set to be i days before the current date, where i is an integer greater than or equal to 1. Wherein, the length of the time period for collecting traffic data statistics every day can be dynamically adjusted, for example, it can be 1 hour or 2 hours.

As a specific example, in the embodiment of the present application, the traffic situation of the user in each same time period in the past i days may be regularly summarized through the historical statistics module.

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 traffic number of the segment, the actual traffic number of the jth time period of the next day, ..., the actual traffic number of the jth time period two days before the current date, and the jth time period of the previous day of the current date The actual number of traffic in the time period. In this example, A _j (t) may be used to represent the actual traffic number of the jth time period on the tth 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 number and the predicted traffic number in the target period of the day before the specified date by using the adjustment factor parameters acquired in advance.

As an example, a 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, among them, 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 on day t-1 in time period j, and A _j (t-1) represents the actual traffic number on day t-1 in time period j.

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

S121. Calculate the traffic 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 traffic number difference to obtain the traffic number adjustment value.

S123. Use the sum of the predicted traffic number in the target time period on the day before the designated date and the traffic number adjustment value as the predicted traffic number in the target time period on the designated date.

In this embodiment, the predicted traffic number in the target period on the day of the specified date can be obtained by processing the actual traffic number in the target period within a predetermined number of days before the input specified date through the forecasting model component.

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

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

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

Wherein, the fluctuation value of the recent traffic volume is the absolute value of 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.

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 transaction numbers.

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 value of the traffic within N days from the first day within the predetermined number of days before the specified date, where 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, the predicted value of the t-i day (that is, the first day within the predetermined number of days before the specified date) in the historical traffic data is the t-i day, the t-i+1 day (the second day within the predetermined number of days before the specified date) ), the average value of the actual traffic in the t-i+2th day (the third 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)| means that the recent traffic fluctuation value is greater than or equal to Fluctuation value of long-term traffic.

Among them, |A _j (t-2)-A _j (t-1)| represents the fluctuation value of the recent traffic number, that is, the actual traffic number A _j in the target time 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) and 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 long-term traffic fluctuation value, that is, the first day within the predetermined number of days before the specified date in the target period (jth time period) 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 recent traffic number is greater than or equal to the fluctuation value of the long-term traffic number, the adjustment factor parameter α can be expressed as the following expression (2).

In the above expression (2), if the calculated short-term traffic fluctuation value is equal to the long-term traffic fluctuation value, then the value of the adjustment factor parameter α is 0.5. It should be understood that in the above-mentioned expression (2), the expressions A _j (t-1), A(t-2), A _j (ti), and A _j (t-i+1), and the above-mentioned embodiment The same expressions in have the same meanings, which will not be repeated in the embodiments 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 traffic number, 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 in the above-mentioned embodiment The expressions have the same meaning, which will not be 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 recent traffic number and the fluctuation of the long-term traffic number, so that the prediction result of the forecast model component on the traffic number is more in line with the actual application scenario. The actual traffic situation, so as to obtain more accurate traffic prediction results.

In one embodiment, the target time period is a time period selected from suspected idle time periods; 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 time period within a predetermined number of days before a specified date, wherein the actual traffic numbers in the busy time period are all greater than a second preset traffic number threshold.

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

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

In this embodiment, after predicting the traffic number of the target period in the suspected idle time period on a specified date (for example, day t), the predicted traffic number is compared with the preset first traffic number threshold , if the predicted traffic number is lower than the preset first traffic number threshold, it is determined that the target time period is 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 The closing operation of the auxiliary 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 traffic number of the target period of the specified date can be predicted, and the idle time can be determined according to the prediction result. During the time period, the auxiliary carrier is turned off during the determined idle time period, so as to save a large amount of energy consumption resources while ensuring the coverage capability of the base station.

The multi-carrier processing apparatus according to the embodiment 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 device provided according to an embodiment of the present application. As shown in Fig. 2, the device for multi-carrier processing may include the following modules.

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

The prediction module 220 is configured to process the obtained actual traffic numbers by using a forecast model component to obtain the predicted traffic numbers in the target time period on the specified date.

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

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

In one embodiment, the historical traffic data is traffic statistics obtained by counting the actual traffic numbers in each same period of 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 forecast model component is a model component obtained by processing the actual traffic number and the predicted traffic number in the target period of the day before the specified date by using pre-acquired adjustment factor parameters, 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 forecasting module 220 is specifically configured to: calculate the actual traffic number of the day before the specified date in the target period and the predicted traffic number of the day before the specified date in the target period Calculate the product of the adjustment factor parameter obtained in advance and the difference in the traffic number to obtain the adjusted value of the traffic number; compare the predicted traffic number in the target period of the day before the specified date with the The sum of traffic number adjustment values is used as the predicted traffic number in the target time period on the specified date.

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

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 value of the 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 device may further include: a busy time period determining unit, configured to target the time period in the multi-carrier coverage scenario The target scenario is to determine the busy time period within a predetermined number of days before the specified date, wherein the actual traffic numbers in the busy time period are all greater than the second preset traffic number threshold; the target time period determining unit is used to set the busy time period A time period other than the time period is used as a suspected idle 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 idle time period.

According to the multi-carrier processing device 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 traffic number of the target period of the specified date can be predicted, and the idle time can be determined according to the prediction result. During the time period, the auxiliary carrier is turned off during the determined idle time period, so as to save a large amount of energy consumption resources while 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 description, detailed descriptions of known methods are omitted here, and the specific working processes of the above-described systems, modules, and units can refer to the corresponding processes in the foregoing method embodiments, and are not repeated here.

FIG. 3 is a structural diagram showing 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 . Wherein, 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 connected to the computing device 300 other component connections.

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 computer-executable instructions stored in the memory 304 to generate output information, temporarily or permanently store the output information 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 programs; a processor configured to run the The stored program is used to execute the multi-carrier processing method described in the above embodiments.

According to an embodiment 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 including a computer program tangibly embodied on a machine-readable medium, the computer program including program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network, and/or from a removable storage medium.

In the above embodiments, all or part of them may be implemented by software, hardware, firmware or any combination thereof. When implemented using software, 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, which, when run on a computer, cause the computer to execute the methods described in the foregoing embodiments. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. 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 transmitted from a website, computer, server or data center Transmission to another website site, computer, server, or data center by wired (eg, coaxial cable, optical fiber, 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 or a data center integrated with one or more available media. The available media may be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, DVD), or semiconductor media (eg, solid state hard disk), etc.

The device embodiments described above are only illustrative, and 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 to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without any creative efforts.

It can be understood that the above implementations are only exemplary implementations adopted to illustrate the principle 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 application, and these modifications and improvements are also regarded as the protection scope of the application.

Claims (10)

1. A method of multi-carrier processing, comprising:

for a target scene in the multi-carrier coverage scene, acquiring the actual traffic number in a target period of time per day in a preset day before a specified date from historical traffic data;

processing the obtained actual traffic number by using a prediction model component to obtain a predicted traffic number of the target period on the appointed date; the predictive model component is for: summing the product of the adjustment factor parameter and the first value with the predicted traffic number of the target period of time on the day before the appointed date, and taking the sum result as the predicted traffic number of the target period of time on the appointed date; the first value is: a traffic number difference between an actual traffic number and a corresponding predicted traffic number for the target period a day prior to the specified date;

the adjustment factor parameter is a parameter which is dynamically adjusted according to the magnitude difference of the recent telephone traffic number fluctuation value and the long-term telephone traffic number fluctuation value; the recent traffic number fluctuation value is the absolute value of the difference between the actual traffic number of the first day in the target period within two days before the appointed date and the actual traffic number of the first day in the target period before the appointed date; the long-term traffic number fluctuation value is the absolute value of the difference between the actual traffic number of the first day in the target period within a preset day before a specified date and the actual traffic number of the second day in the target period within a preset day before the specified date;

if the predicted traffic number is smaller than a first preset traffic number threshold, the target period is used as an idle period in the target scene;

and closing the auxiliary carrier in the multi-carrier coverage of the idle period in the target scene.

2. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the historical telephone traffic data is telephone traffic data obtained by counting the actual telephone traffic number of each same time period in at least the preset days before the current date aiming at different scenes in the multi-carrier coverage scene;

the prediction model component is a model component obtained by processing the actual telephone traffic number and the predicted telephone traffic number of the target period of the day before the appointed date by utilizing the pre-acquired adjustment factor parameter, wherein the value of the adjustment factor parameter is more than or equal to 0 and less than or equal to 1.

3. The method of claim 1, wherein the processing the obtained actual traffic number with the predictive model component to obtain a predicted traffic number for the target period of time on the specified date comprises:

calculating a traffic number difference value of an actual traffic number of a day before a specified date in the target period and a predicted traffic number of the day before the specified date in the target period;

calculating the product of the pre-acquired adjustment factor parameter and the telephone traffic number difference value to obtain a telephone traffic number adjustment value;

and taking the sum of the predicted traffic number of the target period and the traffic number adjustment value on the day before the appointed date as the predicted traffic number of the target period on the appointed date.

4. The method of claim 1, wherein prior to said processing the actual traffic number acquired with the predictive model component to obtain a predicted traffic number for the target period of time on the specified date, the method further comprises:

if the calculated recent traffic number fluctuation value is greater than or equal to the long-term traffic number fluctuation value, determining the adjustment factor parameters as follows: a smaller value between the ratio of the long-term traffic number fluctuation value to the recent traffic number fluctuation value and the predetermined first ratio;

if the calculated recent traffic number fluctuation value is smaller than the long-term traffic number fluctuation value, determining the adjustment factor parameters as follows: a larger value between the ratio of the long-term traffic number fluctuation value to the near-term traffic number fluctuation value and the predetermined first ratio.

5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,

the actual traffic number of the first day in the preset days before the appointed date is the average value of the traffic numbers of N days from the first day in the preset days before the appointed date, wherein N is an integer which is more than or equal to 3 and less than or equal to the preset days before the appointed date.

6. The method according to any one of claims 1-5, wherein the target period is a period selected from suspected idle periods; before acquiring the actual traffic number in the target period of time per day in a preset day before a specified date from historical traffic data aiming at the target scene in the multi-carrier coverage scene, the method comprises the following steps:

determining a busy hour period within a predetermined number of days before a specified date for a target scene in the multi-carrier coverage scene, wherein actual traffic numbers within the busy hour period are all greater than a second preset traffic number threshold;

and taking the time periods except the busy hour time period as suspected idle time periods in a preset number of days before a specified date in the target scene, and selecting the target time period from the suspected idle time periods.

7. A multi-carrier processing apparatus, comprising:

the historical statistics module is used for acquiring the actual telephone traffic number of each day in a target period of time within a preset day before a specified date from historical telephone traffic data aiming at a target scene in the multi-carrier coverage scene;

a prediction module for processing the obtained actual traffic number by using a prediction model component to obtain a predicted traffic number of the target period on the appointed date; the predictive model component is for: summing the product of the adjustment factor parameter and the first value with the predicted traffic number of the target period of time on the day before the appointed date, and taking the sum result as the predicted traffic number of the target period of time on the appointed date; the first value is: a traffic number difference between an actual traffic number and a corresponding predicted traffic number for the target period a day prior to the specified date;

the adjustment factor parameter is a parameter which is dynamically adjusted according to the magnitude difference of the recent telephone traffic number fluctuation value and the long-term telephone traffic number fluctuation value; the recent traffic number fluctuation value is the absolute value of the difference between the actual traffic number of the first day in the target period within two days before the appointed date and the actual traffic number of the first day in the target period before the appointed date; the long-term traffic number fluctuation value is the absolute value of the difference between the actual traffic number of the first day in the target period within a preset day before a specified date and the actual traffic number of the second day in the target period within a preset day before the specified date;

the idle period judging module is used for taking the target period as the idle period in the target scene if the predicted traffic number is smaller than a first preset traffic number threshold value;

and the auxiliary carrier closing module is used for closing auxiliary carriers in the multi-carrier coverage of the idle period in the target scene.

8. The apparatus of claim 7, wherein the device comprises a plurality of sensors,

the historical telephone traffic data is telephone traffic data obtained by counting the actual telephone traffic number of each same time period in at least the preset days before the current date aiming at different scenes in the multi-carrier coverage scene;

the prediction model component is a model component obtained by processing the actual telephone traffic number and the predicted telephone traffic number of the target period of the day before the appointed date by utilizing the pre-acquired adjustment factor parameter, wherein the value of the adjustment factor parameter is more 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 executable program code stored in the memory to perform the multi-carrier processing method of any one of claims 1 to 6.

10. A computer readable storage medium comprising instructions which, when run 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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