CN112118581B - Multi-carrier processing method, device, system and computer readable storage medium - Google Patents
Multi-carrier processing method, device, system and computer readable storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
- H04W16/10—Dynamic resource partitioning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/06—Testing, supervising or monitoring using simulated traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
- H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The application discloses a multi-carrier processing method, a multi-carrier processing device, a multi-carrier processing system and a computer readable storage medium. The method comprises the following steps: 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 telephone traffic number by using a prediction model component to obtain a predicted telephone traffic number of a target period on a specified date; if the predicted traffic number is smaller than a first preset traffic number threshold value, taking the target period as an idle period in the target scene; the secondary carriers in the multicarrier coverage of the idle period in the target scenario are turned off. According to the method, the auxiliary carriers in the multi-carrier can be managed, and network energy consumption resources are saved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method, an apparatus, a system, and a computer readable storage medium for processing multiple carriers.
Background
In various base station coverage scenarios of the fifth generation mobile communication technology (5th Generation Mobile Networks,5G) network, there are cases of multicarrier coverage. I.e. 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 is relatively large in the network cost of an operator, and in a multi-carrier coverage scene of the base station, when the network is idle, the coverage enhancement capability of the auxiliary carrier can bring about the condition of resource waste. Therefore, the secondary carriers in the multi-carrier need to be managed to save energy consumption resources in the network operation.
Disclosure of Invention
For this reason, the present application provides a multi-carrier processing method, apparatus, system and computer readable storage medium, so as to solve the problem of base station energy consumption and resource waste occurring when the network is idle in the prior art due to coverage enhancement capability of the auxiliary carrier.
To achieve the above object, a first aspect of the present application provides a multi-carrier processing method, including: 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 telephone traffic number by using a prediction model component to obtain a predicted telephone traffic number of a target period on a specified date; if the predicted traffic number is smaller than a first preset traffic number threshold value, taking the target period as an idle period in the target scene; the secondary carriers in the multicarrier coverage of the idle period in the target scenario are turned off.
A second aspect of the present application provides a multi-carrier processing apparatus, the 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; the prediction module is used for processing the obtained actual telephone traffic number by utilizing the prediction model component to obtain the predicted telephone traffic number of the target period on the appointed 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.
A third aspect of the present application provides 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 of the above aspects.
A fourth aspect of the present application provides a computer readable storage medium having instructions stored therein which, when executed on a computer, cause the computer to perform the multi-carrier processing method of any of the above aspects.
The application has the following advantages: according to the multi-carrier processing method, the device, the system and the computer readable storage medium, the telephone traffic number in the counted appointed target time period is processed by utilizing the preset model component through counting the time-division telephone traffic data of the multi-carrier coverage scene, so that the prediction result of the telephone traffic number is obtained, and whether the appointed target time period is an idle time period in the target scene is determined according to the prediction result of the telephone traffic number, and therefore auxiliary carriers in the multi-carrier are closed for the idle time period in the target scene, the energy consumption of a base station is reduced under the condition of ensuring the coverage capability of the base station under different coverage scenes, and the operation cost is saved.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and, together with the description, do not limit the 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 according to an embodiment of the present application;
fig. 3 is a block diagram of an exemplary hardware architecture of a computing device capable of implementing the multi-carrier processing method and apparatus according to an embodiment of the present application.
Detailed Description
The following detailed description of specific embodiments of the present application refers to the accompanying drawings. It should be understood that the detailed description is presented herein for purposes of illustration and explanation only and is 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 merely intended 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 "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the 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 the energy consumption of the base station occupies a relatively large area in the network cost of an operator, in order to save energy consumption resources in network operation, auxiliary carrier management is required to be carried out on a multi-carrier coverage scene of the base station. Currently existing management strategies include: most auxiliary carriers operate continuously for 24 hours, and only a small part of carriers are turned off briefly according to a fixed period, so that maintenance means is realized. The closing period of the method is fixed time which is initially set, flexibility is not available, and closing operation is not carried out according to actual user requirements.
The application provides a multi-carrier processing method of a 5G base station, which realizes statistical prediction according to the telephone traffic number of multiple coverage areas, so that auxiliary carriers are closed when idle so as to save a large amount of energy consumption resources.
For a better understanding of the present application, a multi-carrier processing method according to embodiments of the present application will be described in detail below with reference to the accompanying drawings, and 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 multicarrier processing method according to an embodiment of the present application. As shown in fig. 1, the multi-carrier processing method in the embodiment of the present application may include the following steps:
step S110, for a target scenario in the multi-carrier coverage scenario, acquiring an actual traffic number per day in a target period in a predetermined number of days before a specified date from the historical traffic data.
Step S120, the obtained actual telephone traffic number is processed by the prediction model component to obtain the predicted telephone traffic number of the target time period on the appointed date.
Step S130, if the predicted traffic number is smaller than the first preset traffic number threshold, the target period is taken as an idle period in the target scene.
Step S140, turning off the secondary carrier in the multi-carrier coverage of the idle period in the target scenario.
According to the multi-carrier processing method, the traffic number of the multi-carrier coverage area is statistically predicted, so that the auxiliary carrier is closed when idle so as to save a large amount 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 hot spots, dense urban areas, general urban areas, suburban areas or county and rural areas.
In one embodiment, the traffic data of the multi-layer (multi-carrier) coverage scene may be counted by the network management system at regular time, and the counted traffic data may be classified according to different scene numbers.
In one embodiment, the historical traffic data is traffic data obtained by counting actual traffic numbers for each same period of time at least the predetermined number of days before the current date for different ones of the multi-carrier coverage scenarios.
As one example, the statistical range of the historical traffic data is set to i days before the current date, i being an integer of 1 or more. The length of the time period for carrying out traffic data statistics every day can be dynamically adjusted, for example, 1 hour or 2 hours.
As a specific example, in embodiments of the present application, traffic conditions of a user during the same period of time every past i days may be summarized periodically by a history statistics module.
For example, { A j (t-i)、A j (t-i+1)、…、A j (t-2) and A j (t-1) } which can be used to represent the actual traffic number in the jth period of the first day, the actual traffic in the jth period of the second dayTraffic number, … …, actual traffic number for the j-th time period of the first two days of the current date, and actual traffic number for the j-th time period of the first day of the current date. In this example, A can be used j (t) represents the actual traffic number for the jth time period on the t-th day, where j is an integer greater than 1.
In one embodiment, the predictive model component is a model component that processes the actual traffic and the predicted traffic for the target period on a day before the specified date using pre-acquired adjustment factor parameters.
As an example, the prediction model component may be represented 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), wherein α represents a dynamic adjustment factor parameter, wherein 0.ltoreq.α.ltoreq.1, S j (t) represents the predicted traffic number for the jth time period on the t-th day, S j (t-1) represents the predicted traffic number at the jth time zone on the t-1 th day, A j (t-1) represents the actual traffic number at the jth time period on the t-1 th day.
In one embodiment, the step S120 may specifically include the following steps.
S121, calculating the difference value of the actual traffic number of the day before the appointed date in the target period and the predicted traffic number of the day before the appointed date in the target period.
S122, 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.
S123, 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.
In this embodiment, by the predictive model component, the actual traffic number per day within the target period within a predetermined number of days before the specified date is entered, the predicted traffic number within the target period on the day of the specified date can be obtained.
In one embodiment, the multi-carrier processing method may further include the following steps before step S120.
S11, if the calculated recent telephone traffic number fluctuation value is greater than or equal to the long-term telephone 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 near-term traffic number fluctuation value and the predetermined first ratio.
S12, if the calculated recent telephone traffic number fluctuation value is smaller than the long-term telephone 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 near-term traffic number fluctuation value and the predetermined first ratio.
The recent traffic number fluctuation value is the absolute value of the difference between the actual traffic number in the target period two days before the appointed date and the actual traffic number in the target period one day 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 in the preset days before the appointed date and the actual traffic number of the second day in the target period before the appointed date.
In one embodiment, the actual traffic number for the first day within a predetermined number of days before the specified date is an average of traffic numbers for N days from the first day within the predetermined number of days before the specified date, where 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.
As an example, the predicted value for day t-i (i.e., day 1 within a predetermined number of days before the specified date) in the historical traffic data is the average of the actual traffic number for day t-i, day t-i+1 (day 2 within a predetermined number of days before the specified date), day t-i+2 (day 3 within a predetermined number of days before the specified date).
In this example, if |A j (t-2)-A j (t-1)|≥|A j (t-i)-A j And (t-i+1) is that the recent traffic number fluctuation value is more than or equal to the long-term traffic number fluctuation value.
Wherein, |A j (t-2)-A j (t-1) | represents the recent traffic number fluctuation value, namely: finger meansActual traffic number a at target period (jth period) two days before the fixed date j (t-2) actual traffic number A at the target period (the jth period) one day before the specified date j (t-1) traffic number difference absolute value.
Wherein, |A j (t-i)-A j (t-i+1) | is the long-term traffic number fluctuation value, namely: actual traffic number A of the first day in the target period (the jth period) within a predetermined number of days before the specified date j (t-i) actual traffic number A in the target period (the jth period) with the next day before the specified date j Traffic number difference absolute value of (t-i+1).
In this embodiment, if the calculated recent traffic number fluctuation value is equal to or greater than the long-term traffic number fluctuation value, the adjustment factor parameter α may be expressed as the following expression (2).
In the above expression (2), if the calculated recent traffic number fluctuation value is equal to the long-term traffic number fluctuation value, the adjustment factor parameter α takes a value of 0.5. It is to be understood that in the above expression (2), expression a j (t-1)、A(t-2)、A j (t-i), and A j (t-i+1) the same expressions as those in the above embodiments have the same meanings, and the embodiments of the present application will not be repeated.
In this example, if |A j (t-2)-A j (t-1)|＜|A j (t-i)-A j (t-i-1) i, indicating that the recent traffic number fluctuation value is smaller than the long-term traffic number fluctuation value, at this time, the adjustment factor parameter α may be expressed as the following expression (3).
In the above expression (3), expression a j (t-1)、A(t-2)、A j (t-i), and A j (t-i+1) the same expressions as those in the above embodiment have the same meanings, the presentThe application embodiments are not described in detail.
In this embodiment, the adjustment factor parameters in the prediction model component can be dynamically adjusted according to the fluctuation conditions of the recent traffic number and the distant traffic number, so that the prediction result of the prediction model component on the traffic number better accords with the actual traffic condition in the actual application scene, and a more accurate traffic number prediction result is obtained.
In one embodiment, the target period is a period selected from suspected idle periods; in this embodiment, the multi-carrier processing method may further include the following steps before step S110.
S21, determining a busy hour period within a preset number of days before a specified date aiming at a target scene in the multi-carrier coverage scene, wherein the actual traffic number within the busy hour period is larger than a second preset traffic number threshold value.
S22, taking the time periods except the busy hour time period as suspected idle time periods in a preset number of days before a designated date in the target scene, and selecting the target time period from the suspected idle time periods.
In this embodiment, the historical traffic data may be initially screened, if the traffic number in a predetermined number of days in a certain period of time is greater than the second preset traffic number threshold, it may be determined that the period of time is busy, the traffic number in the busy period of time does not need to be processed using the prediction model, and when the rest period of time except the busy period of time is determined to be suspected idle, the data after being initially screened is transmitted to the prediction model component for processing, so that unnecessary calculation amount may be reduced, and processing efficiency of the prediction model component may be improved.
In this embodiment, after predicting the traffic number of the target period in the suspected idle 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 number is lower than the preset first traffic number threshold, the target period is determined to be the idle period, the time code of the idle period and the corresponding scene code are fed back to the network management system, and the network management system can execute the closing operation of the auxiliary carrier of the target scene of the target period according to the time code.
According to the multi-carrier processing method, in the multi-carrier coverage scene of the 5G base station, the telephone traffic number of the target time period of the appointed date in the target scene of the multi-carrier coverage area can be predicted, the idle time period is judged according to the prediction result, and the auxiliary carrier is closed in the judged idle time period, so that a large amount of energy consumption resources can be saved under the condition that the coverage capacity of the base station is ensured.
A multi-carrier processing apparatus according to an embodiment of the present application is described in detail below with reference to the accompanying drawings. Fig. 2 shows a schematic structural diagram of a multi-carrier processing device according to an embodiment of the present application. As shown in fig. 2, the multicarrier processing apparatus may comprise the following modules.
The historical statistics module 210 is configured to obtain, for a target scenario in the multi-carrier coverage scenario, from the historical traffic data, an actual traffic number per day in a target period within a predetermined number of days before a specified date.
A prediction module 220, configured to process the obtained actual traffic number by using a prediction model component, to obtain a predicted traffic number for the target period on the specified date.
And an idle period determining module 230, configured to take the target period as an idle period in the target scene if the predicted traffic number is less than a first preset traffic number threshold.
And the secondary carrier closing module 240 is configured to close a secondary carrier in the multi-carrier coverage of the idle period in the target scenario.
In one embodiment, the historical traffic data is traffic data obtained by counting actual traffic numbers of each same period of time at least within the predetermined number of days before the current date for different scenes in the multi-carrier coverage scene.
In one embodiment, the prediction model component is a model component obtained by processing the actual traffic number and the predicted traffic number of the target period of the day before the specified date by using a pre-acquired adjustment factor parameter, 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: 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.
In one embodiment, the multi-carrier processing apparatus may further include: the first adjustment factor parameter determining unit is configured to determine, if the calculated recent traffic number fluctuation value is greater than or equal to the long-term traffic number fluctuation value, that the adjustment factor parameter is: 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; the second adjustment factor parameter determining unit is configured to determine, if the calculated recent traffic number fluctuation value is greater than the long-term traffic number fluctuation value and less than or equal to the long-term traffic number fluctuation value, that the adjustment factor parameter is: a smaller 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.
In this embodiment, the recent traffic number fluctuation value 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 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 and the actual traffic number of the second day in the target period.
In one embodiment, the actual traffic number for the first day within a predetermined number of days before the specified date is an average of traffic numbers for N days from the first day within the predetermined number of days before the specified date, where 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 period is a period selected from suspected idle periods; in this embodiment, the multicarrier processing apparatus may further comprise: a busy hour period determining unit, configured to determine, for a target scenario in a multi-carrier coverage scenario, a busy hour period within a predetermined number of days before a specified date, where actual traffic numbers within the busy hour period are both greater than a second preset traffic number threshold; and the target period determining unit is used for taking the period except the busy hour period as a suspected idle time period in a preset number of days before a specified date in the target scene, and selecting the target period from the suspected idle time periods.
According to the multi-carrier processing device provided by the embodiment of the application, in a multi-carrier coverage scene of a 5G base station, the telephone traffic number of a target time period of a designated date in a target scene of the multi-carrier coverage area can be predicted, the idle time period is judged according to a prediction result, and the auxiliary carrier is closed in the judged idle time period, 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 arrangements and processes described in the above embodiments and shown in the drawings. For convenience and brevity of description, detailed descriptions of known methods are omitted herein, and specific working processes of the systems, modules and units described above may refer to corresponding processes in the foregoing method embodiments, which are not repeated herein.
Fig. 3 is a block diagram illustrating an exemplary hardware architecture of a computing device capable of implementing the multi-carrier processing methods and apparatus according to embodiments of the present application.
As shown in fig. 3, computing device 300 includes an input device 301, an input interface 302, a central processor 303, a memory 304, an output interface 305, and an output device 306. The input interface 302, the central processor 303, the memory 304, and the output interface 305 are connected to each other through a bus 310, and the input device 301 and the output device 306 are connected to the bus 310 through the input interface 302 and the output interface 305, respectively, and further connected to other components of the computing device 300.
Specifically, the input device 301 receives input information from the outside, and transmits the input information to the central processor 303 through the input interface 302; the central processor 303 processes the input information based on computer executable instructions stored in the memory 304 to generate output information, temporarily or permanently stores the output information in the memory 304, and then transmits the output information to the output device 306 through the output interface 305; output device 306 outputs the output information to the outside of computing device 300 for use by a user.
In one embodiment, computing device 300 shown in fig. 3 may be implemented as a multi-carrier processing system that may include: a memory configured to store a program; and a processor configured to execute a program stored in the memory to perform the multi-carrier processing method described in the above embodiment.
According to embodiments of the present application, the processes described above with reference to 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 comprising program code for performing the method shown 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 embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions which, when run on a computer, cause the computer to perform the methods described in the various embodiments above. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., 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 contains an integration of one or more available 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), etc.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
It is to be understood that the above embodiments are merely illustrative of the exemplary embodiments employed to illustrate the principles of the present application, however, the present application is not limited thereto. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the application, and are also considered to be within the 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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