Traffic computation method, device, storage medium, and program product

Abstract

The present disclosure relates to the technical field of communication, and in particular to a traffic calculation method, device, storage medium and program product. The method comprises the following steps: acquiring a user individuality coefficient of a user in a set historical time range and a traffic quota contained in a communication package purchased by the user; and calculating a gift traffic quota of the user based on a user value coefficient, the traffic quota and a set adjustment coefficient. By using the scheme of the present application, the user can be dynamically and differentially gifted with the traffic quota.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a traffic calculation method, device, storage medium, and program product. Background Technology

[0002] With the gradual development of the mobile internet, users' mobile data consumption is steadily increasing. As various mobile internet applications flood users' lives, and daily data consumption is influenced by numerous factors—such as trending events, major sporting events, roaming in different locations, and changes in commonly used apps—mobile data usage can surge. This fluctuating consumption leads users to exceed their monthly data allowance prematurely. According to operator statistics, after the 25th of each month, most users' data consumption drops significantly. This is mainly because the free data allowance in users' plans is exhausted around the 25th of each month, causing users to be very careful with their data usage at the end of the month.

[0003] Currently, mobile operators offer only two types of data packages: mandatory monthly plans that include a certain amount of free data, and optional data packages (priced at 5 / 10 / 20 yuan, etc.). These optional packages can be stacked to supplement free data at any time during the month. However, in practice, most users only use the free data included in their monthly plans, and very few purchase additional data packages. When their data allowance is insufficient, users are more inclined to change their internet usage habits or seek Wi-Fi to reduce mobile data consumption, rather than increasing their available data allowance by purchasing data packages.

[0004] In conclusion, current data plans offered by telecom operators are relatively simple, with fixed data capacities and a severe lack of flexibility. Therefore, the current product mechanisms of operators fail to stimulate users' desire to consume data, which to some extent damages users' data consumption habits, thereby reducing their online experience, decreasing user loyalty, and hindering the cultivation of good data consumption habits. Summary of the Invention

[0005] This disclosure is made in view of the above-mentioned problems. This disclosure provides a flow calculation method, apparatus, storage medium, and program product.

[0006] According to a first aspect of this disclosure, a method for calculating traffic flow is provided, comprising: The system obtains the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user. The user personality coefficient is used to reflect the user's consumption contribution and historical loyalty. The user's gifted data allowance is calculated based on the user's personality coefficient, the data allowance, and the set adjustment coefficient.

[0007] Furthermore, according to the traffic calculation method of the first aspect of this disclosure, the user personality coefficient includes at least one of the following: User value coefficient; Behavioral activity coefficient; Scene consistency coefficient; Package sensitivity coefficient; The user value coefficient is related to the user's average monthly consumption level, the behavior activity coefficient is related to the user's average weekly data consumption, the scenario consistency coefficient is related to the user's similar behavior cycle repetition rate, and the package sensitivity coefficient is related to the user's package change frequency.

[0008] Furthermore, according to the traffic calculation method of the first aspect of this disclosure, the weight of the user value coefficient is greater than the weight of the behavior activity coefficient, the weight of the behavior activity coefficient is greater than the weight of the scenario consistency coefficient, and the weight of the scenario consistency coefficient is greater than the weight of the package sensitivity coefficient.

[0009] Furthermore, according to the traffic calculation method of the first aspect of this disclosure, the user's gifted traffic quota is calculated based on the user value coefficient, the traffic quota, and a set adjustment coefficient, including: Sum the adjustment coefficient with 1; The product of the summation result, the data allowance, and the adjustment coefficient is calculated to obtain the gifted data allowance.

[0010] Furthermore, the flow calculation method according to the first aspect of this disclosure also includes: Obtain the user's reading and browsing data over the past N months; Based on the reading and browsing data, predict the user's expected data consumption for the current month; Calculate the remaining data allowance based on the data allowance and the expected data consumption. If the remaining data allowance is less than or equal to the remaining data allowance threshold, the step of obtaining the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user is executed.

[0011] Furthermore, the flow calculation method according to the first aspect of this disclosure also includes: If the remaining data allowance is less than or equal to the remaining data allowance threshold, a data usage warning will be sent to the user.

[0012] According to a second aspect of this disclosure, a flow calculation apparatus is provided, comprising: The acquisition module is used to acquire the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user. The user personality coefficient is used to reflect the user's consumption contribution and historical loyalty. The calculation module is used to calculate the user's gifted data allowance based on the user's personality coefficient, the data allowance, and the set adjustment coefficient.

[0013] According to a third aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the steps of the method described in the first aspect. According to a fourth aspect of this disclosure, a computer-readable storage medium is provided that stores a computer program / instructions thereon, which, when executed by a processor, implement the steps of the method described in the first aspect. According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0014] As will be described in detail below, the data traffic calculation method according to embodiments of this disclosure calculates the user's bonus data traffic quota by obtaining the user's individual coefficient within a set historical time range and the data traffic quota included in the user's purchased communication package, based on the user value coefficient, the data traffic quota, and a set adjustment coefficient. Using the solution of this application, dynamic and differentiated bonus data traffic quotas can be provided to users.

[0015] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0016] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 This is a flowchart illustrating a traffic calculation method according to an embodiment of the present disclosure.

[0018] Figure 2 This is a structural diagram illustrating a flow calculation device according to an embodiment of the present disclosure.

[0019] Figure 3 This is a hardware block diagram illustrating an electronic device according to an embodiment of the present disclosure.

[0020] Figure 4 This is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure. Detailed Implementation

[0021] The technical methods of the embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings.

[0022] To facilitate understanding of this embodiment, a traffic calculation method disclosed in this disclosure will first be described in detail. The execution subject of the traffic calculation method provided in this disclosure is generally an electronic device with certain computing capabilities, such as a terminal device, a server, or other processing device. In some possible implementations, the traffic calculation method can be implemented by a processor calling computer-readable instructions stored in memory.

[0023] See Figure 1 The diagram shown is a flowchart of a traffic calculation method provided in an embodiment of this disclosure. The method includes the following steps: Step 101: Obtain the user's personality coefficient and the data allowance included in the user's purchased communication package within the set historical time range. The user personality coefficient is used to reflect the user's consumption contribution and historical loyalty.

[0024] In one or more embodiments, the user personality coefficient includes at least one of the following: User value coefficient; Behavioral activity coefficient; Scene consistency coefficient; Package sensitivity coefficient; The user value coefficient is related to the user's average monthly consumption level; the behavior activity coefficient is related to the user's average weekly data consumption; the scenario consistency coefficient is related to the repetition rate of similar user behaviors; and the package sensitivity coefficient is related to the frequency of user package changes.

[0025] Wherein, the user value coefficient = ARPU (Average Revenue Per User). Remaining time factor for the package.

[0026] ARPU = Total revenue within the period ÷ Number of users within the period.

[0027] Period: usually monthly, quarterly, or yearly (month is the default if not specified).

[0028] User count: Commonly used is the number of active users (total users / paying users can also be used, the method should be consistent).

[0029] Behavioral activity coefficient = weekly average traffic consumption standard deviation Frequency of use.

[0030] The standard deviation of weekly average traffic consumption, also known as the standard deviation of weekly average traffic consumption, is used to measure whether weekly traffic consumption fluctuates greatly or is stable.

[0031] Scene consistency coefficient = repetition rate of similar behaviors.

[0032] Package sensitivity coefficient = Package change frequency Activity participation rate.

[0033] In one or more alternative embodiments, the weight of the user value coefficient is greater than the weight of the behavior activity coefficient, the weight of the behavior activity coefficient is greater than the weight of the scenario consistency coefficient, and the weight of the scenario consistency coefficient is greater than the weight of the package sensitivity coefficient.

[0034] For example, the user value coefficient can be set to 40%, the behavior activity coefficient to 30%, the scenario consistency coefficient to 20%, and the package sensitivity coefficient to 10%.

[0035] Step 102: Calculate the user's gifted data allowance based on the user's personality coefficient, data allowance, and set adjustment coefficient.

[0036] In one or more alternative embodiments, the user's gifted data allowance is calculated based on the user value coefficient, data allowance, and a set adjustment coefficient, including: Sum the adjustment factor with 1; The bonus data allowance is obtained by multiplying the sum of the results, the data allowance, and the adjustment coefficient.

[0037] The formula for calculating the free data allowance can be expressed as: Free data allowance = Data allowance (1+a) User value coefficient Where 'a' is an adjustment coefficient, ranging from 0.1 to 0.5, which can be flexibly adjusted according to actual operating conditions and data analysis results.

[0038] Regarding the data allowance within the plan, in one example, the data allowance is the plan tier a user is on, such as the 19 yuan plan which includes 5GB of data. Of course, this data allowance can vary depending on the plan.

[0039] In one or more alternative embodiments, the method may further include the following steps: Obtain the user's reading and browsing data over the past N months; Predict the expected data usage of users in the current month based on reading and browsing data; Calculate the remaining data allowance based on the data allowance and expected data consumption. If the remaining data allowance is less than or equal to the remaining data allowance threshold, the steps are to obtain the user's personality coefficient and the data allowance included in the user's purchased communication package within the set historical time range.

[0040] Reading and browsing data refers to various consumption statistics generated by users during the process of reading and browsing content, which are commonly divided into three categories: 1. Data usage (most common) Mobile data / network data consumed when reading articles, viewing images and text, refreshing pages, and loading content.

[0041] Units: MB, GB.

[0042] 2. Duration Consumption Data The time spent reading and browsing.

[0043] Units: seconds, minutes, hours.

[0044] Common metrics: average daily reading time, duration of a single browsing session.

[0045] 3. Behavior consumption / click data Page views, pageviews, dwell time, and bounce rate.

[0046] It is mostly used for APP / website operation analysis.

[0047] In one or more alternative embodiments, the reading and browsing consumption data may include the user's historical data usage, real-time data allowance information, terminal type data, and network access type data.

[0048] User historical traffic usage data: Records user traffic consumption in detail at different time granularities such as daily, weekly, and monthly, in order to capture long-term trends and short-term fluctuations in user traffic usage.

[0049] Real-time data allowance information: Real-time monitoring of users' remaining data allowance provides key information for dynamic threshold calculation and data gifting decisions, ensuring the rationality and timeliness of data gifting.

[0050] Terminal type data: Collect information on the type of terminal device used by users, such as high-end flagship mobile phones, mid-to-low-end smartphones, tablets, IoT devices, etc. The performance and functional differences of different terminal devices will affect users' data usage behavior.

[0051] Network access type data: Accurately identifies the user's current network access type, including 4G, 5G, WiFi, etc. Different network access types have different bandwidth, stability, and speed, which significantly impacts the user's data usage experience and data consumption patterns. For example, the high speed of 5G networks means that users consume data more quickly when watching high-definition videos or downloading large files.

[0052] After obtaining reading and browsing data, a sliding window algorithm is used to analyze user data consumption trends in real time, capturing short-term fluctuations and long-term trends in data usage. Simultaneously, time series forecasting models, such as the exponential smoothing model, are introduced and combined with historical user data to accurately predict future data consumption.

[0053] After obtaining reading and browsing consumption data, a time series dataset can be constructed based on the data. Then, the historical data can be processed using the exponential smoothing method to predict the expected value of traffic consumption in the current month, taking into full account the trend and seasonal characteristics of the data.

[0054] When constructing a time series dataset, a standard univariate time series can be represented as: Time (week / day) → Reading / Browsing Consumption (data usage / duration / number of times). Based on this univariate time series, the reading / browsing consumption data is organized into a basic format. Specifically, the reading / browsing consumption data is organized into two columns, as shown below: Time-based reading and browsing consumption value Week 1: 120.5 MB Week 2: 135.2 MB Week 3, 118.0 MB ... ... It should be understood that when compiling reading and browsing consumption data according to the above basic format, the following conditions must be met: continuous time, consistent units, and no outliers. Continuous time means that missing weeks should be filled with 0 or interpolation; consistent units mean that all reading and browsing consumption values ​​should be converted to MB or GB; and no outliers means that obvious anomalies (such as sudden increases or decreases) should be corrected or removed.

[0055] After organizing the reading and browsing data into a basic format, a standard time series dataset is constructed. This may include the following steps: 1. Define the sequence length For example, using weeks as the unit:

[0056] This represents the reading and browsing consumption in week t.

[0057] 2. Construct a supervised learning dataset (for prediction) Use the previous N weeks to predict the next week, for example, use the previous 4 weeks to predict the 5th week: Features (consumption in the previous N weeks) Tags (consumption in the next week) x1, x2, x3, x4, x5 x2, x3, x4, x5, x6 x3, x4, x5, x6x7 This is the time series dataset that the model can train directly on.

[0058] It should be understood that commonly used derived features can also be added to time series datasets. Specifically, based on the original data, the following can be added: Time characteristics, such as week number, whether it is the beginning / end of the month, and whether it is a holiday; Statistical characteristics, such as weekly average, rolling average (last 3 weeks, last 4 weeks), rolling standard deviation, and month-on-month growth rate; Business characteristics, such as reading time, number of views, number of active users, and ARPU.

[0059] It should be understood that exponential smoothing is a commonly used algorithm for predicting reading and browsing consumption / traffic. Exponential smoothing calculates the current period's predicted value by weighting the past actual value and the previous period's predicted value, with newer data having a larger weight.

[0060] In this embodiment, the remaining data allowance is the difference between the data allowance and the expected data consumption. A remaining data allowance less than or equal to the remaining data allowance threshold indicates that the user's data consumption for the month is very likely to exceed the data allowance, requiring the purchase of additional data.

[0061] In one or more embodiments, the method may further include the following steps: If the remaining data allowance is less than or equal to the remaining data allowance threshold, a data usage warning will be sent to the user.

[0062] In practical applications, this includes, but is not limited to, sending traffic alerts to users via SMS, apps, etc.

[0063] In one or more alternative embodiments, multiple remaining data thresholds can be set to enable phased early warning.

[0064] For example, three remaining balance thresholds can be set to implement three-stage alerts. These thresholds could be 80%, 98%, and 100%. The alerts for these three remaining balance thresholds could be... 80% warning rate (first alert) SMS / APP push notification: [Data usage warning] You have used 80% of your data plan for this month, with XXGB remaining. Please use your data responsibly to avoid exceeding your data limit and incurring additional charges.

[0065] 95% / Almost depleted (Second reminder) SMS / APP push notification: [Urgent Reminder] Your data plan is about to run out. You have XXMB remaining. We recommend that you subscribe to a data package or close unnecessary applications in time.

[0066] Exceeded limits / charged (instant notification) SMS / APP push notification: [Data Exceedance Reminder] You have exceeded your data plan by XXMB, incurring an extra charge of XX yuan. We recommend that you immediately purchase a data top-up package.

[0067] In the solution provided in this embodiment, by obtaining the user's individual coefficient within a set historical time range and the data allowance included in the user's purchased communication package, the user's bonus data allowance is calculated based on the user value coefficient, data allowance, and a set adjustment coefficient. Using the solution of this application, dynamic and differentiated bonus data allowances can be awarded to users.

[0068] The following table compares the solutions of related technologies with the solution of this application:

[0069] This application also provides a flow calculation device, which may include a data acquisition module, a dynamic threshold calculation module, and a multi-dimensional judgment module.

[0070] The data acquisition module is used to collect users' historical data usage, real-time data plan availability, terminal type data, and network access type data. Real-time data allowance information: Real-time monitoring of users' remaining data allowance provides key information for dynamic threshold calculation and data gifting decisions, ensuring the rationality and timeliness of data gifting.

[0071] Terminal type data: Collect information on the type of terminal device used by users, such as high-end flagship mobile phones, mid-to-low-end smartphones, tablets, IoT devices, etc. The performance and functional differences of different terminal devices will affect users' data usage behavior.

[0072] Network access type data: Accurately identifies the user's current network access type, including 4G, 5G, WiFi, etc. Different network access types have different bandwidth, stability, and speed, which significantly impacts the user's data usage experience and data consumption patterns. For example, the high speed of 5G networks means that users consume data more quickly when watching high-definition videos or downloading large files.

[0073] Dynamic threshold calculation module: A sliding window algorithm is used to analyze user data consumption trends in real time, capturing short-term fluctuations and long-term trends in data usage. Simultaneously, a time series forecasting model, such as the exponential smoothing model, is introduced, combined with historical user data, to accurately predict future user data consumption. The specific steps are as follows: First, obtain the user's reading and browsing data from the past N months to construct a time-series dataset.

[0074] Then, the historical data is processed using the exponential smoothing method to predict the expected value of traffic consumption in the current month, taking into full account the trend and seasonal characteristics of the data.

[0075] Finally, based on the remaining balance of each user's data plan, a dynamic threshold T is generated. The calculation formula is: T = predicted value. (1+a), where a is an adjustment coefficient, ranging from 0.1 to 0.5, which can be flexibly adjusted according to actual operating conditions and data analysis results.

[0076] Multidimensional judgment module: Calculate the free data allowance: Free data allowance = Basic allowance (1+a) User Personalization Coefficient. The user personalization coefficient includes user value coefficient, behavioral activity coefficient, scenario consistency coefficient, and package sensitivity coefficient, which enables differentiated data traffic gifting.

[0077] This disclosure also provides a flow calculation apparatus for performing the flow calculation method provided in any of the above embodiments. Figure 2 As shown, the device includes: The acquisition module 21 is used to acquire the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user. The user personality coefficient is used to reflect the user's consumption contribution and historical loyalty. The calculation module 22 is used to calculate the user's free data allowance based on the user's personality coefficient, the data allowance, and the set adjustment coefficient.

[0078] In one or more embodiments, the user personality coefficient includes at least one of the following: User value coefficient; Behavioral activity coefficient; Scene consistency coefficient; Package sensitivity coefficient; The user value coefficient is related to the user's average monthly consumption level, the behavior activity coefficient is related to the user's average weekly data consumption, the scenario consistency coefficient is related to the user's similar behavior cycle repetition rate, and the package sensitivity coefficient is related to the user's package change frequency.

[0079] In one or more embodiments, the weight of the user value coefficient is greater than the weight of the behavior activity coefficient, the weight of the behavior activity coefficient is greater than the weight of the scenario consistency coefficient, and the weight of the scenario consistency coefficient is greater than the weight of the package sensitivity coefficient.

[0080] In one or more embodiments, calculating the user's gifted data allowance based on the user value coefficient, the data allowance, and a set adjustment coefficient includes: Sum the adjustment coefficient with 1; The product of the summation result, the data allowance, and the adjustment coefficient is calculated to obtain the gifted data allowance.

[0081] In one or more embodiments, it further includes: Obtain the user's reading and browsing data over the past N months; Based on the reading and browsing data, predict the user's expected data consumption for the current month; Calculate the remaining data allowance based on the data allowance and the expected data consumption. If the remaining data allowance is less than or equal to the remaining data allowance threshold, the step of obtaining the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user is executed.

[0082] In one or more embodiments, it further includes: If the remaining data allowance is less than or equal to the remaining data allowance threshold, a data usage warning will be sent to the user.

[0083] The flow calculation device and the flow calculation method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0084] This disclosure also provides an electronic device for performing the above-described flow calculation method. Please refer to... Figure 3 It illustrates a schematic diagram of an electronic device provided by some embodiments of this disclosure. For example... Figure 3 As shown, the electronic device 3 includes: a processor 300, a memory 301, a bus 302, and a communication interface 303. The processor 300, the communication interface 303, and the memory 301 are connected via the bus 302. The memory 301 stores a computer program that can run on the processor 300. When the processor 300 runs the computer program, it executes the flow calculation method provided in any of the foregoing embodiments of this disclosure.

[0085] The memory 301 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 303 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0086] Bus 302 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs. After receiving an execution instruction, the processor 300 executes the program. The flow calculation method disclosed in any of the foregoing embodiments of this disclosure can be applied to the processor 300, or implemented by the processor 300.

[0087] The processor 300 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 300 or by instructions in software form. The processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 301. The processor 300 reads the information in memory 301 and, in conjunction with its hardware, completes the steps of the above method.

[0088] The electronic device provided in this disclosure and the traffic calculation method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0089] This disclosure also provides a computer-readable storage medium corresponding to the traffic calculation method provided in the foregoing embodiments. The computer-readable storage medium is an optical disc, on which a computer program (i.e., a computer program product) is stored. When the computer program is run by a processor, it executes the traffic calculation method provided in any of the foregoing embodiments.

[0090] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0091] The computer-readable storage medium provided in the above embodiments of this disclosure and the traffic calculation method provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0092] This disclosure also provides a computer program product; please refer to [reference needed]. Figure 4 The computer program product 400 carries program code, namely computer program 401. The instructions included in the computer program 401 can be used to execute the steps of the flow calculation method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0093] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0094] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0095] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0096] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0097] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0098] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0099] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0100] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A flow rate calculation method, characterized in that, include: The system obtains the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user. The user personality coefficient is used to reflect the user's consumption contribution and historical loyalty. The user's gifted data allowance is calculated based on the user's personality coefficient, the data allowance, and the set adjustment coefficient.

2. The method according to claim 1, characterized in that, The user personality coefficient includes at least one of the following: User value coefficient; Behavioral activity coefficient; Scene consistency coefficient; Package sensitivity coefficient; The user value coefficient is related to the user's average monthly consumption level, the behavior activity coefficient is related to the user's average weekly data consumption, the scenario consistency coefficient is related to the user's similar behavior cycle repetition rate, and the package sensitivity coefficient is related to the user's package change frequency.

3. The method according to claim 2, characterized in that, The weight of the user value coefficient is greater than the weight of the behavior activity coefficient, the weight of the behavior activity coefficient is greater than the weight of the scenario consistency coefficient, and the weight of the scenario consistency coefficient is greater than the weight of the package sensitivity coefficient.

4. The method according to claim 1, characterized in that, Based on the user value coefficient, the data allowance, and the set adjustment coefficient, the user's gifted data allowance is calculated, including: Sum the adjustment coefficient with 1; The product of the summation result, the data allowance, and the adjustment coefficient is calculated to obtain the gifted data allowance.

5. The method according to claim 1, characterized in that, Also includes: Obtain the user's reading and browsing data over the past N months; Based on the reading and browsing data, predict the user's expected data consumption for the current month; Calculate the remaining data allowance based on the data allowance and the expected data consumption. If the remaining data allowance is less than or equal to the remaining data allowance threshold, the step of obtaining the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user is executed.

6. The method according to claim 5, characterized in that, Also includes: If the remaining data allowance is less than or equal to the remaining data allowance threshold, a data usage warning will be sent to the user.

7. A flow calculation device, characterized in that, include: The acquisition module is used to acquire the user's personality coefficient within a set historical time range and the data allowance included in the communication package purchased by the user. The user personality coefficient is used to reflect the user's consumption contribution and historical loyalty. The calculation module is used to calculate the user's gifted data allowance based on the user's personality coefficient, the data allowance, and the set adjustment coefficient.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-6.

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