CN112001563A - Method and device for managing phone bill amount, electronic equipment and storage medium - Google Patents
Method and device for managing phone bill amount, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112001563A CN112001563A CN202010920001.3A CN202010920001A CN112001563A CN 112001563 A CN112001563 A CN 112001563A CN 202010920001 A CN202010920001 A CN 202010920001A CN 112001563 A CN112001563 A CN 112001563A
- Authority
- CN
- China
- Prior art keywords
- target
- flow
- target user
- traffic
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000006870 function Effects 0.000 claims description 32
- 230000003993 interaction Effects 0.000 claims description 27
- 238000004590 computer program Methods 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 8
- 238000011156 evaluation Methods 0.000 claims description 7
- 230000002452 interceptive effect Effects 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 4
- 230000000875 corresponding effect Effects 0.000 description 40
- 238000007726 management method Methods 0.000 description 14
- 238000004364 calculation method Methods 0.000 description 11
- 238000012417 linear regression Methods 0.000 description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- 238000012545 processing Methods 0.000 description 5
- 230000002829 reductive effect Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000014509 gene expression Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000000670 limiting effect Effects 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000000528 statistical test Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/60—Business processes related to postal services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Telephonic Communication Services (AREA)
Abstract
The invention discloses a method and a device for managing phone bill amount, electronic equipment and a storage medium, belonging to the technical field of operator data service and comprising the following steps: after historical flow data of a target user are obtained, flow prediction parameters corresponding to future target time of the target user are estimated by utilizing the historical flow data; determining a target step length of the flow data of the target user based on a preset total cost function by using the flow prediction parameters and the target time; and adjusting the ticket generation frequency of the target user according to the target step length so as to manage the ticket amount of the target user. By implementing the scheme, the traffic service condition of the target user at the target time is evaluated by utilizing the historical traffic data of the target user, the target step length is determined according to the traffic prediction parameter of the target user and the target time, the call ticket generation frequency of the target user is adjusted, the call ticket quantity of the target user is effectively managed, the pressure of a large amount of network call ticket data quantity on a network element and a charging side is avoided, and the reliability is realized.
Description
Technical Field
The present invention relates to the technical field of data services of operators, and in particular, to a method and an apparatus for managing a phone bill amount, an electronic device, and a storage medium.
Background
With the development of data service of operators, networks are developed from 3G and 4G to 5G at present, the number of charging bills and messages is exponentially increased, and the number of monthly bills in the 3G era is increased from 48 hundred million to 470 hundred million taking a certain provincial charging bill as an example.
The related technology is to realize the management of the step length based on a fixed rule and an empirical value mode so as to calculate the call ticket data of the data service of the operator. After a 5G network is mature and commercial, along with the influence of various charging factors such as network slicing, content RG, 5G network quality of each dimension, and the like, if the call ticket data volume of the 5G network is significantly increased according to the current calculation processing mode based on a fixed step length, pressure on multiple aspects such as interaction frequency, call ticket data volume, machine performance and the like is brought to a network element and a charging side.
Therefore, it is necessary to provide a new call ticket data volume management technology to reduce the call ticket data volume of the network.
Disclosure of Invention
The application provides a method and a device for managing the bill volume, electronic equipment and a storage medium, which can solve the technical problem that the bill data volume of a network is obviously increased due to a fixed step length processing mode.
The first aspect of the present invention provides a method for managing a phone bill amount, the method comprising:
after historical flow data of a target user are obtained, flow prediction parameters corresponding to future target time of the target user are evaluated by utilizing the historical flow data;
determining a target step length of the flow data of the target user based on a preset total cost function by using the flow prediction parameters and the target time;
and adjusting the ticket generation frequency of the target user according to the target step length so as to manage the ticket amount of the target user.
A second aspect of the present invention provides an apparatus for managing a ticket amount, the apparatus comprising:
the evaluation module is used for evaluating a flow prediction parameter corresponding to the future target time of the target user by using historical flow data after the historical flow data of the target user is obtained;
the determining module is used for determining a target step length of the traffic data of the target user based on a preset total cost function by utilizing the traffic prediction parameters and the target time;
and the adjusting module is used for adjusting the ticket generating frequency of the target user according to the target step length so as to manage the ticket amount of the target user.
Optionally, the method further includes:
the first acquisition module is used for acquiring the priority weight coefficient of the target user;
the optimization module is used for generating an optimized target step length according to the priority weight coefficient and the target step length;
the adjusting module is further used for adjusting the ticket generating frequency of the target user according to the optimized target step length;
and the management module is used for managing the bill quantity of the target user according to the bill generation frequency.
A third aspect of the present invention provides an electronic device comprising: the memory is coupled to the processor, the memory stores a computer program thereon, and the processor implements each step of the method for managing a phone number in the first aspect when executing the computer program.
A fourth aspect of the present invention provides a storage medium which is a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for managing a word quantity of the first aspect.
The invention provides a method for managing phone bill amount, which comprises the following steps: after historical flow data of a target user are obtained, flow prediction parameters corresponding to future target time of the target user are estimated by utilizing the historical flow data; determining a target step length of the flow data of the target user based on a preset total cost function by using the flow prediction parameters and the target time; and adjusting the ticket generation frequency of the target user according to the target step length so as to manage the ticket amount of the target user. Compared with the prior art, the method can realize the generation of different target step lengths among different target users, so as to control the generated amount of the call tickets and realize the management of the call ticket amount, by implementing the scheme, the historical flow data of the target users are utilized to evaluate the flow prediction parameters corresponding to the target users at the future target time, so as to prejudge the flow use condition of the target users at the target time, and the historical flow data and the target time are utilized to determine the target step length of the flow data at the target time, so as to realize the calculation of the individual target step length according to the historical flow data of the target users, so as to adjust the call ticket generation frequency of the target users, further effectively manage the call ticket amount of the target users, avoid the increase of the call ticket data amount of the network, bring pressure to network elements and charging sides, and have.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart illustrating steps of a method for managing call ticket amount according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating another step of a method for managing call volume according to an embodiment of the present invention;
FIG. 3 is a block diagram of a device for managing call tickets according to an embodiment of the present invention;
fig. 4 is an architecture diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical problem that the call ticket data volume of the network is obviously increased due to the fixed step length processing mode in the prior art is solved.
In order to solve the above technical problems, the present invention provides a method and an apparatus for managing a phone number, an electronic device, and a storage medium.
The invention mainly explores the characteristics of the network call list quantity from the data, predicts the step length by using the big data technology and an AI intelligent algorithm, avoids realizing the management of the step length in a mode of fixing the rule and the experience value, and brings pressure on multiple aspects of interaction frequency, call list data quantity, machine performance and the like to a network element and a charging side.
The target step length obtained based on the big data + AI algorithm is obtained by calculation according to the use condition of the historical flow data of each target user, namely the target step lengths of the future target time of different target users are different, and the step length management of different target users and different target time of the same target user between different target users in a dynamic step length mode is realized, so that the call ticket amount of related target users is managed. The method comprises the steps of analyzing user using behaviors by extracting features of big data, generating user flow prediction parameters by algorithms such as random forest regression, gradient lifting tree and LSTM, combining various correlation factors of step length and the flow prediction parameters, configuring according to a flow reminding threshold strategy, and generating a user dynamic step length based on a preset dynamic step length calculation formula model.
Referring to fig. 1, a flowchart of steps of a method for managing a phone bill quantity according to an embodiment of the present invention is shown, where the method includes the following steps:
step S101: and after the historical flow data of the target user is obtained, evaluating the flow prediction parameters corresponding to the target time of the target user in the future by using the historical flow data.
After each user registers with a certain operator, the service data corresponding to the service used by the user is calculated and processed by the corresponding operator to generate a related ticket, which may be a consumption ticket or a prompt ticket, and this embodiment does not limit this. With the development of data services of operators, the data services are developed from 3G and 4G to 5G, so that diversified data service experiences are brought to each user, meanwhile, telephone bills corresponding to the data services are correspondingly increased, and burden is brought to network elements and charging sides of an operator system. In contrast, in the embodiment, the target step length of the target user at the target time is estimated based on the historical traffic data of the target user or the related user as the basis for estimating the traffic prediction parameter corresponding to the future target time.
Specifically, the historical traffic data is traffic data generated when the target user uses the traffic service at a past time or a certain time, such as the traffic service is consumed when the user watches a video or audio program at the historical time (the past time or a past time period); further, the historical traffic data includes: the total amount of traffic (total amount of traffic) subscribed or owned by the target user, the amount of traffic consumed by the target user during the historical period or historical time, and the historical traffic data may further include: the basic information of the user, the consumption habits of the user, the traffic consumption conditions, the traffic usage habits, the traffic consumption details of each time segment in history, the holiday attributes, and the like may be specifically set according to the actual conditions, and this embodiment will not be further described herein.
According to the historical flow data of the target user, all historical time periods of the flow used by the user or all time periods of the habit of using the flow can be obtained, the habit of using the flow by the user at the future time is analyzed through the time periods, and the corresponding future target time period or the flow use habit at the target time is evaluated, so that the description and understanding are facilitated in the future, and the future target time, the target time period and other time are collectively called as the target time in the embodiment.
And estimating a flow prediction parameter corresponding to the target time of the target user in the future by using the historical flow data, namely estimating the flow consumption used by the user at the target time, the target time when the user will use the flow and the like. It should be noted that the historical traffic data is used to estimate the traffic prediction parameters corresponding to the target user at the future target time, and the traffic prediction parameters can be obtained by performing calculation and estimation through algorithm models such as random forest regression, gradient lifting tree, LSTM, and the like. Taking regression model as an example, regression model is a mathematical model for quantitatively describing statistical relationship, such as the mathematical model of multiple linear regression, which can be expressed as y ═ β0+β1·x+i, wherein ,β0，β1…, where β p is p +1 parameters to be estimated,iare independent of each other and follow the same normal distribution N (0, sigma)2) Y is a random variable, x can be a random variable or a non-random variable, and β i is called a regression coefficient and represents the degree of influence of the independent variable on the dependent variable; in this embodiment, after obtaining the historical flow data of the target user, a regression model may be constructed by using the historical flow data, where the regression model may be a unitary linear regression model or a multiple linear regression model, and specifically, the multiple linear regression model is taken as an example according to the historical flow data, and for example, the historical flow data includes: the flow consumption, the flow usage habit (each time period of the history of the flow usage), the flow consumption detail of each time period of the history, etc. can be constructed based on the history flow dataThe multiple linear regression model is built, and further details regarding the building process of the multiple linear regression model are not described in this embodiment.
After the regression model is built, starting from historical flow data of a target user as sample data, determining mathematical relational expressions among variables to carry out various statistical tests on credibility of the relational expressions, finding out which variables have obvious and inconspicuous effects from a plurality of variables influencing a certain specific variable, and predicting or controlling the value of another specific variable according to the value of one or more variables by using the obtained relational expressions to obtain more accurate flow prediction data of the target user at the future target time, so that the accuracy of evaluating the flow use condition of the target user is improved, and the reliability is achieved.
In one embodiment, step S101 includes: establishing a flow regression model corresponding to the target user based on the acquired historical flow data; and flow prediction parameters corresponding to the evaluation target time based on the flow regression model by using the historical flow data. Specifically, after obtaining the historical flow data of the target user, a regression model is constructed based on the obtained historical flow data of the target user, for example, a multiple linear regression model is constructed, the historical flow data and the future target time can be used as variables, or the historical flow data can be used as variables, specifically, according to the constructed regression model and the actual situation, the flow prediction data of the target user at the future target time is evaluated based on the constructed multiple linear regression model, for example, the flow consumption and the flow residual quantity of the target user at the target time are obtained. This step can improve the accuracy of evaluating the traffic prediction data.
Step S102: and determining the target step length of the flow data of the target user based on a preset total cost function by utilizing the flow prediction parameters and the target time.
The embodiment includes a constructed total cost function, which is a cost function using a step length as an argument and includes a plurality of costs, such as interaction costs, excessive traffic limiting risks, and comprehensive risks, and the costs are set to be in a relation related to the step length of traffic, such as the interaction costs are directly proportional to the number of interactions and inversely proportional to the time step; the excess flow limit cost and the excess flow limit risk are in linear positive correlation with the time step; the comprehensive risk is in linear positive correlation with the time step; when the total cost function containing the relation is used for obtaining the minimum value of the total cost function, the target step length can be obtained, the target step length can be understood as the optimal step length, and the ticket generation frequency can be adjusted through the target step length.
The variables or operation parameters related to the total cost function include: the flow rate v of the target time, the flow step size distribution m of the target time, the time step size distribution tau of the target time, and the flow residual h of the target time. Since the flow step allocation m of the target time is equal to v · τ, when the flow rate v of the target time is determined, the flow step allocation m of the target time may also be determined, and for convenience of representation, this embodiment focuses on acquiring the time step allocation τ of the target time. The construction of the overall cost function is as follows:
in order to obtain a flow step size distribution m of a target time corresponding to a minimum value of the total cost function and a time step size distribution τ of the target time, that is, to obtain a target step size corresponding to the minimum value of the total cost function in this embodiment, an estimated flow prediction parameter is used for calculation, and in an embodiment, the flow prediction parameter includes: and when the target step length tau is a certain time step length, calculating a target total cost value L (tau, v, h) of the target user by combining the flow residual quantity and the flow rate, wherein the target step length is an optimal step length and corresponds to the minimum total cost value L (tau, v, h).
In particular, with T0With the period as the target time, the total cost function is as follows:
wherein ,T0Maximum target time representing the step size of the constraint, a representing a single intersectionThe mutual cost, beta, represents the flow restriction coefficient;
further, a derivative of the total cost function is obtained, and the derivative of the total cost function is obtained as follows:
Further, in order to consider the flow residual h, a safety factor beta of the flow service of the target user when the flow service does not exceed the flow is set1And setting an over-flow loss coefficient or an over-flow cost coefficient beta when the flow service of the target user exceeds the flow2；
In an implementation manner of step S102, step S102 specifically includes: calculating based on a preset total cost function by using the residual flow, the flow rate and the target time to obtain a target total cost value; and determining a target step length of the flow data of the target user according to the target total cost value.
The target total cost value is calculated as follows:
wherein L (τ, v, h) represents the total cost value of the target, T0Representing the target time, a represents the preset one-time interaction cost, beta1Indicating that the safety factor of the flow, beta, has not been exceeded2A loss coefficient indicating an excess flow, h indicating a remaining amount of the flow, v indicating a flow rate, and τ indicating a target step size.
Further, if the determined time step of the target user is too small, when the target user watches consumption traffic such as video through the terminal, the interaction frequency between the terminal and the network element and the charging side of the operator system is too low, which may cause the failure of the system to be not solved in time and the user experience perception to be reduced, for example, may cause the update of the ticket inquired by the user to be not in time, the statistics to be inaccurate, and the like. In order to avoid the above problem, when calculating the total cost value, an integrated risk term τ · w is added, wherein w represents an integrated risk coefficient, and the cost of the integrated risk term is positively correlated with the step length. Therefore, the total cost function for calculating the target total cost value is as follows:
in order to simplify the total cost function, a safety cost coefficient B which does not exceed the flow is set in the interaction cost in the target time1＝β1/(T0A) risk cost coefficient of the overflow B2＝β2/(T0A), the cost of the sum term W ═ W/(T)0A), the simplified total cost function is set as:
then L (τ, v, h) is (T)0·a)·F(τ,v,h)。
Further, determining an optimal step size of the traffic data of the target user according to the target total cost value, that is, determining a target step size of the traffic data of the target user according to the target total cost value, and when the target total cost value has a minimum value, there exists an optimal solution of the step size, that is, there exists an extreme point of the target step size, and the extreme point of the target step size can be understood as a target step size, which is specifically as follows:
obtaining a target step size extreme value corresponding to the optimal solution of the target total cost value, and obtaining a corresponding target step size value when the target step size extreme value is obtained, wherein the target step size value comprises a first target step size value tau1And a second target step size τ2Wherein the first target step size value is greater than the second target step size value;
generating a corresponding step length threshold value according to the residual flow quantity and the flow rate, wherein the step length threshold value is h/v;
comparing the target step size value with a step size threshold value;
if the first target step size is less than or equal to the step size threshold, the target step size is the first target step size τ1The method comprises the following steps:
when in use
When the temperature of the water is higher than the set temperature,
if the second target step size is larger than the step size threshold, the target step size is the second target step size τ2The method comprises the following steps:
when in use
When the temperature of the water is higher than the set temperature,
if the first target step size is greater than the step size threshold and the second target step size is less than the step size threshold, the target step size is the step size threshold3The method comprises the following steps:
when in use
When the temperature of the water is higher than the set temperature,
further, in another embodiment of this embodiment, if the traffic rate v in the estimated traffic prediction parameter is zero at the target time of the target user, that is, v is 0, a safe step size, such as 150 minutes, may be allocated based on the target user at the target time according to the business experience of the service end of the operator system, which is not limited in this embodiment, and the safe step size may be specifically set according to the actual requirement. When the flow rate v in the flow prediction parameter is 0, setting the safe step length T of the target time1Then the safe step size of the target time is expressed as follows:
Further, in another implementation manner of this embodiment, the target step length is determined according to the remaining amount of the traffic in the traffic prediction parameter and a preset traffic reminding threshold, that is, when the remaining amount of the traffic is close to the traffic reminding threshold, the allocated target step length is decreased, so that when the remaining amount of the traffic is close to the traffic reminding threshold, the generated amount of the call ticket of the target user is increased, where the call ticket takes a short message as an example, and the traffic reminding threshold can be understood as a short message reminding point, and when the remaining amount of the traffic is close to the short message reminding point, the network element of the operator system and the charging side decrease the target step length allocated to the target user, so as to increase the generated amount of.
Specifically, the traffic alert threshold may be set to 20% and 30%, that is, the short message alert point is the remaining 20% and 30% of the traffic, and the maximum remaining traffic (total traffic or total traffic) is H0，H040G, the residual flow of the short message reminding points is respectively H1＝0.3·H0And H2＝0.2·H0. Then, in order to increase the interaction frequency before the short message reminding point, the safety cost coefficient B for the traffic which is not exceeded1And risk cost coefficient B of the overflow2The values of (a) are set as follows:
B1′＝B1·(1+γ1+γ2)，
B2′＝B2·(1+γ1+γ2)，
wherein ,γ1The alarm coefficient is the alarm coefficient when the remaining flow is 20 percent, or the alarm coefficient is the first alarm coefficient; gamma ray2And the alarm coefficient is the alarm coefficient when the residual flow is 30 percent, or the alarm coefficient is called as a second alarm coefficient. Setting the first alarm coefficient and the second alarm coefficient as follows:
wherein when
When the temperature of the water is higher than the set temperature,
when H is less than H2Or
When, gamma1＝0；
Wherein when
When the temperature of the water is higher than the set temperature,
when H is less than H1Or
γ2＝0。
Obtaining a target step size value corresponding to the target total cost value, wherein the target step size value comprises a first target step size tau1And a second target step size τ2Wherein the first target step size value is greater than the second target step size value;
generating a corresponding step length threshold value according to the residual flow quantity and the flow rate, wherein the step length threshold value is h/v;
comparing the target step size value with a step size threshold value;
if the first target step size is less than or equal to the step size threshold, the target step size is the first target step size τ1The method comprises the following steps:
when in use
When the temperature of the water is higher than the set temperature,
if the second target step size is larger than the step size threshold, the target step size is the second target step size τ2The method comprises the following steps:
when in use
When the temperature of the water is higher than the set temperature,
if the second target step size is larger than the step size threshold, the target step size is the second target step size τ2The method comprises the following steps:
when in use
When the temperature of the water is higher than the set temperature,
it can be understood that since B2′＞B1', then τ1＞τ2I.e. when any of the above conditions is met, different target extreme points do not occur simultaneously,
such as when satisfying
When the optimal step extreme point is tau, only the optimal step extreme point appears1The optimal target step length is
The step extreme points that meet other conditions also exist, and this embodiment will not be further described.
In step S102, the process of determining the target step size of the traffic data of the target user based on the preset total cost function by using the traffic prediction parameter and the target time is performed, and regarding the determined target step size, the process is used by the network element and the charging side of the operator system to determine the interaction frequency of the target user in the future target time. Furthermore, after the target step length is determined, the target step length can be directly used for managing the interaction frequency of the target user terminal, the network element of the operator system and the charging side, or the target step length after being determined can be used as the final target step length of each target user after being optimized or weight-calculated, for example, the target user of the super member, the final target step length of the target user can be prolonged on the basis of the originally determined target step length, so that the interaction frequency when the residual flow is low can be reduced, and the call volume can be managed.
Step S103: and adjusting the ticket generation frequency of the target user according to the target step length so as to manage the ticket amount of the target user.
After the operator 5G network is mature and commercial, with the influence of various charging factors such as network slice, content RG, 5G network quality of each dimensionality and the like, different target time of different target users can have different target step lengths by calculating the target step lengths of different target time of each target user, namely the operator system does not manage each user in a fixed step length mode; specifically, the call ticket generation frequency of the target user is adjusted according to the determined target step length, so that the interaction frequency of the terminal of each target user, the network element of the operator system and the charging side is effectively managed, and the call ticket generation amount of each user or target user is effectively managed. After the 5G network of the operator optimizes the system capability, the call ticket generation amount is effectively managed and the system pressure is reduced while the system resources are increased.
The invention provides a method for managing phone bill quantity, which comprises the following steps: after historical flow data of a target user are obtained, flow prediction parameters corresponding to future target time of the target user are estimated by utilizing the historical flow data; determining a target step length of the flow data of the target user based on a preset total cost function by using the flow prediction parameters and the target time; and adjusting the ticket generation frequency of the target user according to the target step length so as to manage the ticket amount of the target user. Compared with the prior art, the method can realize the generation of different target step lengths among different target users, so as to control the generated amount of the call tickets and realize the management of the call ticket amount, by implementing the scheme, the historical flow data of the target users are utilized to evaluate the flow prediction parameters corresponding to the target users at the future target time, so as to prejudge the flow use condition of the target users at the target time, and the historical flow data and the target time are utilized to determine the target step length of the flow data at the target time, so as to realize the calculation of the individual target step length according to the historical flow data of the target users, so as to adjust the call ticket generation frequency of the target users, further effectively manage the call ticket amount of the target users, avoid the increase of the call ticket data amount of the network, bring pressure to network elements and charging sides, and have.
Please refer to fig. 2, which is a flowchart illustrating a method for managing a phone number according to an embodiment of the present invention. Specifically, the flow of the step is as follows:
step S201: and receiving interactive information sent by a terminal corresponding to a target user, wherein the interactive information comprises historical flow data.
Specifically, after the terminal of the target user sends the interaction information to the operator system, the operator system processor or the server or the network element and the charging side receive the interaction information from the terminal of the target user, where the interaction information is used to learn the traffic usage of the target user, for example, the interaction information may be used to learn the usage of each data service of the target user registered with the runtime system by an account or a member, or may be used by the target user after subscribing to the traffic data service.
Step S202: extracting flow consumption and total flow corresponding to target historical time in historical flow data, wherein the target historical time corresponds to the target time; the historical flow data includes flow consumption and total flow.
Specifically, the traffic consumption corresponding to the target historical time in the historical traffic data is extracted, and the total traffic or the total traffic of the traffic data service subscribed by the target user is extracted, so that the traffic amount or the traffic use condition used by the user in the historical time or in the past is known. It is understood that the target historical time is a time corresponding to the target time, for example, in order to predict the flow usage of 20:30 tomorrow, the extractable historical target time is the flow usage of 20:30 tomorrow at last night, or the flow usage of 20:30 tomorrow at all night in the past, and the average calculation is performed to obtain the predicted flow usage of 20:30 tomorrow, or the weight calculation is performed to obtain the predicted flow usage of 20:30 tomorrow. Further, the target time may also be a certain time period, for example, 12:00 to 14:00 at noon in tomorrow, the target historical time is a corresponding time period in the past, and the obtaining or calculating method of the traffic usage in the time period is the same as or similar to the foregoing, which is not further described in this embodiment.
Step S203: and estimating the flow rate in the flow prediction parameter corresponding to the target time by using the flow consumption and the historical time parameter.
Step S204: and determining the target step length of the flow data of the target user based on a preset total cost function by using the flow residual quantity, the flow rate and the target time.
Specifically, the method steps described in steps S203-S204 are similar to or similar to the method steps described in step S101 and step S102, and the content description of the partial flow is consistent with the content description of step S101 to step S102, which is not further described in this embodiment.
Step S205: acquiring a priority weight coefficient of a target user;
specifically, the network operator manages the interaction frequency of each user terminal with the network elements of the operator system and the charging side differently, so as to avoid that when the flow residual amount is too low, the interaction frequency is too high, so that too many call tickets are generated to disturb the user, and the use experience of the user is reduced. After the target step length of the traffic data of the target user is obtained in step S102 or step S204, for example, the target step length of the target user may be managed by a member level, the interaction frequency of the target user is adjusted, specifically, the ranking of the target user among all users is obtained, and the ranking priority weight coefficient of the target user is calculated according to a preset ranking rate, which is specifically as follows:
R＝(N-K)/(N-1)
wherein, R represents a ranking priority weight coefficient, N represents the number of all users, and K represents the ranking of the target user.
Further, the priority coefficient of the target user may also be determined by using the ranking priority coefficient in combination with the credit priority coefficient, which is not further described in this embodiment.
Step S206: and generating an optimized target step length according to the priority weight coefficient and the target step length.
Specifically, the optimized target step length is calculated as follows:
wherein ,τopRepresents the optimized target step size, τ represents the target step size, which may include a first target step size τ1And a second target step τ2，a1Represents the minimum step-size boost rate, a, of the target user2Representing the maximum step-up rate of the target user.
Step S207: adjusting the ticket generation frequency of the target user according to the optimized target step length;
specifically, the interactive frequency between the terminal of the target user, the network element of the operator system and the charging side is adjusted according to the optimized target step length, so that the call ticket generation frequency of the target user is managed, and the call ticket generation amount of the target user is effectively managed.
Step S208: and managing the bill quantity of the target user according to the bill generation frequency.
Specifically, the call ticket generation frequency of the target user is adjusted according to the determined target step length, call ticket generation amount of each user or the target user is effectively managed, so that after the 5G network of an operator optimizes system capacity, system resources are increased, the phenomenon that the network call ticket amount is obviously increased due to the fixed step length is avoided, call ticket generation amount is effectively managed, and system pressure is reduced.
Referring to fig. 3, fig. 3 is a block diagram of a management apparatus of a phone book according to an embodiment of the present invention, the management apparatus of the phone book corresponds to an execution main processor of a management method of the phone book, and the apparatus 300 includes:
the evaluation module 301 is configured to, after obtaining historical traffic data of a target user, evaluate a traffic prediction parameter corresponding to a target time of the target user in the future by using the historical traffic data;
a determining module 302, configured to determine a target step size of traffic data of a target user based on a preset total cost function by using a traffic prediction parameter and a target time;
and the adjusting module 303 is configured to adjust the ticket generation frequency of the target user according to the target step length, so as to manage the ticket amount of the target user.
Further, the apparatus 300 further comprises:
the receiving module 304 is configured to receive interaction information sent by a terminal corresponding to a target user, where the interaction information includes historical traffic data.
Specifically, after the terminal of the target user sends the interaction information to the operator system, the receiving module 304 of the processor at the operator system server or the network element and the charging side receives the interaction information from the terminal of the target user, where the interaction information is used to learn the traffic usage of the target user, for example, the interaction information may be used to learn the usage of each data service of the target user registered with an account or a member of the runtime system, or may be used after the target user subscribes to the traffic data service.
An extracting module 305, configured to extract a flow consumption amount and a total flow corresponding to a target historical time in historical flow data, where the target historical time corresponds to the target time; the historical flow data includes flow consumption and total flow.
Specifically, the traffic consumption amount corresponding to the target historical time in the historical traffic data is extracted by the extraction module 305, and the total traffic or the total traffic service amount in the traffic data service subscribed by the target user is extracted by the extraction module 305, so as to learn the traffic amount or the traffic usage used by the user at the historical time or in the past. It is understood that the target historical time is a time corresponding to the target time, for example, in order to predict the flow usage of 20:30 tomorrow, the extractable historical target time is the flow usage of 20:30 tomorrow at last night, or the flow usage of 20:30 tomorrow at all night in the past, and the average calculation is performed to obtain the predicted flow usage of 20:30 tomorrow, or the weight calculation is performed to obtain the predicted flow usage of 20:30 tomorrow. Further, the target time may also be a certain time period, for example, 12:00 to 14:00 at noon in tomorrow, the target historical time is a corresponding time period in the past, and the obtaining or calculating method of the traffic usage in the time period is the same as or similar to the foregoing, which is not further described in this embodiment.
Further, the apparatus 300 further comprises:
an optimizing module 306, configured to generate an optimized target step length according to the priority weight coefficient and the target step length;
the adjusting module 303 is further configured to adjust a ticket generation frequency of the target user according to the optimized target step length;
and the management module 307 is configured to manage the ticket amount of the target user according to the ticket generation frequency.
Specifically, the optimizing module 306 and the managing module 307 in the device for managing a phone bill quantity provided in this embodiment of the present invention are device modules corresponding to step S206 and step S208 in the steps of the method for managing a phone bill quantity provided in the foregoing embodiment of the present invention, and the description of the content of the relevant module is consistent with the description of the content corresponding to the embodiment of the method for managing a phone bill quantity provided in the present invention, which is not further described in this embodiment.
The invention provides a device for managing phone bill amount, comprising: an evaluation module 301, a determination module 302, an adjustment module 303, a reception module 304, an extraction module 305, an optimization module 306, and a management module 307; specifically, the method comprises the following steps: after obtaining the historical traffic data of the target user, the evaluation module 301 evaluates the traffic prediction parameter corresponding to the target time of the target user in the future by using the historical traffic data; determining a target step length of the traffic data of the target user based on a preset total cost function by using the traffic prediction parameter and the target time through the determining module 302; the call ticket generation frequency of the target user is adjusted according to the target step length through the adjusting module 303 so as to manage the call ticket amount of the target user. Compared with the prior art, the invention can realize the generation of different target step lengths among different target users through the modules so as to control the generation amount of the call tickets and realize the management of the call ticket amount, through implementing the scheme, the historical flow data of the target users are utilized to evaluate the flow prediction parameters corresponding to the target time of the target users in the future so as to prejudge the flow use condition of the target users at the target time, the historical flow data and the target time are utilized to determine the target step length of the flow data at the target time, the personal target step length is calculated according to the historical flow data of the target users so as to adjust the call ticket generation frequency of the target users, the call ticket amount of the target users is further effectively managed, the pressure on network elements and the charging side caused by the increase of the call ticket data amount of the network is avoided, and the reliability is realized.
The present invention provides an electronic device, please refer to fig. 4, which is an architecture diagram of the electronic device according to an embodiment of the present invention, and the electronic device includes: the device comprises a memory 401, a processor 402 and a communication bus 403, wherein the communication bus 403 is respectively connected with the memory 401 and the processor 402 in a communication way, the memory 401 is coupled with the processor 402, a computer program is stored on the memory 401, and when the processor 402 executes the computer program, each step in the method for managing the phone number is realized.
An exemplary computer program of the method for managing a word amount mainly includes: after historical flow data of a target user are obtained, flow prediction parameters corresponding to future target time of the target user are estimated by utilizing the historical flow data; determining a target step length of the flow data of the target user based on a preset total cost function by using the flow prediction parameters and the target time; and adjusting the ticket generation frequency of the target user according to the target step length so as to manage the ticket amount of the target user. In addition, the computer program may also be divided into one or more modules, which are stored in the memory and executed by the processor to accomplish the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, the instruction segments being used to describe the execution of a computer program in a computing device. For example, the computer program may be divided into an evaluation module 301, a determination module 302, an adjustment module 303, a reception module 304, an extraction module 305, an optimization module 306, and a management module 307 as shown in fig. 3.
The Processor 402 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The present invention further provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps in the method for managing a phone book are implemented.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the above description, for the method, the apparatus, the electronic device and the storage medium for managing a phone number provided by the present invention, for those skilled in the art, there are variations in the specific implementation and the application scope according to the idea of the embodiment of the present invention, and in summary, the content of the present specification should not be construed as limiting the present invention.
Claims (10)
1. A method for managing a phone bill amount, the method comprising:
after historical flow data of a target user are obtained, flow prediction parameters corresponding to future target time of the target user are evaluated by utilizing the historical flow data;
determining a target step length of the flow data of the target user based on a preset total cost function by using the flow prediction parameters and the target time;
and adjusting the ticket generation frequency of the target user according to the target step length so as to manage the ticket amount of the target user.
2. The method for managing incoming call traffic according to claim 1, wherein the historical traffic data includes traffic consumption and total traffic, and the step of using the historical traffic data to estimate the traffic prediction parameter corresponding to the target time of the target user in the future comprises:
receiving interactive information sent by a terminal corresponding to the target user, wherein the interactive information comprises historical flow data;
extracting flow consumption and total flow corresponding to target historical time in the historical flow data, wherein the target historical time corresponds to the target time;
then, the evaluating the traffic prediction parameter corresponding to the target time of the target user by using the historical traffic data includes:
evaluating the flow residual quantity in the flow prediction parameter corresponding to the target time by using the total flow and the flow consumption;
and evaluating the flow rate in the flow prediction parameter corresponding to the target time by using the flow consumption and the historical time parameter.
3. The method for managing the phone bill quantity according to claim 1, wherein the step of evaluating the traffic prediction parameter corresponding to the target time of the target user using the historical traffic data comprises:
establishing a flow regression model corresponding to the target user based on the acquired historical flow data;
and evaluating a flow prediction parameter corresponding to the target time based on the flow regression model by using the historical flow data.
4. The method for managing traffic volume according to claim 1, wherein the traffic prediction parameters include the remaining traffic volume and a traffic rate, and the step of determining the target step size of the traffic data of the target user based on a preset total cost function by using the traffic prediction parameters and the target time comprises:
calculating based on a preset total cost function by using the residual flow, the flow rate and the target time to obtain a target total cost value;
the target total cost value is calculated as follows:
wherein L (τ, v, h) represents the target total cost value, T0Representing the target time, a representing a preset single interaction cost, beta1Indicating that the safety factor of the flow, beta, has not been exceeded2Loss factor, h table, representing excess flowIndicating the residual flow, v indicating the flow rate, and tau indicating the target step length;
and determining a target step length of the flow data of the target user according to the target total cost value.
5. The method for managing traffic volume according to claim 4, wherein the step of determining the target step size of the traffic data of the target user according to the target total cost value comprises:
acquiring a target step value corresponding to the target total cost value, wherein the target step value comprises a first target step value and a second target step value, and the first target step value is larger than the second target step value;
generating a corresponding step length threshold according to the flow residual quantity and the flow rate;
comparing the target step size value to the step size threshold;
if the first target step size value is less than or equal to the step size threshold, the target step size is the first target step size value;
if the second target step size is greater than the step size threshold, the target step size is the second target step size;
if the first target step size is larger than the step size threshold and the second target step size is smaller than the step size threshold, the target step size is the step size threshold.
6. The method for managing traffic volume according to any one of claims 1-5, wherein the step of determining the target step size of the traffic data of the target user based on a preset total cost function using the traffic prediction parameter and the target time further comprises:
acquiring a priority weight coefficient of the target user;
generating an optimized target step length according to the priority weight coefficient and the target step length;
the managing the generation frequency of the call ticket quantity of the target user according to the target step length to manage the call ticket quantity of the target user includes:
adjusting the ticket generation frequency of the target user according to the optimized target step length;
and managing the bill quantity of the target user according to the bill generation frequency.
7. The apparatus for managing a word amount according to claim 1, comprising:
the evaluation module is used for evaluating a flow prediction parameter corresponding to the future target time of the target user by using historical flow data after the historical flow data of the target user is obtained;
the determining module is used for determining a target step length of the traffic data of the target user based on a preset total cost function by utilizing the traffic prediction parameters and the target time;
and the adjusting module is used for adjusting the ticket generating frequency of the target user according to the target step length so as to manage the ticket amount of the target user.
8. The apparatus for managing a word amount according to claim 7, further comprising:
the first acquisition module is used for acquiring the priority weight coefficient of the target user;
the optimization module is used for generating an optimized target step length according to the priority weight coefficient and the target step length;
the adjusting module is further used for adjusting the ticket generating frequency of the target user according to the optimized target step length;
and the management module is used for managing the bill quantity of the target user according to the bill generation frequency.
9. An electronic device, comprising: a memory, a processor and a communication bus, wherein the communication bus is respectively connected with the memory and the processor in a communication manner, the memory is coupled with the processor, the memory is stored with a computer program, and the processor executes the computer program to realize each step of the method for managing the phone bill quantity according to any one of claims 1 to 6.
10. A storage medium which is a computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method for managing a phone bill according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010920001.3A CN112001563B (en) | 2020-09-04 | 2020-09-04 | Method and device for managing ticket quantity, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010920001.3A CN112001563B (en) | 2020-09-04 | 2020-09-04 | Method and device for managing ticket quantity, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112001563A true CN112001563A (en) | 2020-11-27 |
| CN112001563B CN112001563B (en) | 2023-10-31 |
Family
ID=73468313
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010920001.3A Active CN112001563B (en) | 2020-09-04 | 2020-09-04 | Method and device for managing ticket quantity, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112001563B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112672296A (en) * | 2020-12-25 | 2021-04-16 | 中国联合网络通信集团有限公司 | Call bill collection method and device |
| CN114449466A (en) * | 2021-12-16 | 2022-05-06 | 深圳联想懂的通信有限公司 | User charging intelligent monitoring method and device, electronic equipment and storage medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060190310A1 (en) * | 2005-02-24 | 2006-08-24 | Yasu Technologies Pvt. Ltd. | System and method for designing effective business policies via business rules analysis |
| CN102056182A (en) * | 2010-12-13 | 2011-05-11 | 哈尔滨工业大学 | Method for predicting mobile traffic based on LS-SVM |
| CN102083077A (en) * | 2010-12-24 | 2011-06-01 | 大唐移动通信设备有限公司 | Baseband pool shared resource distribution method and device |
| CN103796247A (en) * | 2012-11-02 | 2014-05-14 | 中国移动通信集团浙江有限公司 | User traffic speed control method and system |
| CN103987056A (en) * | 2014-05-30 | 2014-08-13 | 南京华苏科技有限公司 | Wireless network telephone traffic prediction method based on big-data statistical model |
| CN104041091A (en) * | 2012-01-06 | 2014-09-10 | 华为技术有限公司 | Systems And Methods For Predictive Downloading In Congested Networks |
| CN104581779A (en) * | 2014-12-11 | 2015-04-29 | 华为技术有限公司 | Business processing method and device |
| WO2018019769A1 (en) * | 2016-07-26 | 2018-02-01 | Electricite De France | Method for predicting consumption demand using an advanced prediction model |
| CN107786351A (en) * | 2016-08-24 | 2018-03-09 | 中国电信股份有限公司 | Service bandwidth self-adapting regulation method, system and SDN controllers |
| CN108574933A (en) * | 2017-03-07 | 2018-09-25 | 华为技术有限公司 | User trajectory restoration methods and device |
| CN110443657A (en) * | 2019-08-19 | 2019-11-12 | 泰康保险集团股份有限公司 | Customer traffic data processing method, device, electronic equipment and readable medium |
| CN110618922A (en) * | 2019-08-15 | 2019-12-27 | 平安普惠企业管理有限公司 | Performance test method and related equipment |
| CN111325310A (en) * | 2018-12-13 | 2020-06-23 | 中国移动通信集团有限公司 | Data prediction method, device and storage medium |
-
2020
- 2020-09-04 CN CN202010920001.3A patent/CN112001563B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060190310A1 (en) * | 2005-02-24 | 2006-08-24 | Yasu Technologies Pvt. Ltd. | System and method for designing effective business policies via business rules analysis |
| CN102056182A (en) * | 2010-12-13 | 2011-05-11 | 哈尔滨工业大学 | Method for predicting mobile traffic based on LS-SVM |
| CN102083077A (en) * | 2010-12-24 | 2011-06-01 | 大唐移动通信设备有限公司 | Baseband pool shared resource distribution method and device |
| CN104041091A (en) * | 2012-01-06 | 2014-09-10 | 华为技术有限公司 | Systems And Methods For Predictive Downloading In Congested Networks |
| CN103796247A (en) * | 2012-11-02 | 2014-05-14 | 中国移动通信集团浙江有限公司 | User traffic speed control method and system |
| CN103987056A (en) * | 2014-05-30 | 2014-08-13 | 南京华苏科技有限公司 | Wireless network telephone traffic prediction method based on big-data statistical model |
| CN104581779A (en) * | 2014-12-11 | 2015-04-29 | 华为技术有限公司 | Business processing method and device |
| WO2018019769A1 (en) * | 2016-07-26 | 2018-02-01 | Electricite De France | Method for predicting consumption demand using an advanced prediction model |
| CN107786351A (en) * | 2016-08-24 | 2018-03-09 | 中国电信股份有限公司 | Service bandwidth self-adapting regulation method, system and SDN controllers |
| CN108574933A (en) * | 2017-03-07 | 2018-09-25 | 华为技术有限公司 | User trajectory restoration methods and device |
| CN111325310A (en) * | 2018-12-13 | 2020-06-23 | 中国移动通信集团有限公司 | Data prediction method, device and storage medium |
| CN110618922A (en) * | 2019-08-15 | 2019-12-27 | 平安普惠企业管理有限公司 | Performance test method and related equipment |
| CN110443657A (en) * | 2019-08-19 | 2019-11-12 | 泰康保险集团股份有限公司 | Customer traffic data processing method, device, electronic equipment and readable medium |
Non-Patent Citations (2)
| Title |
|---|
| 谭晖;李婧琦;杨林;廖振松;: "一种面向互联网运营的流量管理模式", 信息通信, no. 01 * |
| 马敏;李威;陈洁;: "基于机器学习的细分业务用户感知评估方法", 电信工程技术与标准化, no. 05 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112672296A (en) * | 2020-12-25 | 2021-04-16 | 中国联合网络通信集团有限公司 | Call bill collection method and device |
| CN112672296B (en) * | 2020-12-25 | 2022-05-03 | 中国联合网络通信集团有限公司 | Call bill collection method and device |
| CN114449466A (en) * | 2021-12-16 | 2022-05-06 | 深圳联想懂的通信有限公司 | User charging intelligent monitoring method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112001563B (en) | 2023-10-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2010314292B2 (en) | Method and system for adapting a session timeout period | |
| WO2019062405A1 (en) | Application program processing method and apparatus, storage medium, and electronic device | |
| CN112001563A (en) | Method and device for managing phone bill amount, electronic equipment and storage medium | |
| CN110659922B (en) | Client screening method, device, server and computer readable storage medium | |
| CN107301466A (en) | To business load and resource distribution and the Forecasting Methodology and forecasting system of property relationship | |
| CN110896357A (en) | Flow prediction method, device and computer readable storage medium | |
| CN112669091A (en) | Data processing method, device and storage medium | |
| CN114826924A (en) | Method and device for bandwidth allocation | |
| CN115202889A (en) | Computing resource adjusting method and computing system | |
| WO2019062404A1 (en) | Application program processing method and apparatus, storage medium, and electronic device | |
| CN107844496B (en) | Statistical information output method and device | |
| JP2023164741A (en) | Information processing device, method, and program | |
| CN116777518A (en) | Transaction management method, device, storage medium and equipment | |
| CN110769454B (en) | Flow prediction method and device | |
| CN110570136A (en) | distribution range determining method, distribution range determining device, electronic equipment and storage medium | |
| CN115185606A (en) | Method, device, equipment and storage medium for obtaining service configuration parameters | |
| CN116074774A (en) | Network element step length value adjusting method and device, equipment and storage medium | |
| CN113904940A (en) | Resource adjusting method and device, electronic equipment and computer readable storage medium | |
| CN115130026A (en) | Target object determination method, device, medium and electronic equipment | |
| CN111476639A (en) | Commodity recommendation strategy determining method and device, computer equipment and storage medium | |
| WO2019085742A1 (en) | Background application cleaning method and apparatus, and storage medium and electronic device | |
| CN115082117A (en) | Data processing method, data processing device, computer equipment and computer readable storage medium | |
| JP2020071558A (en) | Resource management support apparatus and resource management support method | |
| CN117909601B (en) | Payment social matching method, device, equipment and readable storage medium | |
| CN113595921B (en) | Data stream processing method and device, electronic equipment and computer readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |