CN113015116B - Dynamic quota method and device based on flow prediction - Google Patents
Dynamic quota method and device based on flow prediction Download PDFInfo
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- CN113015116B CN113015116B CN201911326792.0A CN201911326792A CN113015116B CN 113015116 B CN113015116 B CN 113015116B CN 201911326792 A CN201911326792 A CN 201911326792A CN 113015116 B CN113015116 B CN 113015116B
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Abstract
The invention discloses a dynamic quota method and a device based on flow prediction, wherein the method comprises the following steps: predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain a predicted internet surfing flow type; inquiring a flow residual value corresponding to the predicted internet surfing flow type stored in the flow classification inquiring module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type; and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type. The dynamic flow quota value is dynamically calculated based on the user internet access habit and the flow residual values of different flow types, so that the allocation is more accurate and more conforms to the current internet access habit of the user, the load of a signaling network and a support network is reduced, and the resource utilization rate is improved.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a dynamic quota method and a dynamic quota device based on flow prediction.
Background
With the widespread use of the mobile internet, a data traffic billing system is widely used, and the current data traffic billing methods include: online charging and offline charging. Online Charging (OCS) is the most mainstream traffic Charging mode at present, and Online Charging can perform dynamic traffic quota authorization during the internet access process of a user, and perform real-time user reminding and fee deduction according to the use condition of the user, thereby greatly improving the user internet access reminding experience.
However, the conventional OCS uses a uniform static quota value, which may cause system inefficiency, resource waste, and reduced user satisfaction, for example, allocating the same quota value to a user watching video on the internet and a user chatting on the internet may cause the user watching video to frequently send a large amount of meaningless quota applications to the OCS, which may cause high load on both the signaling network and the support network, which may easily cause message delay, resource waste, and rate jitter, and poor user experience. The method for statically acquiring the quota value cannot meet the real-time charging requirement of a high-speed data flow network. On the other hand, with a series of domestic hot applications and the vigorous development of short videos, for example, directional traffic such as the first item, the fast hand, the migu and the like in this day, internet traffic is greatly subdivided on network element equipment, operators also popularize various directional traffic products in a large area, and on the background that traffic is continuously classified, a new mechanism for predicting according to type and calculating quota intelligently and dynamically is needed by a traffic online charging system, so that resource waste in a high-network-speed environment is avoided, and the user satisfaction is improved.
Disclosure of Invention
In view of the above, the present invention is proposed to provide a dynamic quota based on traffic prediction method and apparatus that overcomes or at least partially solves the above mentioned problems.
According to an aspect of the present invention, there is provided a dynamic quota method based on traffic prediction, including the following steps:
predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain a predicted internet surfing flow type; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types;
inquiring a flow residual value corresponding to the predicted network traffic type stored in a traffic classification inquiry module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type;
and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type.
According to another aspect of the present invention, there is provided a dynamic quota apparatus based on traffic prediction, including:
the cache module is suitable for storing historical internet surfing data; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types;
the traffic classification query module is suitable for storing traffic use total values and traffic residual values corresponding to all internet traffic types;
the prediction module is suitable for predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain the predicted internet surfing flow type;
the query module is suitable for querying the traffic residual value corresponding to the predicted internet traffic type stored in the traffic classification query module;
and the quota value calculation module is suitable for calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow residual value corresponding to the predicted internet flow type.
According to yet another aspect of the present invention, there is provided a computing device comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the dynamic quota method based on the flow prediction.
According to still another aspect of the present invention, a computer storage medium is provided, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform an operation corresponding to the above dynamic quota method based on traffic prediction.
According to the dynamic quota method and device based on the traffic prediction, the next internet surfing behavior of the user is predicted according to historical internet surfing data stored in the cache module, and the predicted internet surfing traffic type is obtained; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types; inquiring a flow residual value corresponding to the predicted network traffic type stored in a traffic classification inquiry module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type; and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type. The method and the device predict the next internet surfing behavior of the user based on the recent internet surfing habit of the user, predict which type of flow the user uses next time, further inquire the flow residual value of the type of the user, and finally calculate the dynamic quota, so that the dynamic flow quota value is calculated according to the internet surfing habit of the user and the flow residual values of different flow types, the distribution is more accurate, the current internet surfing habit of the user is better met, the load of a signaling network and a supporting network is reduced, and the resource utilization rate is improved.
The above description is only an overview of the technical solutions of the present invention, and the present invention can be implemented in accordance with the content of the description so as to make the technical means of the present invention more clearly understood, and the above and other objects, features, and advantages of the present invention will be more clearly understood.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a dynamic quota method based on traffic prediction according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a dynamic quota method based on traffic prediction according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating a dynamic quota device based on traffic prediction according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computing device provided in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Example one
Fig. 1 is a flowchart illustrating an embodiment of a dynamic quota method based on traffic prediction according to the present invention, where as shown in fig. 1, the method includes the following steps:
s101: and predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain the predicted internet surfing flow type.
Specifically, in this step, according to the historical internet surfing data of the user stored in the cache module, the historical internet surfing data includes: historical internet traffic type (Servicecode ID, SID) and a time period accumulated traffic value corresponding to the historical internet traffic type. As shown in table 1, the historical internet surfing data stored in the cache module is stored, specifically, the historical internet surfing data is stored in the internet traffic cache table, where SID is an internet traffic type, SID 2000000002 is "CMNET traffic", SID 1750000002 is "forward direction-youku video", and SID 1750000013 is "forward direction-express hand". Specifically, as shown in table 1, the first 3 rows listed behind the head of the meter are monthly accumulated flow values corresponding to the SIDs, where the CMNET flow accumulated flow value is 100M, the kuku video accumulated flow value is 1G, the express accumulated flow value is 3G, and the 4 th row listed behind the head of the meter is the internet access flow type reported by the last CCR packet and the time period accumulated flow value corresponding to the type.
TABLE 1 caching table for internet traffic
In this step, for an internet access application request initiated by a user for the first time in a long time, the most frequently used historical internet access traffic type in one month can be used as a predicted internet access traffic type; considering that the user's habit of surfing the internet is generally relatively stable and the traffic type of surfing the internet in a short time is generally relatively concentrated, the historical surfing traffic type accessed last time can be used as the predicted surfing traffic type for the intermediate continuous surfing application request of the user in a short time.
S102: and inquiring the flow residual value corresponding to the predicted internet flow type stored in the flow classification inquiring module.
The traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type. Table 2 is a traffic classification lookup table in the traffic classification lookup module, and as shown in table 2, there are two major types of traffic subscribed by the user, the first type is a CMNET traffic type, and as shown in the first row listed behind the head of the table, preferential treatment can be performed on all types of traffic, the traffic already uses 100M, the total traffic usage value corresponding to the CMNET traffic type is 2G, and the remaining traffic value is 2G to 100M, that is, 1G is zero 924M; the second type is 30G directional traffic, and as shown in the second row, the traffic already uses 4G, and only directional traffic offers with SID of 1750000002, 1750000013, 1750000012 can be generated, so that the total traffic usage value corresponding to the directional traffic type is 30G, and the remaining traffic value is 30G-4G, that is, 26G.
TABLE 2 flow classification look-up table
S103: and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow residual value corresponding to the predicted internet flow type.
Specifically, according to step S101, if the historical internet traffic type accessed last time is used as the predicted internet traffic type, the traffic remaining value corresponding to the predicted internet traffic type stored in the traffic classification query module is searched; for example, the traffic classification lookup table records that the remaining value of traffic corresponding to the directional traffic type 1 is Vb1, the remaining value of traffic corresponding to the directional traffic type 2 is Vb2, the remaining value of traffic corresponding to the directional traffic type 3 is Vb3, and the remaining value of traffic corresponding to the non-directional traffic type is V9, if the predicted online traffic type is the directional traffic type 1, the remaining value of traffic corresponding to the directional traffic type 1 is Vb1, and considering that the traffic of the non-directional traffic type can also be used for the directional traffic, the predicted total remaining value of traffic available for the online traffic type is (Vb1+ V9), and then the dynamic traffic quota value is determined according to the total available traffic value.
By adopting the method provided by the embodiment, the next internet surfing behavior of the user is predicted according to the historical internet surfing data stored in the cache module, so that the predicted internet surfing flow type is obtained; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types; inquiring a flow residual value corresponding to the predicted internet surfing flow type stored in the flow classification inquiring module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type; and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type. The method provided by the embodiment predicts the next internet surfing behavior of the user based on the recent internet surfing habit of the user, predicts which type of flow the user will use next time, further queries the flow residual value of the type of the user, and finally calculates the dynamic quota, so that the dynamic flow quota value is dynamically calculated according to the internet surfing habit of the user and the flow residual values of different flow types, so that distribution is more accurate, the current internet surfing habit of the user is better met, signaling network and supporting network loads are reduced, and the resource utilization rate is improved.
Example two
Fig. 2 is a flowchart illustrating another embodiment of a dynamic quota method based on traffic prediction according to the present invention, and as shown in fig. 2, the method includes the following steps:
s201: analyzing a CCR message generated in the user internet surfing process, determining the internet surfing flow type requested by the user in the internet surfing time period and the time period accumulated flow value corresponding to the internet surfing flow type, and storing the internet surfing flow type and the time period accumulated flow value corresponding to the internet surfing flow type as historical internet surfing data in a cache module.
In this step, during the online charging user's online process, an online connection application and a traffic report are performed through a first online Request (CCR) message, and the CCR message is acquired and analyzed. The CCR message at least includes a charging number, an internet traffic type, a time period accumulated traffic value corresponding to the internet traffic type, a reporting time, an International Mobile Equipment Identity (IMEI), an International Mobile Subscriber Identity (IMSI), an Access Point Name (APN), and other data. If a plurality of internet traffic types exist in the same time period, the internet uses the internet traffic type with the most traffic as the internet traffic type.
S202: and generating a flow bill for charging according to the current internet flow type and the time period accumulated flow value corresponding to the current internet flow type.
In this step, after the accumulated traffic value of the time period corresponding to the current internet traffic type is charged, a traffic bill is generated.
S203: and calculating a total traffic usage value and a residual traffic value corresponding to each internet traffic type of the user according to the traffic ticket and the user traffic purchase information, and storing the total traffic usage value and the residual traffic value corresponding to each internet traffic type into a traffic classification query module.
In this step, the traffic ticket and the user traffic purchase information are combined at the same time, the user traffic purchase information may be a traffic package purchased by the user, and after the traffic ticket is charged, the traffic use total value and the traffic remaining value corresponding to each internet traffic type are updated and stored in the traffic classification query module. The traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type.
For example, the user's latest internet traffic type is a kuku video, and the total traffic usage value corresponding to the internet traffic type is 30G, and the remaining traffic value is 30G-4G-26G.
S204: and predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain the predicted internet surfing flow type.
The historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types. In an optional manner, step S204 further includes: searching historical internet surfing data stored in a first preset time period by a cache module; if the predicted internet surfing flow type is found, the historical internet surfing flow type in the historical internet surfing data which is stored in the cache module for the last time in the first preset time period is used as the predicted internet surfing flow type; if the predicted internet traffic type is not found, determining the predicted internet traffic type according to the historical internet data stored in the second preset time period by the cache module.
Specifically, determining the predicted internet traffic type according to the historical internet data stored in the cache module in the second preset time period further includes: counting historical internet surfing data stored in the cache module in a second preset time period to obtain the use times corresponding to each historical internet surfing flow type; and taking the historical internet traffic type with the most use times as a predicted internet traffic type.
Specifically, the user internet access habit is generally relatively stable, and the internet access traffic types in a short time are generally relatively concentrated, so that the next internet access behavior of the user can be predicted according to the historical internet access traffic types and the accumulated traffic values in the time periods corresponding to the historical internet access traffic types to obtain the predicted internet access traffic types; for example, the first preset time period is 3 hours, the second preset time period is one month, and if historical internet surfing data exists in the latest 3 hours, the historical internet surfing traffic type in the historical internet surfing data stored for the latest time in the 3 hours is used as the predicted internet surfing traffic type; if no historical internet surfing data exists within 3 hours, namely, the user does not surf the internet within 3 hours, searching for historical internet surfing data stored in a second preset time period, namely, one month, and counting the historical internet surfing data stored in the cache module in one month to obtain the using times corresponding to each historical internet surfing flow type; and taking the historical internet traffic type with the most use times as a predicted internet traffic type.
Table 3 is an internet traffic cache table updated by the cache module, as shown in table 3, the latest internet traffic type of the user is a kuku video, the total traffic usage value corresponding to the internet traffic type is 30G, the remaining traffic value is 30G-4G ═ 26G, the last traffic quota is 50M, so that a 70M quota is allocated to the user by uploading the quota this time, after the user quota is used up, the traffic is reported, and after the ticket billing is finished, the internet traffic cache table is updated, and the updating content includes: updating the usage amount of the Youkou video type 1750000002 monthly, and increasing by 70M; and updating the accumulated flow value in the last time period, wherein the flow uses 70M.
| Charging number | SID | Cache cycles | Used amount | Effective time | Time to failure | Time of operation |
| 13900000001 | 2000000002 | Degree of the moon | 100M | 20190501000000 | 20190531000000 | 20190505000300 |
| 13900000001 | 1750000002 | Degree of the moon | 1G 70M | 20190501000000 | 20190531000000 | 20190505002300 |
| 13900000001 | 1750000013 | Degree of the moon | 3G | 20190501000000 | 20190531000000 | 20190505000300 |
| 13900000001 | 1750000002 | Last time | 70M | 20190505002300 |
Table 3 updated internet traffic buffer table
S205: and inquiring the flow residual value corresponding to the predicted internet flow type stored in the flow classification inquiring module.
Table 4 is an updated traffic classification lookup table in the traffic classification lookup module, and compared with table 2, the user updates the traffic classification lookup table after using 70M directional traffic, and adds 70M traffic in the total value of the directional traffic usage for the next traffic quota allocation calculation. It should be noted that the classification of the internet traffic types in the method is not limited to directional and non-directional types, and may also be other types of internet traffic types.
TABLE 4 updated traffic classification look-up table
S206: and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type.
Specifically, step S206 further includes: judging whether a flow residual value corresponding to the predicted internet traffic type is larger than a first threshold value or not;
if the predicted internet traffic type is larger than the first threshold, calculating a dynamic traffic quota value corresponding to the predicted internet traffic type by using a last traffic quota value corresponding to the predicted internet traffic type and the first proportion;
if the predicted internet traffic type is smaller than or equal to the first threshold, judging whether a traffic residual value corresponding to the predicted internet traffic type is larger than a second threshold; the first threshold is greater than the second threshold;
if the predicted internet traffic type is larger than the second threshold, calculating a dynamic traffic quota value corresponding to the predicted internet traffic type by using the last traffic quota value corresponding to the predicted internet traffic type and the second proportion;
and if the preset traffic quota value is smaller than or equal to the second threshold value, taking the preset traffic quota value as a dynamic traffic quota value corresponding to the predicted internet traffic type.
In this step, the predicted internet traffic type and the traffic usage total value and the traffic remaining value corresponding to each internet traffic type stored in the traffic classification query module in step S205 are obtained according to step S204, if the traffic remaining value corresponding to the predicted internet traffic type is large, a large quota value may be allocated, and if the traffic remaining value corresponding to the predicted internet traffic type is small, only a small quota value may be allocated. For example, a preset first threshold is 1G, a preset second threshold is 500M, a first proportion is 1.1 times, and a second proportion is 0.9 times, then, if it is predicted that a traffic remaining value corresponding to the internet traffic type is greater than 1G, a next dynamic traffic quota value may be adjusted up according to the first proportion, that is, 1.1 times of the previous traffic quota value, and the adjusted up value does not exceed the traffic remaining values of the traffic of other internet traffic types; if the flow residual value corresponding to the predicted internet traffic type is smaller than or equal to 1G and larger than 500M, the dynamic flow quota value can be adjusted downwards according to a second proportion, namely 0.9 time of the flow quota value at the previous time; if the type of flow remaining value is less than or equal to 500M, the dynamic flow quota value may be allocated at a preset flow quota value, such as a fixed value of 10M or 20M.
By adopting the method provided by the embodiment, the recent internet surfing habits of the user are summarized by acquiring and analyzing the messages generated in the internet surfing process of the user, the next internet surfing behavior of the user is predicted, which type of flow can be used by the user next time is predicted, the flow residual value of the type of the user is further inquired, and finally, the dynamic quota is calculated.
EXAMPLE III
Fig. 3 is a schematic structural diagram illustrating an embodiment of a dynamic quota device based on traffic prediction according to the present invention. As shown in fig. 3, the apparatus includes: a caching module 301, a traffic classification query module 302, a prediction module 303, a query module 304, and a quota value calculation module 305.
The cache module 301 is adapted to store historical internet surfing data; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types.
The traffic classification query module 302 is adapted to store a traffic usage total value and a traffic remaining value corresponding to each internet traffic type.
The prediction module 303 is adapted to predict a next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module 301, so as to obtain a predicted internet surfing traffic type.
In particular, the prediction module 303 is further adapted to: searching historical internet surfing data stored in a first preset time period by the cache module 301; if the predicted internet surfing flow type is found, the historical internet surfing flow type in the historical internet surfing data which is stored in the cache module for the last time in the first preset time period is used as the predicted internet surfing flow type; if the predicted internet traffic type is not found, determining the predicted internet traffic type according to the historical internet data stored in the second preset time period by the cache module 301.
In an alternative approach, the prediction module 303 is further adapted to: counting historical internet surfing data stored in the cache module 301 in a second preset time period to obtain the number of times of use corresponding to each historical internet surfing traffic type; and taking the historical internet traffic type with the most use times as a predicted internet traffic type.
The query module 304 is adapted to query the traffic residual value corresponding to the predicted internet traffic type stored in the traffic classification query module 302.
The quota value calculating module 305 is adapted to calculate a dynamic traffic quota value corresponding to the predicted internet traffic type according to the traffic remaining value corresponding to the predicted internet traffic type.
In an optional manner, the quota value calculation module 305 is further adapted to: judging whether a flow residual value corresponding to the predicted internet traffic type is larger than a first threshold value or not; if the predicted internet traffic type is larger than the first threshold, calculating a dynamic traffic quota value corresponding to the predicted internet traffic type by using a last traffic quota value corresponding to the predicted internet traffic type and the first proportion; if the predicted internet traffic type is smaller than or equal to the first threshold, judging whether a traffic residual value corresponding to the predicted internet traffic type is larger than a second threshold; the first threshold is greater than the second threshold; if the predicted internet traffic type is larger than the second threshold, calculating a dynamic traffic quota value corresponding to the predicted internet traffic type by using the last traffic quota value corresponding to the predicted internet traffic type and the second proportion; and if the preset traffic quota value is smaller than or equal to the second threshold value, taking the preset traffic quota value as a dynamic traffic quota value corresponding to the predicted internet traffic type.
In an optional manner, the apparatus further comprises: the charging control module is suitable for analyzing a CCR message generated in the user internet surfing process, determining the internet surfing flow type requested by the user in the internet surfing time period and the time period accumulated flow value corresponding to the internet surfing flow type, and storing the internet surfing flow type and the time period accumulated flow value corresponding to the internet surfing flow type as historical internet surfing data in the cache module; generating a traffic bill for charging according to the current internet traffic type and the time period accumulated traffic value corresponding to the current internet traffic type; and calculating a total traffic usage value and a residual traffic value corresponding to each internet traffic type of the user according to the traffic ticket and the user traffic purchase information, and storing the total traffic usage value and the residual traffic value corresponding to each internet traffic type into a traffic classification query module.
In summary, the dynamic quota method and apparatus based on traffic prediction according to the first embodiment, the second embodiment, and the third embodiment are described in terms of a user internet interaction process.
Step 1: when the online charging user mobile phone terminal accesses the internet, the online charging user mobile phone terminal initiates an internet access application to the gateway equipment.
Step 2: the gateway device initiates a first internet application (CCR-I) message packet to a charging Control module in the online charging system, wherein the application carries data such as a charging number, a communication protocol context, internet access start time and the like.
And step 3: the charging control module receives the internet request message, analyzes the charging number, the communication protocol context and the internet start time data from the request, and initiates an internet initialization request to the prediction module 303.
And 4, step 4: after receiving the initialization request, the prediction module 303 queries, according to the charging number, historical internet surfing data corresponding to the charging number stored in the cache module 301, and predicts the next internet surfing behavior of the user to obtain a predicted internet surfing traffic type, where the predicted internet surfing traffic type may be a predicted SID value.
And 5: after the prediction module 303 obtains the predicted internet traffic type, a traffic remaining value query request corresponding to the predicted internet traffic type is sent to the query module 304.
Step 6: the query module 304 searches the traffic residual value corresponding to the predicted internet traffic type from the traffic classification query module 302 according to the billing number and the predicted internet traffic type, and sends the traffic residual value to the quota value calculation module 305.
And 7: the quota value calculation module 305 receives the traffic remaining value corresponding to the predicted internet traffic type, and calculates a dynamic traffic quota value corresponding to the predicted internet traffic type.
And 8: after receiving the CCA (INITIAL _ REQUEST) message packet fed back by the online charging system, the gateway device allows the user terminal to access the internet.
And step 9: after the user terminal is allowed to surf the internet, the data traffic service flow is continuously generated according to the internet surfing requirement of the user terminal, and the traffic data is fed back to the network element equipment in real time.
Step 10: the gateway device accumulates the user online flow in real time, and when the quota value pre-allocated by the user is used up or the online time exceeds the threshold value, a CCR (UPDATE _ REQUEST) message packet is triggered. The gateway device initiates a quota use-up-continuation-application (CCR-U) message packet to the charging Control module, where the application carries a charging number, an SID, a reporting time, and also includes an internet traffic type and a corresponding traffic usage value.
Step 11: after receiving the message packet of the gateway device, the charging control module analyzes the charging number, SID, reporting time, flow usage value and other data from the request, and transfers the data to the ticket charging unit for processing.
Step 12: and the ticket charging unit is used for carrying out wholesale price charging by combining package data ordered by the user according to the internet traffic type and the corresponding traffic use value, and feeding back a charging result to the charging control module.
Step 13: the charging control module updates the cache module 301 according to the charging result of the ticket, modifies and updates the historical internet access data in the current month, and updates the last internet access traffic type and the corresponding traffic value of the number.
Step 14: after the cache module 301 successfully updates the data, it returns a successful identifier of the charging control module.
Step 15: and the charging control module updates the flow classification query module 302 according to the charging result of the call ticket, and modifies the used flow and the flow residual value of the flow of the corresponding historical internet traffic type.
Step 16: after the data updating is successful, the traffic classification query module 302 returns a successful identifier of the charging control unit.
At this point, the interactive process of one internet surfing process is finished, and then the processes of continuous cyclic internet surfing prediction, margin query, quota calculation, internet surfing authorization, usage amount reporting and the like are carried out, and details are not repeated here.
By adopting the device provided by the embodiment, the next internet surfing behavior of the user is predicted according to the historical internet surfing data stored in the cache module, so that the predicted internet surfing flow type is obtained; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types; inquiring a flow residual value corresponding to the predicted internet surfing flow type stored in the flow classification inquiring module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type; and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type. The device predicts the next internet access behavior of the user based on the recent internet access habit of the user, predicts which type of flow the user uses next time, further inquires the flow residual value of the type of the user, and finally calculates the dynamic quota, so that the dynamic flow quota value is dynamically calculated according to the internet access habit of the user and the flow residual values of different flow types, the distribution is more accurate, the current internet access habit of the user is better met, the loads of a signaling network and a supporting network are reduced, and the resource utilization rate is improved.
Example four
An embodiment of the present invention provides a non-volatile computer storage medium, where at least one executable instruction is stored in the computer storage medium, and the computer executable instruction may execute a dynamic quota method based on traffic prediction in any method embodiment described above.
The executable instructions may be specifically configured to cause the processor to:
predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain a predicted internet surfing flow type; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types;
inquiring a flow residual value corresponding to the predicted internet surfing flow type stored in the flow classification inquiring module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type;
and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type.
EXAMPLE five
Fig. 4 is a schematic structural diagram of an embodiment of a computing device according to the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
As shown in fig. 4, the computing device may include: a processor (processor), a Communications Interface (Communications Interface), a memory (memory), and a Communications bus.
Wherein: the processor, the communication interface, and the memory communicate with each other via a communication bus. A communication interface for communicating with network elements of other devices, such as clients or other servers. The processor is configured to execute a program, and may specifically execute relevant steps in the dynamic quota method embodiment based on traffic prediction.
In particular, the program may include program code comprising computer operating instructions.
The processor may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The server comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program may specifically be adapted to cause a processor to perform the following operations:
predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain a predicted internet surfing flow type; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types;
inquiring a flow residual value corresponding to the predicted internet surfing flow type stored in the flow classification inquiring module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type;
and calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow surplus value corresponding to the predicted internet flow type.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (7)
1. A dynamic quota method based on flow prediction is characterized by comprising the following steps:
predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain a predicted internet surfing flow type; the historical internet surfing data comprises: historical internet traffic types and time period accumulated traffic values corresponding to the historical internet traffic types;
inquiring a flow residual value corresponding to the predicted network traffic type stored in a traffic classification inquiry module; the traffic classification query module stores a traffic use total value and a traffic residual value corresponding to each internet traffic type;
calculating a dynamic traffic quota value corresponding to the predicted internet traffic type according to the traffic surplus value corresponding to the predicted internet traffic type;
the predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain the predicted internet surfing flow type further comprises:
searching historical internet surfing data stored in a first preset time period by the cache module;
if the predicted internet surfing flow type is found, taking the historical internet surfing flow type in the historical internet surfing data which is stored by the cache module for the last time in a first preset time period as the predicted internet surfing flow type;
if the historical internet surfing data is not found, the historical internet surfing data stored in the cache module in a second preset time period is counted to obtain the using times corresponding to each historical internet surfing flow type; and taking the historical internet traffic type with the most use times as a predicted internet traffic type.
2. The method according to claim 1, wherein before predicting a next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain a predicted internet traffic type, the method further comprises:
analyzing a CCR message generated in the user internet surfing process, determining the internet surfing flow type requested by the user in the internet surfing time period and the time period accumulated flow value corresponding to the internet surfing flow type, and storing the internet surfing flow type and the time period accumulated flow value corresponding to the internet surfing flow type as historical internet surfing data in a cache module;
generating a traffic bill for charging according to the current internet traffic type and a time period accumulated traffic value corresponding to the current internet traffic type;
and calculating a total traffic usage value and a residual traffic value corresponding to each internet traffic type of the user according to the traffic ticket and the user traffic purchase information, and storing the total traffic usage value and the residual traffic value corresponding to each internet traffic type into a traffic classification query module.
3. The method of claim 1, wherein the calculating the dynamic traffic quota value corresponding to the predicted internet traffic type according to the traffic remaining value corresponding to the predicted internet traffic type further comprises:
judging whether the flow residual value corresponding to the predicted internet traffic type is larger than a first threshold value or not;
if the current traffic quota value is larger than the first threshold value, calculating a dynamic traffic quota value corresponding to the predicted internet traffic type by using a last traffic quota value corresponding to the predicted internet traffic type and a first proportion;
if the predicted internet traffic type is smaller than or equal to the first threshold, judging whether a traffic residual value corresponding to the predicted internet traffic type is larger than a second threshold; the first threshold is greater than the second threshold;
if the predicted internet traffic type is larger than the second threshold, calculating a dynamic traffic quota value corresponding to the predicted internet traffic type by using a last traffic quota value corresponding to the predicted internet traffic type and a second proportion;
and if the predicted internet traffic type is smaller than or equal to the second threshold, taking a preset traffic quota value as a dynamic traffic quota value corresponding to the predicted internet traffic type.
4. A dynamic quota device based on traffic prediction, comprising:
the cache module is suitable for storing historical internet surfing data; the historical internet surfing data comprises: historical internet traffic types and time quantum accumulated traffic values corresponding to the historical internet traffic types;
the traffic classification query module is suitable for storing traffic use total values and traffic residual values corresponding to all internet traffic types;
the prediction module is suitable for predicting the next internet surfing behavior of the user according to the historical internet surfing data stored in the cache module to obtain the predicted internet surfing flow type;
the query module is suitable for querying the traffic residual value corresponding to the predicted internet traffic type stored in the traffic classification query module;
the quota value calculation module is suitable for calculating a dynamic flow quota value corresponding to the predicted internet flow type according to the flow residual value corresponding to the predicted internet flow type;
the prediction module is further adapted to:
searching historical internet surfing data stored in a first preset time period by the cache module;
if the predicted internet surfing flow type is found, the historical internet surfing flow type in the historical internet surfing data stored by the cache module last time in a first preset time period is used as the predicted internet surfing flow type;
if the historical internet surfing data is not found, the historical internet surfing data stored in the cache module in a second preset time period is counted to obtain the using times corresponding to each historical internet surfing flow type; and taking the historical internet traffic type with the most use times as a predicted internet traffic type.
5. The apparatus of claim 4, further comprising:
the charging control module is suitable for analyzing a CCR message generated in the user internet surfing process, determining the internet surfing flow type requested by the user in the internet surfing time period and the time period accumulated flow value corresponding to the internet surfing flow type, and storing the internet surfing flow type and the time period accumulated flow value corresponding to the internet surfing flow type as historical internet surfing data in the cache module; generating a traffic bill for charging according to the current internet traffic type and a time period accumulated traffic value corresponding to the current internet traffic type; and calculating a total traffic usage value and a residual traffic value corresponding to each internet traffic type of the user according to the traffic ticket and the user traffic purchase information, and storing the total traffic usage value and the residual traffic value corresponding to each internet traffic type into a traffic classification query module.
6. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the traffic prediction based dynamic quota method of any of claims 1-3.
7. A computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to a dynamic quota based traffic prediction method as claimed in any one of claims 1-3.
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| CN113468442A (en) * | 2021-07-15 | 2021-10-01 | 掌阅科技股份有限公司 | Resource bit flow distribution method, computing device and computer storage medium |
| CN114554431B (en) * | 2022-02-16 | 2024-02-06 | 武汉纵拓信息技术有限公司 | Short video flow use early warning system and method based on big data |
| CN116108291B (en) * | 2023-04-12 | 2023-08-08 | 武汉中科通达高新技术股份有限公司 | Mobile internet traffic service recommendation method and system |
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