CN116600336A

CN116600336A - A traffic forecasting method, device and storage medium

Info

Publication number: CN116600336A
Application number: CN202310673973.0A
Authority: CN
Inventors: 刘洋; 王伟
Original assignee: China United Network Communications Group Co Ltd
Current assignee: China United Network Communications Group Co Ltd
Priority date: 2023-06-07
Filing date: 2023-06-07
Publication date: 2023-08-15

Abstract

The application provides a traffic prediction method, a traffic prediction device and a storage medium, relates to the technical field of communication, and is used for solving the technical problem of how to predict traffic in a coverage area of a 5G base station in the prior art. The traffic prediction method comprises the following steps: determining the number of target terminals; the target terminal is a first terminal using a first service after a first preset time period; determining a target network rate; the target network rate is the average network rate of the first service after a first preset time period; determining a target service duration; the target service duration is the service duration of the target terminal using the first service after the first preset time period; and predicting the traffic of the target terminal using the first service according to the number of the target terminals, the target network rate and the target service duration.

Description

Traffic prediction method, device and storage medium

Technical Field

The present application relates to the field of communications technologies, and in particular, to a method and apparatus for traffic prediction, and a storage medium.

Background

With the rapid development of the fifth generation mobile communication technology (5th Generation Mobile Communication Technology,5G), the number of 5G base stations is also increasing, and more users can realize 5G services through the 5G base stations.

In the process of constructing a 5G base station, a planning strategy of a 5G network provided by the 5G base station needs to be determined according to traffic in the coverage area of the 5G base station, so how to accurately predict the traffic in the coverage area of the 5G base station is a problem to be solved at present.

Disclosure of Invention

The application provides a traffic prediction method, a traffic prediction device and a storage medium, which are used for solving the technical problem of how to predict traffic in a 5G base station coverage area in the prior art.

In order to achieve the above purpose, the application adopts the following technical scheme:

in a first aspect, a traffic prediction method is provided, including: determining the number of target terminals; the target terminal is a first terminal using a first service after a first preset time period; determining a target network rate; the target network rate is the average network rate of the first service after a first preset time period; determining a target service duration; the target service duration is the service duration of the target terminal using the first service after the first preset time period; and predicting the traffic of the target terminal using the first service according to the number of the target terminals, the target network rate and the target service duration.

Optionally, determining the number of target terminals includes: predicting the number of the third terminals according to the number of the second terminals; the second terminal is a terminal with a terminal price greater than the first preset price in a second preset time period; the third terminal is a terminal with a terminal price greater than the first preset price after the first preset time period; the second preset time period is before the first preset time period; acquiring the shipment rate of the first terminal in the third terminal; determining the product of the shipment rate and the number of the third terminals as the number of the first terminals after a first preset time period; determining a ratio of the number of fourth terminals to the number of second terminals as a first ratio; the fourth terminal is a terminal with the package price being greater than a second preset price in the second terminal; predicting a second ratio based on the first ratio; the second ratio is the ratio of the number of the fifth terminals to the number of the third terminals; the fifth terminal is a terminal with the package price larger than the second preset price in the third terminal; and determining the product of the number of the first terminals and the second ratio after the first preset time period as the number of the target terminals.

Optionally, determining the target network rate includes: predicting the traffic of the target class service used by the fifth terminal according to the traffic of the target class service used by the fourth terminal; determining the ratio of the traffic of the target class service used by the fifth terminal to the traffic of all the class services used by the fifth terminal as a third ratio; and determining the target network rate according to the network rate corresponding to the target class service and the third ratio.

Optionally, determining the target service duration includes: predicting a second service duration according to the first service duration; the first service duration is the service duration of the fourth terminal using the second service; the second service duration is the service duration of the fifth terminal using the first service; determining the ratio of the third service duration to the fourth service duration as a fourth ratio; the third service duration is the service duration of the second service used by the first terminal in a third preset time period; the fourth service duration is the service duration of the first service used by the first terminal in a fourth preset time period after the first planning strategy is used; the fourth preset time period is after the third preset time period; and determining the product of the second service duration and the fourth ratio as the target service duration.

Optionally, the method further comprises: when the traffic volume of the first service used by the target terminal is smaller than or equal to the traffic volume threshold corresponding to the first planning strategy, determining the first planning strategy as the target planning strategy; outputting prompt information when the traffic volume of the target terminal using the first service is larger than the traffic volume threshold corresponding to the first planning strategy; the prompt information is used for prompting the first planning strategy to be changed into the second planning strategy.

In a second aspect, there is provided a traffic prediction apparatus comprising: a determination unit and a prediction unit; a determining unit configured to determine the number of target terminals; the target terminal is a first terminal using a first service after a first preset time period; a determining unit, configured to determine a target network rate; the target network rate is the average network rate of the first service after a first preset time period; the determining unit is also used for determining the target service duration; the target service duration is the service duration of the target terminal using the first service after the first preset time period; and the prediction unit is used for predicting the traffic of the target terminal using the first service according to the number of the target terminals, the target network rate and the target service duration.

Optionally, the determining unit is specifically configured to: predicting the number of the third terminals according to the number of the second terminals; the second terminal is a terminal with a terminal price greater than the first preset price in a second preset time period; the third terminal is a terminal with a terminal price greater than the first preset price after the first preset time period; the second preset time period is before the first preset time period; acquiring the shipment rate of the first terminal in the third terminal; determining the product of the shipment rate and the number of the third terminals as the number of the first terminals after a first preset time period; determining a ratio of the number of fourth terminals to the number of second terminals as a first ratio; the fourth terminal is a terminal with the package price being greater than a second preset price in the second terminal; predicting a second ratio based on the first ratio; the second ratio is the ratio of the number of the fifth terminals to the number of the third terminals; the fifth terminal is a terminal with the package price larger than the second preset price in the third terminal; and determining the product of the number of the first terminals and the second ratio after the first preset time period as the number of the target terminals.

Optionally, the determining unit is specifically configured to: predicting the traffic of the target class service used by the fifth terminal according to the traffic of the target class service used by the fourth terminal; determining the ratio of the traffic of the target class service used by the fifth terminal to the traffic of all the class services used by the fifth terminal as a third ratio; and determining the target network rate according to the network rate corresponding to the target class service and the third ratio.

Optionally, the determining unit is specifically configured to: predicting a second service duration according to the first service duration; the first service duration is the service duration of the fourth terminal using the second service; the second service duration is the service duration of the fifth terminal using the first service; determining the ratio of the third service duration to the fourth service duration as a fourth ratio; the third service duration is the service duration of the second service used by the first terminal in a third preset time period; the fourth service duration is the service duration of the first service used by the first terminal in a fourth preset time period after the first planning strategy is used; the fourth preset time period is after the third preset time period; and determining the product of the second service duration and the fourth ratio as the target service duration.

Optionally, the method further comprises: an output unit; the determining unit is further configured to determine the first planning strategy as a target planning strategy when a traffic volume of the target terminal using the first service is less than or equal to a traffic volume threshold corresponding to the first planning strategy; the output unit is used for outputting prompt information when the traffic volume of the target terminal using the first service is larger than the traffic volume threshold corresponding to the first planning strategy; the prompt information is used for prompting the first planning strategy to be changed into the second planning strategy.

In a third aspect, a traffic prediction apparatus is provided, comprising a memory and a processor; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the traffic prediction device is operating, the processor executes computer-executable instructions stored in the memory to cause the traffic prediction device to perform the traffic prediction method according to the first aspect.

The traffic prediction means may be a network device or may be a part of a device in a network device, such as a system-on-chip in a network device. The system-on-a-chip is configured to support the network device to implement the functions involved in the first aspect and any one of its possible implementations, e.g. to obtain, determine, send data and/or information involved in the traffic prediction method described above. The chip system includes a chip, and may also include other discrete devices or circuit structures.

In a fourth aspect, there is provided a computer readable storage medium comprising computer executable instructions which, when run on a computer, cause the computer to perform the traffic prediction method of the first aspect.

In a fifth aspect, there is also provided a computer program product comprising computer instructions which, when run on a traffic prediction device, cause the traffic prediction device to perform the traffic prediction method according to the first aspect described above.

It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer readable storage medium may be packaged together with the processor of the traffic prediction device or may be packaged separately from the processor of the traffic prediction device, which is not limited by the embodiment of the present application.

The description of the second, third, fourth and fifth aspects of the present application may refer to the detailed description of the first aspect.

In the embodiment of the present application, the names of the traffic prediction devices are not limited to the devices or functional modules, and in actual implementation, the devices or functional modules may appear under other names. For example, the receiving unit may also be referred to as a receiving module, a receiver, etc. Insofar as the function of each device or function module is similar to that of the present application, it falls within the scope of the claims of the present application and the equivalents thereof.

The technical scheme provided by the application has at least the following beneficial effects:

based on any one of the above aspects, the present application provides a traffic prediction method, including: the electronic device may determine a number of target terminals, a target network rate, a target traffic duration. The target terminals are the number of the first terminals using the first service after the first preset time period. The target network rate is an average network rate of the first service after the first preset time period. The target service duration is the service duration of the target terminal using the first service after the first preset time period. In this case, the electronic device may predict the traffic of the target terminal using the first service according to the number of target terminals, the target network rate, and the target traffic duration.

From the above, the electronic device may determine the number of target terminals, the target network rate, and the target service duration, and then, the electronic device may predict the traffic of the target terminal using the first service according to the number of target terminals, the target network rate, and the target service duration. When the target terminal is a 5G terminal and the first service is a 5G service provided by the 5G base station, the electronic device can accurately predict the 5G service volume in the coverage area of the 5G base station according to the service volume of the target terminal using the first service, so that the electronic device can determine the planning strategy of the 5G network according to the 5G service volume in the coverage area of the 5G base station.

The advantages of the first, second, third, fourth and fifth aspects of the present application may be referred to in the analysis of the above-mentioned advantages, and will not be described here again.

Drawings

Fig. 1 is a schematic structural diagram of a traffic prediction system according to an embodiment of the present application;

fig. 2 is a schematic hardware structure diagram of a traffic prediction device according to an embodiment of the present application;

fig. 3 is a schematic hardware diagram of a traffic prediction device according to a second embodiment of the present application;

fig. 4 is a schematic flow chart of a traffic prediction method according to an embodiment of the present application;

fig. 5 is a second flow chart of a traffic prediction method according to an embodiment of the present application;

fig. 6 is a flow chart diagram III of a traffic prediction method according to an embodiment of the present application;

fig. 7 is a flow chart diagram of a traffic prediction method according to an embodiment of the present application;

fig. 8 is a flow chart diagram of a traffic prediction method according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a traffic prediction device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In order to clearly describe the technical solution of the embodiment of the present application, in the embodiment of the present application, the words "first", "second", etc. are used to distinguish identical items or similar items having substantially the same function and effect, and those skilled in the art will understand that the words "first", "second", etc. are not limited in number and execution order.

As described in the background art, with the rapid development of 5G, the number of 5G base stations is also increasing, and more users can implement 5G services through the 5G base stations.

In view of the above problems, the present application provides a traffic prediction method, including: the electronic device may determine a number of target terminals, a target network rate, a target traffic duration. The target terminals are the number of the first terminals using the first service after the first preset time period. The target network rate is an average network rate of the first service after the first preset time period. The target service duration is the service duration of the target terminal using the first service after the first preset time period. In this case, the electronic device may predict the traffic of the target terminal using the first service according to the number of target terminals, the target network rate, and the target traffic duration.

The traffic prediction method is suitable for a traffic prediction system. Fig. 1 shows one configuration of the traffic prediction system. As shown in fig. 1, the traffic prediction system includes: an electronic device 101, a base station 102, and a plurality of terminals 103.

Wherein the electronic device 101 is communicatively coupled to the base station 102. The base station 102 and the plurality of terminals 103 are connected in communication, respectively.

In the present application, the base station 102 can acquire the number of the plurality of terminals 103 within its coverage, i.e., the number of the plurality of terminals 103 within the history period (i.e., the second period in the present application). Then, the base station 102 may transmit the acquired number of the plurality of terminals 103 within the history period (i.e., the second period in the present application) to the electronic device 101. Thereafter, the electronic device 101 predicts the traffic volume of the first service used by the first terminal using the first service after the future period (i.e., the first period in the present application) based on the number of the plurality of terminals 103 within the history period (i.e., the second period in the present application) transmitted by the base station 102.

Wherein the first service may be a 5G service. The first terminal may be a 5G terminal.

Alternatively, the base station 102 may be a base station or a base station controller for wireless communication, etc. In the embodiment of the present application, the base station may be a base station (base transceiver station, BTS) in a global system for mobile communications (global system for mobile communication, GSM), a base station (base transceiver station, BTS) in a code division multiple access (code division multiple access, CDMA), a base station (node B) in a wideband code division multiple access (wideband code division multiple access, WCDMA), a base station (eNB) in an internet of things (internet of things, ioT) or a narrowband internet of things (NB-IoT), a base station in a future 5G mobile communication network or a future evolved public land mobile network (public land mobile network, PLMN), which is not limited in this embodiment of the present application.

Alternatively, the electronic device 101 and the base station 102 may be two devices, or may be integrated devices, which is not limited in this embodiment of the present application.

Alternatively, the plurality of terminals 103 may be devices that provide voice and/or data connectivity to the user, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The terminal may communicate with one or more core networks via a radio access network (radio access network, RAN). Terminals may be mobile terminals such as mobile telephones (or "cellular" telephones) and computers with mobile terminals, as well as portable, pocket, hand-held, computer-built-in or car-mounted mobile devices which exchange voice and/or data with radio access networks, e.g. cell phones, tablet computers, notebook computers, netbooks, personal digital assistants (personal digital assistant, PDA).

The basic hardware structure of the electronic device 101 comprises the elements comprised by the traffic prediction means shown in fig. 2 or fig. 3. The hardware configuration of the electronic device 101 will be described below using the traffic prediction apparatus shown in fig. 2 and 3 as an example.

Fig. 2 is a schematic hardware structure diagram of a traffic prediction device according to an embodiment of the present application. The traffic prediction device comprises a processor 21, a memory 22, a communication interface 23, and a bus 24. The processor 21, the memory 22 and the communication interface 23 may be connected by a bus 24.

The processor 21 is a control center of the traffic prediction device, and may be one processor or a collective term of a plurality of processing elements. For example, the processor 21 may be a general-purpose central processing unit (central processing unit, CPU), or may be another general-purpose processor. Wherein the general purpose processor may be a microprocessor or any conventional processor or the like.

As one example, processor 21 may include one or more CPUs, such as CPU 0 and CPU 1 shown in fig. 2.

Memory 22 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

In a possible implementation, the memory 22 may exist separately from the processor 21, and the memory 22 may be connected to the processor 21 by a bus 24 for storing instructions or program code. The processor 21, when calling and executing instructions or program code stored in the memory 22, is capable of implementing the traffic prediction method provided in the embodiments of the present application described below.

In the embodiment of the present application, the software program stored in the memory 22 is different for the electronic device 101, so the functions implemented by the electronic device 101 are different. The functions performed with respect to the respective devices will be described in connection with the following flowcharts.

In another possible implementation, the memory 22 may also be integrated with the processor 21.

The communication interface 23 is used for connecting the traffic prediction device with other devices through a communication network, such as ethernet, radio access network, wireless local area network (wireless local area networks, WLAN), etc. The communication interface 23 may include a receiving unit for receiving data, and a transmitting unit for transmitting data.

Bus 24 may be an industry standard architecture (industry standard architecture, ISA) bus, an external device interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 2, but not only one bus or one type of bus.

Fig. 3 shows another hardware configuration of the traffic prediction apparatus in the embodiment of the present application. As shown in fig. 3, the traffic prediction device may include a processor 31 and a communication interface 32. The processor 31 is coupled to a communication interface 32.

The function of the processor 31 may be as described above with reference to the processor 21. The processor 31 also has a memory function and can function as the memory 22.

The communication interface 32 is used to provide data to the processor 31. The communication interface 32 may be an internal interface of the traffic prediction device or an external interface (corresponding to the communication interface 23) of the traffic prediction device.

It should be noted that the structure shown in fig. 2 (or fig. 3) does not constitute a limitation of the traffic predicting apparatus, and the traffic predicting apparatus may include more or less components than those shown in fig. 2 (or fig. 3), or may combine some components, or may be a different arrangement of components.

The following describes a traffic prediction method according to an embodiment of the present application in detail with reference to the accompanying drawings.

The traffic prediction method provided by the embodiment of the present application is applied to the electronic device 101 in the traffic prediction system shown in fig. 1, as shown in fig. 4, and the traffic prediction method provided by the embodiment of the present application includes:

S401, the electronic equipment determines the number of target terminals.

The target terminal is a first terminal using a first service after a first preset time period.

Optionally, the embodiment of the present application is applied to a preset range, where the preset range may be one cell of a fourth generation mobile communication technology (4th Generation Mobile Communication Technology,4G) base station, or may be multiple cells of a 4G base station, which is not limited in the embodiment of the present application.

Alternatively, the first terminal may be all 5G terminals within a preset range, or may be a 5G terminal with a residence time greater than a threshold in the preset range. The first service may be a 5G service.

Specifically, since the embodiment of the application is applied to the cell of the 4G base station, after the 5G network is deployed, the 5G terminal in the cell of the 4G base station can realize the 5G service through the 5G network. Since the first service may be a 5G service, the first terminal may be a 5G terminal. Therefore, the electronic device may determine the number of 5G terminals (i.e., target terminals in the present application) that are converted into use of the 5G service after a future period of time (i.e., first preset period of time in the present application) by the number of terminals (i.e., second terminals in the present application) whose terminal prices are expensive (i.e., terminal prices in the present application are greater than the first preset price) in the history period of time (i.e., second preset period of time in the present application).

S402, the electronic equipment determines a target network rate.

The target network rate is an average network rate of the first service after the first preset time period.

Specifically, since the number of the target terminals may be plural, and the types of the first service may be plural, the target network rate may be an average network rate of the first service used by the target terminal after the first preset period of time. In this case, the electronic device may determine the network rate of the 5G service (i.e., the target network rate in the present application) after the future period by using the traffic volume of the 4G service by the terminal (i.e., the fourth terminal in the present application) whose terminal price is high and whose package price is high (i.e., the package price is greater than the second preset price in the present application) for the history period.

S403, the electronic equipment determines the target service duration.

The target service duration is the service duration of the target terminal using the first service after the first preset time period.

Specifically, after determining the number of target terminals and the target network rate, the electronic device may further determine a target service duration, so that the electronic device predicts a service volume of the target terminal using the first service according to the number of target terminals, the target network rate and the target service duration. In this case, the electronic device may determine the service duration of the 5G service used by the 5G terminal (i.e., the first service duration in the present application) after the future period by using the service duration of the 4G service by the terminal having a higher price in the historical period and a higher price in the package.

S404, the electronic equipment predicts the service quantity of the first service used by the target terminal according to the number of the target terminals, the target network rate and the target service duration.

Specifically, in order to determine the planning policy of the 5G network provided by the 5G base station, the electronic device may determine, according to the number of target terminals, the target network rate and the target service duration, the service volume of the target terminal using the first service. The electronic device may then determine a planning strategy for the 5G network based on the traffic volume of the target terminal using the first service.

In some embodiments, as shown in fig. 5 in conjunction with fig. 4, in S401 described above, the determining, by the electronic device, the number of target terminals specifically includes:

s501, the electronic equipment predicts the number of the third terminals according to the number of the second terminals.

The second terminal is a terminal with a terminal price greater than the first preset price in a second preset time period. The third terminal is a terminal with a terminal price greater than the first preset price after the first preset time period. The second preset time period precedes the first preset time period.

Optionally, the first preset price is a lowest price of the first terminal after the first preset time period.

Alternatively, since the average time period of the terminal for switching between the machine is 3 years to 5 years, the first preset time period may be 3 years to 5 years.

Specifically, since the terminal within the preset range has a high possibility of changing the terminal after the first preset period of time, the number of third terminals may also change. To determine the number of third terminals, the electronic device may acquire the number of second terminals. The electronic device may then predict the number of third terminals based on the number of second terminals.

Optionally, the electronic device predicts the number of the third terminals according to the number of the second terminals, and the predicting may be performed by a prediction algorithm. I.e. the electronic device may take the number of second terminals as input to the prediction algorithm and then take the value output by the prediction algorithm as the number of third terminals.

It should be noted that, since the second preset period of time is before the first preset period of time and the second preset period of time is a period of time before the 5G network is deployed, the electronic device may obtain the number of second terminals through the base station.

Alternatively, the terminals may be all 4G terminals or 5G terminals within a preset range, or may be 4G terminals or 5G terminals with a residence time greater than a threshold in the preset range, which is not limited in the embodiment of the present application.

Alternatively, the number of second terminals may be an average number of terminals having a terminal price greater than the first preset price for the second preset time period.

Alternatively, in order to determine the accuracy of the number of third terminals, the first preset time period may be equal to the second preset time period.

Alternatively, the prediction algorithm may be a machine learning algorithm (such as a linear regression function) or a non-machine learning algorithm, which is not limited in this embodiment of the present application.

Alternatively, the base station may determine the terminal model by means of the international mobile equipment identity (International Mobile Equipment Identity, IMEI) of the terminal, and then may determine the terminal price based on the terminal model.

For example, if the third terminal is a terminal whose terminal price is greater than the a-ary (i.e., the first preset price in the present application) after x months in the future (i.e., the first preset time period in the present application), then N is determined _ue Is the number of third terminals. N (N) _uen The terminal price is greater than the number of terminals of the a element in the nth month (i.e., the second preset time period in the present application) before the 5G network is deployed. The electronic device can pass through the function f1 (N _ue1 ，

N _ue2 ，……，N _uen ) (i.e., the predictive algorithm in the present application) determining N _ue . Wherein N is _ue1 Refers to the number of terminals with a terminal price greater than A element in the first month before the 5G network is deployed, N _ue2 Refers to the number of terminals with a terminal price greater than A element in the second month before the 5G network is deployed, N _uen Refers to the number of terminals with a terminal price greater than a-ary in the nth month before deployment of the 5G network.

S502, the electronic equipment obtains the shipment rate of the first terminal in the third terminal.

Specifically, after the first preset period of time, the number of the first terminals in the third terminal may change. Accordingly, in order to determine the number of first terminals in the third terminal, the electronic device may acquire the shipment rate of the first terminals in the third terminal from the device in which the terminal-related data is stored.

Alternatively, the shipment rate of the first terminal in the third terminal may be a ratio of the shipment number of the first terminal in the third terminal to the shipment number of the third terminal.

It should be noted that, the third terminal includes a 5G terminal with a terminal price greater than the first preset price after the first preset time period and a 4G terminal with a terminal price greater than the first preset price after the first preset time period.

S503, the electronic device determines the product of the shipment rate and the number of the third terminals as the number of the first terminals after the first preset time period.

Specifically, since the shipment rate is the shipment rate of the first terminal in the third terminal, the electronic device may determine, according to the shipment rate and the number of the third terminals, the number of the first terminals in the third terminal, that is, the number of the first terminals in the terminals having the terminal price greater than the first preset price after the first preset time period. And, since the first preset price is the lowest price of the first terminal, the electronic device may determine that the number of the first terminals in the third terminal is the number of the first terminals after the first preset period of time.

S504, the electronic equipment determines the ratio of the number of the fourth terminals to the number of the second terminals as a first ratio.

The fourth terminal is a terminal with a package price greater than a second preset price in the second terminal.

Optionally, the second preset price is a lowest price of the 5G package after the first preset time period.

Specifically, in order to determine the ratio of the terminals with the package price greater than the second preset price in the second terminal, the electronic device may determine the ratio of the number of the fourth terminals to the number of the second terminals as the first ratio, so that the electronic device may determine the second ratio according to the first ratio.

S505, the electronic equipment predicts a second ratio according to the first ratio.

The second ratio is a ratio of the number of the fifth terminals to the number of the third terminals. The fifth terminal is a terminal with the package price larger than the second preset price in the third terminal.

Specifically, since the fifth terminal is a terminal in which the package price in the third terminal is greater than the second preset price, and the second preset price is the lowest price of the 5G package, the number of terminals in which the package price in the third terminal is greater than the second preset price may be changed. In this way, the electronic device may predict the second ratio according to the first ratio, that is, the ratio occupied by the terminal with the package price greater than the second preset price in the terminal with the terminal price greater than the first preset price after the first preset time period.

Alternatively, the electronic device predicts the second ratio based on the first ratio and may predict by a prediction algorithm. That is, the electronic device may take the first ratio as an input to the predictive algorithm and then take the value output by the predictive algorithm as the third ratio.

Alternatively, the package price may be a monthly package fee for the terminal.

For example, assuming that Rapru is the ratio of terminals with a terminal price greater than the first preset price (i.e., the second ratio in the present application) with a package price greater than the second preset price, the electronic device may determine the value of the current value by the function f2 (Rapru ₁ ，Rapru ₂ ，……，Rapru _n ) The prediction algorithm in the present application determines Rapru. Wherein Rapru is ₁ Refers to the ratio of terminals with a price of package greater than that of element B (i.e. the second preset price in the application) in terminals with a price of terminal greater than element A in the first month before the 5G network is deployed, rapru ₂ Refers to the ratio of terminals with the price of the package being greater than that of the B element in terminals with the price of the terminal being greater than that of the A element in the second month before the 5G network is deployed, rapru _n Refers to the number of terminals with a terminal price greater than a-ary in the nth month before deployment of the 5G network.

S506, the electronic equipment determines the product of the number of the first terminals and the second ratio after the first preset time period as the number of the target terminals.

Specifically, because the second ratio is a ratio occupied by a terminal with a package price greater than the second preset price in the third terminal, the electronic device may determine that the ratio occupied by a terminal with a package price greater than the second preset price in the first terminal after the first preset time period is the second ratio. In this case, the electronic device may determine that the number of 5G terminals that open the 5G package, that is, the number of first terminals using the first service, is the number of 5G terminals that can be determined by the product of the number of first terminals and the second ratio after the first preset period.

It should be noted that, because the 5G terminal can use the 5G service after opening the 5G package, the electronic device may determine the second ratio through the first ratio, or when the 5G terminal may use the 5G service without opening the 5G package, the electronic device determines the second ratio as 1.

In some embodiments, as shown in fig. 6 in conjunction with fig. 5, in S402 described above, the determining, by the electronic device, the target network rate specifically includes:

s601, the electronic equipment predicts the traffic of the target class service used by the fifth terminal according to the traffic of the target class service used by the fourth terminal.

Specifically, the electronic device may obtain, by using the base station, a traffic volume of the target class service used by the fourth terminal, and then, the electronic device may predict, according to the traffic volume of the target class service used by the fourth terminal, a traffic volume of the target class service used by the fifth terminal.

Optionally, the electronic device predicts the traffic volume of the target class service used by the fifth terminal according to the traffic volume of the target class service used by the fourth terminal, and the traffic volume of the target class service used by the fifth terminal may be predicted by a prediction algorithm. That is, the electronic device may use the traffic of the target class service used by the fourth terminal as an input of the prediction algorithm, and then use the value output by the prediction algorithm as the traffic of the target class service used by the fifth terminal.

The electronic device can make the fourth terminal use the target class service of the fifth terminalThe traffic of the target class of service is used as input, i.e. the electronic device can be controlled by a function f3 (Si ₁ ，Si ₂ ，……，Si _n ) The Si, i.e. the traffic of the target class of service used by the fifth terminal, is determined (i.e. the predictive algorithm in the present application). Wherein Si is ₁ Refers to the traffic of the target class service used by the fourth terminal in the first month before the 5G network is deployed, si ₂ Refers to the traffic of the target class service used by the fourth terminal in the second month before the 5G network is deployed, si _n And the traffic of the target class service used by the fourth terminal in the nth month before the 5G network is deployed.

S602, the electronic equipment determines the ratio of the traffic volume of the target class service used by the fifth terminal to the traffic volume of all the class services used by the fifth terminal as a third ratio.

Specifically, after determining the traffic of the target class service used by the fifth terminal, the electronic device may add the traffic of the plurality of target class services used by the fifth terminal, thereby determining the traffic of all the class services used by the fifth terminal. Then, the electronic device may determine a ratio of the traffic volume of the target class service used by the fifth terminal to the traffic volume of all the class services used by the fifth terminal as a third ratio.

Wherein, the third ratio corresponding to different target class services is different.

It should be noted that, because the third ratio is the ratio of the traffic volume of the target class service used by the fifth terminal to the traffic volume of all the class services used by the fifth terminal, and the fifth terminal is the terminal with the package price greater than the second preset price in the third terminal after the first preset time period after the 5G network is deployed. Since the ratio of the traffic of the plurality of services before and after the deployment of the 5G network changes less, the electronic device may determine the third ratio as the ratio of the traffic after the deployment of the 5G network.

S603, the electronic equipment determines a target network rate according to the network rate corresponding to the target class service and the third ratio.

Optionally, the network rate corresponding to the target class service is a network rate corresponding to the target class service in the first service after the first preset time period (i.e., after the first preset time period after the 5G network is deployed).

Specifically, because the network rates corresponding to different target class services are different, and the third ratios corresponding to different target class services are different, the electronic device may determine a product of the network rate corresponding to the target class service and the third ratio corresponding to the target class service, and then the electronic device may determine a sum of products of the network rates corresponding to multiple classes of target class services and the third ratios as a target network rate (may also be referred to as a user perception guarantee rate), that is, a weighted average rate of all the class services.

Optionally, the network rate corresponding to the target service is a preset network rate.

It should be noted that, before deploying the 5G network, the operator may determine the expected performance of the 5G network, so the operator may preset different network rates according to the expected performance of the 5G network. When the 5G network which is expected to be deployed is a high-performance 5G network, the network rate corresponding to the preset target class service is higher. In this case, the target network rates for the different intended 5G networks are different.

Optionally, after the 5G network is deployed, the duty ratio of the different types of services in the target class of services may be changed compared to the duty ratio of the different types of services in the target class of services before the 5G network is deployed. For example, before the 5G network is deployed, most of the services with lower video quality are deployed, and most of the services with higher video quality are deployed after the 5G network is deployed, so that the duty ratio of the services with higher video quality in the video services after the 5G network is deployed is improved. In this case, the electronic device may obtain the duty ratio of the different types of services in the target type service through testing, so as to correct the network rate corresponding to the preset target type service, and determine the product of the duty ratio of the different types of services in the target type service and the network rate corresponding to the preset target type service as the corrected network rate corresponding to the preset target type service. Subsequently, the electronic device can determine the network rate corresponding to the target class service according to the duty ratio of different classes of service in the target class service and the corrected network rate corresponding to the preset target class service.

The testing method comprises the following steps: and deploying the 5G network of the first planning strategy in a preset range, and then, the electronic equipment can acquire the duty ratio of different types of services in the target type of services in a fifth preset time period through the base station in the preset range.

In combination with the above example, assuming Pi is the network rate corresponding to the i-th service, ri is the ratio of the i-th service to the total service after x months in the future, since Si is the service volume of the target service used by the fifth terminal, the electronic device may determine that the service volume S of the total service is Σsi. The electronic device may then determine Ri as Si/S. Subsequently, the electronic device may determine that the target network rate P of the first traffic is Σ (ri×pi).

In some embodiments, referring to fig. 6, as shown in fig. 7, in S403, the determining, by the electronic device, the target service duration specifically includes:

s701, the electronic equipment predicts a second service duration according to the first service duration.

The first service duration is the service duration of the fourth terminal using the second service. The second service duration is the service duration of the fifth terminal using the first service.

Alternatively, the fourth service may be a 4G service.

Optionally, the service duration may be a service duration of the terminal transmitting the service in busy hours, may be an average value in one month, or may be an average service duration of the service transmission in one month, which is not limited in the embodiment of the present application.

Specifically, before the fourth terminal deploys the 5G network, the electronic device may obtain, by using the base station, a service duration of the fourth terminal using the second service, where the package price is greater than the second preset price in the terminals with the terminal price greater than the first preset price in the second preset time period. The electronic device may then predict a second traffic duration based on the first traffic duration.

Optionally, the electronic device predicts the second service duration according to the first service duration by using a prediction algorithm. That is, the electronic device may take the first service duration as input to the predictive algorithm and then take the value output by the predictive algorithm as the second service duration.

For example, assuming Ti is the service duration of the first service used by the fifth terminal, the electronic device may take as input the service duration of the second service used by the fourth terminal, i.e., the electronic device may determine the service duration of the second service by the function f4 (Ti ₁ ，Ti ₂ ，……，Ti _n ) The Ti, i.e. the duration of the first service used by the fifth terminal, is determined (i.e. the predictive algorithm in the present application). Wherein Ti is ₁ Refers to the service duration of the 4G service used by the fourth terminal in the first month before the 5G network is deployed, ti ₂ Refers to the service duration of the 4G service used by the fourth terminal in the second month before the 5G network is deployed, ti _n And the service duration of the 4G service used by the fourth terminal in the nth month before the 5G network is deployed.

S702, the electronic equipment determines the ratio of the third service duration to the fourth service duration as a fourth ratio.

The third service duration is a service duration of the second service used by the first terminal in a third preset time period. The fourth service duration is the service duration of the first service used by the first terminal in a fourth preset time period after the first planning strategy is used. The fourth preset time period is after the third preset time period.

Specifically, after the 5G network is deployed, the service duration of the service used by the terminal may change, so the electronic device may obtain, through the base station, the service duration of the second service used by the first terminal in a third preset time period before the 5G network is deployed. Then, after the 5G network is deployed using the first planning strategy, the electronic device may obtain, by using the base station, a service duration of the first service used by the first terminal in the fourth preset time period. Subsequently, the electronic device may determine the ratio of the third service duration to the fourth service duration as a fourth ratio, that is, the fourth ratio may be used to represent a change in service duration of the service used by the 5G terminal after the 5G network is deployed.

Optionally, the third preset time period and the fourth preset time period are smaller than the first preset time period, and the third preset time period and the fourth preset time period are smaller than the second preset time period.

S703, the electronic device determines the product of the second service duration and the fourth ratio as the target service duration.

Specifically, because the fourth ratio is a change of a duration of the first terminal using the service after the 5G network is deployed using the first planning strategy, the electronic device may determine a product of the second service duration and the fourth ratio as a service duration of the target terminal using the first service, that is, the target service duration.

In some embodiments, referring to fig. 7, as shown in fig. 8, the traffic prediction method provided by the embodiment of the present application further includes:

s801, when the traffic volume of the first service used by the target terminal is smaller than or equal to the traffic volume threshold corresponding to the first planning strategy, the electronic equipment determines the first planning strategy as the target planning strategy.

Specifically, when the traffic volume of the target terminal using the first service is less than or equal to the traffic volume threshold corresponding to the first planning strategy, the electronic device may determine that the 5G network deployed using the first planning strategy satisfies the traffic volume of the target terminal using the first service. Thus, the electronic device may determine the first planning strategy as a target planning strategy, such that the operation and maintenance personnel may deploy the 5G network according to the first planning strategy.

Alternatively, the planning strategy may be to plan the bandwidth, frequency band, channel, etc. of the 5G network.

Alternatively, since the service duration may be a service duration of the terminal transmitting the service in busy hours, the traffic of the target terminal using the first service may be a busy hour traffic of the target terminal using the first service. In this case, the traffic threshold corresponding to the first planning strategy may be a threshold of maximum traffic that the 5G network can carry after using the first planning strategy.

It should be noted that, because the performances of the 5G networks expected by the operation and maintenance personnel are different, the network rates corresponding to the preset target class services are different, the network rates corresponding to the modified preset target class services are different, and the target network rates are different. In this case, the traffic volume of the target terminal determined by the electronic device using the first service is also different.

S802, when the traffic volume of the first service used by the target terminal is larger than the traffic volume threshold corresponding to the first planning strategy, the electronic equipment outputs prompt information.

The prompt information is used for prompting the change of the first planning strategy into the second planning strategy.

Specifically, when the traffic volume of the first service used by the target terminal is greater than the traffic volume threshold corresponding to the first planning strategy, the electronic device may determine that the 5G network deployed by using the first planning strategy cannot meet the traffic volume of the first service used by the target terminal, so the electronic device may output prompt information, so that the operation and maintenance personnel change the first planning strategy into the second planning strategy, and continuously predict the traffic volume of the first service used by the target terminal in the 5G network deployed by the second planning strategy.

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

The embodiment of the application can divide the functional modules of the traffic prediction device according to the method example, for example, each functional module can be divided corresponding to each function, or two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. Optionally, the division of the modules in the embodiment of the present application is schematic, which is merely a logic function division, and other division manners may be implemented in practice.

Fig. 9 is a schematic structural diagram of a traffic prediction device according to an embodiment of the present application. The traffic prediction device may be used to perform the method of traffic prediction shown in any of the fig. 4-8. The traffic prediction apparatus shown in fig. 9 includes: a determination unit 901 and a prediction unit 902;

a determining unit 901 for determining the number of target terminals; the target terminal is a first terminal using the first service after a first preset time period. For example, in connection with fig. 4, the determination unit 901 is for executing S401.

A determining unit 901, configured to determine a target network rate; the target network rate is an average network rate of the first service after the first preset time period. For example, in connection with fig. 4, the determination unit 901 is for executing S402.

A determining unit 901, configured to determine a target service duration; the target service duration is the service duration of the target terminal using the first service after the first preset time period. For example, in connection with fig. 4, the determination unit 901 is for executing S403.

A prediction unit 902, configured to predict a traffic volume of the target terminal using the first service according to the number of target terminals, the target network rate and the target service duration. For example, in connection with fig. 4, the prediction unit 902 is used to perform S404.

Optionally, the determining unit 901 is specifically configured to:

predicting the number of the third terminals according to the number of the second terminals; the second terminal is a terminal with a terminal price greater than the first preset price in a second preset time period; the third terminal is a terminal with a terminal price greater than the first preset price after the first preset time period; the second preset time period precedes the first preset time period. For example, in connection with fig. 5, the determination unit 901 is for executing S501.

And acquiring the shipment rate of the first terminal in the third terminal. For example, in connection with fig. 5, the determination unit 901 is for executing S502.

And determining the product of the shipment rate and the number of the third terminals as the number of the first terminals after the first preset time period. For example, in connection with fig. 5, the determination unit 901 is for executing S503.

Determining a ratio of the number of fourth terminals to the number of second terminals as a first ratio; the fourth terminal is a terminal with the package price being greater than the second preset price in the second terminal. For example, in connection with fig. 5, the determination unit 901 is for executing S504.

Predicting a second ratio based on the first ratio; the second ratio is the ratio of the number of the fifth terminals to the number of the third terminals; the fifth terminal is a terminal with the package price larger than the second preset price in the third terminal. For example, in connection with fig. 5, the determination unit 901 is for executing S505.

And determining the product of the number of the first terminals and the second ratio after the first preset time period as the number of the target terminals. For example, in connection with fig. 5, the determination unit 901 is for executing S506.

Optionally, the determining unit 901 is specifically configured to:

and predicting the traffic of the target class service used by the fifth terminal according to the traffic of the target class service used by the fourth terminal. For example, in connection with fig. 6, the determination unit 901 is for executing S601.

And determining the ratio of the traffic of the target class service used by the fifth terminal to the traffic of all the class services used by the fifth terminal as a third ratio. For example, in connection with fig. 6, the determination unit 901 is for executing S602.

And determining the target network rate according to the network rate corresponding to the target class service and the third ratio. For example, in connection with fig. 6, the determination unit 901 is for executing S603.

Optionally, the determining unit 901 is specifically configured to:

predicting a second service duration according to the first service duration; the first service duration is the service duration of the fourth terminal using the second service; the second service duration is the service duration of the fifth terminal using the first service. For example, in connection with fig. 7, the determination unit 901 is for executing S701.

Determining the ratio of the third service duration to the fourth service duration as a fourth ratio; the third service duration is the service duration of the second service used by the first terminal in a third preset time period; the fourth service duration is the service duration of the first service used by the first terminal in a fourth preset time period after the first planning strategy is used; the fourth preset time period is after the third preset time period. For example, in connection with fig. 7, the determination unit 901 is for executing S702.

And determining the product of the second service duration and the fourth ratio as the target service duration. For example, in connection with fig. 7, the determination unit 901 is for executing S703.

Optionally, the method further comprises: an output unit 903;

the determining unit 901 is further configured to determine the first planning policy as the target planning policy when a traffic volume of the target terminal using the first service is less than or equal to a traffic volume threshold corresponding to the first planning policy. For example, in connection with fig. 8, the determination unit 901 is for executing S801.

An output unit 903, configured to output a prompt message when a traffic volume of the target terminal using the first service is greater than a traffic volume threshold corresponding to the first planning policy; the prompt information is used for prompting the first planning strategy to be changed into the second planning strategy. For example, in connection with fig. 8, the output unit 903 is used to execute S802.

The embodiment of the application also provides a computer readable storage medium, which comprises computer execution instructions, when the computer execution instructions run on a computer, cause the computer to execute the traffic prediction method provided in the embodiment.

The embodiment of the application also provides a computer program which can be directly loaded into a memory and contains software codes, and the computer program can realize the traffic prediction method provided by the embodiment after being loaded and executed by a computer.

Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the present application may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer-readable storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and the division of modules or units, for example, is merely a logical function division, and other manners of division are possible when actually implemented. For example, multiple units or components may be combined or may be integrated into another device, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present application should be included in the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. A traffic prediction method, comprising:

determining the number of target terminals; the target terminal is a first terminal using a first service after a first preset time period;

determining a target network rate; the target network rate is the average network rate of the first service after the first preset time period;

determining a target service duration; the target service duration is the service duration of the target terminal using the first service after the first preset time period;

and predicting the traffic of the target terminal using the first service according to the number of the target terminals, the target network rate and the target service duration.

2. The traffic prediction method according to claim 1, wherein the determining the number of target terminals includes:

predicting the number of the third terminals according to the number of the second terminals; the second terminal is a terminal with a terminal price greater than the first preset price in a second preset time period; the third terminal is a terminal with the terminal price being greater than the first preset price after the first preset time period; the second preset time period is before the first preset time period;

Acquiring the shipment rate of the first terminal in the third terminal;

determining the product of the shipment rate and the number of the third terminals as the number of the first terminals after the first preset time period;

determining a ratio of the number of fourth terminals to the number of second terminals as a first ratio; the fourth terminal is a terminal with a package price greater than a second preset price in the second terminal;

predicting a second ratio according to the first ratio; the second ratio is the ratio of the number of fifth terminals to the number of third terminals; the fifth terminal is a terminal in which the package price is greater than the second preset price in the third terminal;

and determining the product of the number of the first terminals and the second ratio after the first preset time period as the number of the target terminals.

3. The traffic prediction method according to claim 2, wherein the determining the target network rate comprises:

predicting the traffic of the target class service used by the fifth terminal according to the traffic of the target class service used by the fourth terminal;

determining the ratio of the traffic volume of the target class service used by the fifth terminal to the traffic volume of all the class services used by the fifth terminal as a third ratio;

And determining the target network rate according to the network rate corresponding to the target class service and the third ratio.

4. The traffic prediction method according to claim 3, wherein the determining the target service duration comprises:

predicting a second service duration according to the first service duration; the first service duration is the service duration of the fourth terminal using the second service; the second service duration is the service duration of the fifth terminal using the first service;

determining the ratio of the third service duration to the fourth service duration as a fourth ratio; the third service duration is the service duration of the second service used by the first terminal in a third preset time period; the fourth service duration is the service duration of the first terminal using the first service in the fourth preset time period after the first planning strategy is used; the fourth preset time period is after the third preset time period;

and determining the product of the second service duration and the fourth ratio as the target service duration.

5. The traffic prediction method according to any one of claims 1 to 4, characterized by further comprising:

When the traffic volume of the first service used by the target terminal is smaller than or equal to a traffic volume threshold corresponding to the first planning strategy, determining the first planning strategy as a target planning strategy;

outputting prompt information when the traffic volume of the first service used by the target terminal is larger than the traffic volume threshold corresponding to the first planning strategy; the prompt message is used for prompting the first planning strategy to be changed into the second planning strategy.

6. A traffic prediction apparatus, comprising: a determination unit and a prediction unit;

the determining unit is used for determining the number of target terminals; the target terminal is a first terminal using a first service after a first preset time period;

the determining unit is further used for determining a target network rate; the target network rate is the average network rate of the first service after the first preset time period;

the determining unit is further used for determining a target service duration; the target service duration is the service duration of the target terminal using the first service after the first preset time period;

the predicting unit is configured to predict, according to the number of the target terminals, the target network rate, and the target service duration, a service amount of the target terminal using the first service.

7. The traffic prediction device according to claim 6, wherein the determining unit is specifically configured to:

acquiring the shipment rate of the first terminal in the third terminal;

8. The traffic prediction device according to claim 7, wherein the determining unit is specifically configured to:

9. The traffic prediction device according to claim 8, wherein the determining unit is specifically configured to:

10. The traffic prediction device according to any one of claims 6 to 9, further comprising: an output unit;

the determining unit is further configured to determine, when the traffic volume of the target terminal using the first service is less than or equal to a traffic volume threshold corresponding to the first planning policy, the first planning policy as a target planning policy;

the output unit is used for outputting prompt information when the traffic volume of the target terminal using the first service is larger than the traffic volume threshold corresponding to the first planning strategy; the prompt message is used for prompting the first planning strategy to be changed into the second planning strategy.

11. A traffic prediction device, comprising a memory and a processor; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the traffic prediction device is running, the processor executes the computer-executable instructions stored in the memory to cause the traffic prediction device to perform the traffic prediction method according to any one of claims 1-5.

12. A computer readable storage medium comprising computer executable instructions which, when run on a computer, cause the computer to perform the traffic prediction method according to any of claims 1-5.