Mobile phone traffic point giving method and system based on big data analysis

Abstract

The invention relates to the technical field of traffic calculation, and discloses a mobile phone traffic point giving method and system based on big data analysis. The method comprises the following steps: acquiring first resource use data and second resource use data of a user; performing first integral calculation according to the first resource use data to obtain a first given integral; performing first flow calculation according to the first resource use data to obtain a first flow use value; when the first traffic usage value is greater than a first preset traffic threshold value, performing second integral calculation to obtain a second given integral; performing second traffic calculation according to the second resource use data to obtain a second traffic use value; and when the second traffic use value is greater than a second preset traffic threshold value, stopping point giving, and giving points based on a preset point giving condition. According to the method, the integral can be calculated and given through traffic threshold management, and the integral giving rule of traffic used by the user in different scenes is optimized.

Description

Technical Field

[0001] The present invention relates to the field of traffic calculation technology, and in particular to a method and system for granting mobile phone traffic points based on big data analysis. Background Art

[0002] With the advancement of science and technology and the development of mobile communication technology, mobile phones play an increasingly important role in people's lives and play an irreplaceable role in people's daily lives. As the competition in the mobile phone market becomes increasingly fierce, major operators provide various mobile phone package services to attract and retain more mobile phone users.

[0003] The traditional way of giving mobile phone credits is to give credits by using mobile phone traffic, and then after accumulating a certain number of credits, redeem various mobile phones, traffic and other products in the credits mall. In the existing technology, credits are given in a single way, and the association between traffic usage and credits is limited to simple set rules. It does not have adaptive capabilities, cannot flexibly distribute credits, and cannot meet users' needs for credits giving and obtaining in different usage scenarios. Summary of the invention

[0004] The present invention provides a method and system for giving mobile phone traffic points based on big data analysis, which can calculate and give points through traffic threshold management, avoid the problem of giving too many or too few points, and meet the user's needs for giving and obtaining points in different usage scenarios.

[0005] In the first aspect, in order to solve the above technical problems, the present invention provides a method for giving mobile data points based on big data analysis, comprising: Acquire resource usage data of a user; wherein the resource usage data includes first resource usage data and second resource usage data; Performing a first points calculation according to the first resource usage data to obtain first gift points; Performing a first flow calculation according to the first resource usage data to obtain a first flow usage value; When the first traffic usage value is greater than a first preset traffic threshold, a second points calculation is performed to obtain second gift points; Perform a second flow calculation according to the second resource usage data to obtain a second flow usage value; When the second traffic usage value is greater than the second preset traffic threshold, the point granting for the day is stopped, and based on the preset point granting conditions, point granting is performed according to the first granting points and the second granting points.

[0006] Preferably, the first resource usage data includes pre-threshold traffic consumption, pre-threshold text usage times, pre-threshold video usage times, and pre-threshold picture usage times; The second resource usage data includes post-threshold traffic consumption, post-threshold text usage times, post-threshold video usage times, and post-threshold picture usage times.

[0007] Preferably, performing a first point calculation according to the first resource usage data to obtain a first gift point includes: The calculation formula for the first integral calculation is: In the formula, Points are awarded for the first gift; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; is the first integral weight coefficient, corresponding to the integral weights of traffic, text, video and picture respectively.

[0008] Preferably, performing a first flow calculation according to the first resource usage data to obtain a first flow usage value includes: The calculation formula for the first flow calculation is: In the formula, is the first flow usage value; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

[0009] Preferably, when the first traffic usage value is greater than a first preset traffic threshold, performing a second points calculation to obtain a second gift points includes: The calculation formula of the second integral calculation is: In the formula, Points are awarded for the second gift; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the second integral weight coefficient, corresponding to the integral weights of traffic, text, video, and picture respectively.

[0010] Preferably, performing a second flow calculation according to the second resource usage data to obtain a second flow usage value includes: The second flow calculation formula is: In the formula, is the second flow usage value; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

[0011] Preferably, the step of performing point granting based on the preset point granting condition and the first granting points and the second granting points includes: When the first traffic usage value is equal to a first preset traffic threshold, first gift points are gifted; When the first traffic usage value is greater than a first preset traffic threshold, second gift points are gifted based on a set time interval.

[0012] In a second aspect, the present invention provides a mobile phone traffic points granting system based on big data analysis, comprising: A data acquisition module is used to obtain user resource usage data; A first points calculation module, configured to perform a first points calculation according to the first resource usage data to obtain a first gift point; A first flow calculation module, configured to perform a first flow calculation according to the first resource usage data to obtain a first flow usage value; A second integral calculation module, configured to perform a second integral calculation to obtain a second gift integral when the first flow usage value is greater than a first preset flow threshold; A second flow calculation module, configured to perform a second flow calculation according to the second resource usage data to obtain a second flow usage value; The points granting module is used to stop the points granting for the day when the second traffic usage value is greater than the second preset traffic threshold, and to grant points according to the first granting points and the second granting points based on the preset points granting conditions.

[0013] In a third aspect, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for giving mobile phone traffic points based on big data analysis.

[0014] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for giving mobile phone traffic points based on big data analysis.

[0015] Compared with the prior art, the present invention has the following beneficial effects: the embodiment of the present invention provides a method and system for giving mobile phone traffic points based on big data analysis. The method includes: obtaining the user's resource usage data; wherein the resource usage data includes first resource usage data and second resource usage data; performing a first point calculation according to the first resource usage data to obtain a first gift point; performing a first traffic calculation according to the first resource usage data to obtain a first traffic usage value; when the first traffic usage value is greater than a first preset traffic threshold, performing a second point calculation to obtain a second gift point; performing a second traffic calculation according to the second resource usage data to obtain a second traffic usage value; when the second traffic usage value is greater than a second preset traffic threshold, stopping the gift of points for the day, and based on the preset point gift condition, performing the gift of points according to the first gift point and the second gift point.

[0016] In the present invention, the method can achieve reasonable point granting by obtaining the user's resource usage data and combining the preset traffic threshold and point calculation rules. When the user's traffic usage reaches the preset threshold, the corresponding point calculation is performed to dynamically adjust the number of points granted. The beneficial effect is that the method can avoid too many or too few points being granted, ensure that the point allocation is consistent with the user's actual usage, and thus meet the user's needs for point granting and obtaining in different usage scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a method for giving mobile data points based on big data analysis provided by the first embodiment of the present invention; Figure 2 It is a structural diagram of a mobile phone traffic points granting system based on big data analysis provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 The first embodiment of the present invention provides a method for giving mobile data points based on big data analysis, comprising the following steps: S11, obtaining user's resource usage data.

[0020] S12: Perform a first points calculation based on the first resource usage data to obtain first gift points.

[0021] S13: Perform a first flow calculation based on the first resource usage data to obtain a first flow usage value.

[0022] S14: When the first traffic usage value is greater than a first preset traffic threshold, a second points calculation is performed to obtain second gift points.

[0023] S15: Perform a second flow calculation based on the second resource usage data to obtain a second flow usage value.

[0024] S16, when the second traffic usage value is greater than the second preset traffic threshold, stop the point granting for the day, and based on the preset point granting conditions, perform point granting according to the first granting points and the second granting points.

[0025] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0026] In step S11, the user's resource usage data is obtained, wherein the resource usage data includes first resource usage data and second resource usage data.

[0027] Preferably, the first resource usage data includes pre-threshold traffic consumption, pre-threshold text usage times, pre-threshold video usage times, and pre-threshold picture usage times; The second resource usage data includes post-threshold traffic consumption, post-threshold text usage times, post-threshold video usage times, and post-threshold picture usage times.

[0028] Specifically, the first resource usage data mainly refers to the usage data before the user reaches the preset traffic threshold, covering traffic consumption, text usage, video usage, and picture usage. These data reflect the user's basic usage and provide a basis for the system to calculate the initial points. The second resource usage data mainly refers to when the user exceeds the traffic threshold, the system will continue to monitor such data to control the upper limit of the points. Including traffic consumption after exceeding the threshold and the frequency of use of various multimedia content.

[0029] Specifically, in the first resource usage data, the pre-threshold traffic consumption refers to the network traffic usage before the user reaches the preset traffic threshold. The system records the user's traffic consumption data in order to calculate basic points. The pre-threshold text usage count records the number of times the user browses or uses text content before the threshold. For example, the system may record the user's text message or article browsing frequency through an application interface or using a log. The pre-threshold video usage count specifically records the number of times the user watches a video before the threshold. The system may count this data by analyzing the number of video accesses or playbacks. The pre-threshold image usage count records the number of times the user views or sends images, and these data can be obtained from the application's media access log or image loading record. In the present invention, these data are used to calculate basic points, mainly reflecting the user's usage before reaching the traffic threshold, and the system evaluates the first-stage point granting based on this data.

[0030] Specifically, in the second resource usage data, post-threshold traffic consumption means that when the user's traffic consumption exceeds the preset threshold, the system will record the subsequent traffic usage to control the upper limit of the points awarded. Post-threshold text usage is to continue to monitor the frequency of use of its text content after the user reaches the threshold to determine whether to stop awarding additional points. Post-threshold video usage records the frequency of users watching videos after the threshold to avoid points awards exceeding the preset range in scenarios with high data consumption. Post-threshold picture usage records the frequency of users viewing or sending pictures. Such data helps the system to reasonably stop awarding points after reaching the upper limit of the award.

[0031] Optionally, the system can monitor the user's traffic usage in real time through the device or network operator background to obtain resource usage data. In specific application scenarios, data can be obtained by analyzing the log files generated by the application or device, recording the traffic information of each data transmission, and thus calculating the user's traffic consumption. The system can monitor the user's browsing, sending or receiving text operations through the application layer API. For example, when a user reads an article or sends a message, the application will call the corresponding interface, and the system will use this to record the number of operations. The system records each video playback or viewing behavior of the user by analyzing the access logs related to video playback. The system will record the user's behavior of viewing, downloading or uploading pictures. For example, each time a user loads picture content, the application will generate a corresponding access record.

[0032] In step S12, a first points calculation is performed based on the first resource usage data to obtain first gift points.

[0033] Preferably, performing a first point calculation according to the first resource usage data to obtain a first gift point includes: The calculation formula for the first integral calculation is: In the formula, Points are awarded for the first gift; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; is the first integral weight coefficient, corresponding to the integral weights of traffic, text, video and picture respectively.

[0034] Specifically, the formula is used to participate in the point calculation according to each consumption data in the first resource usage data, so as to generate the first gift points. Each usage data is multiplied by the corresponding weight coefficient, and then the parts are summed to generate a comprehensive point value. In this way, the system can quantify the user's usage before the traffic threshold and provide different point weights for different resource usage.

[0035] Optionally, the system records the traffic consumption value in order to calculate the contribution of traffic consumption to the total points. Because traffic consumption is a core indicator of user network usage, this setting has the highest weight. Pre-threshold text usage records the user's demand for lightweight content (such as articles or messages). The system can give a weight lower than the traffic consumption weight value based on its impact on traffic. Video content occupies a higher amount of traffic, so the score weight of video usage will be higher than the text usage weight to more accurately reflect the impact of video usage on points. The traffic occupied by image content is between text and video. The system can balance the impact of user use of image content by assigning a value between the two weights as the image traffic consumption weight.

[0036] It is worth noting that the weight of each resource can be calculated in the following ways: historical data analysis, statistical analysis based on a large amount of historical data of users, and calculation of the average consumption of each type of resource. For example, the frequency of use of traffic, text, video and pictures and their contribution to traffic can be analyzed; machine learning model optimization, using machine learning models (such as regression analysis, cluster analysis, etc.) to analyze the relationship between resource use and points demand, and the weights of each item can be automatically adjusted by training the model; business rules and strategies, artificially set weights according to the business strategy of the company or platform. For example, if the platform wants to encourage users to use more video resources, it can artificially set a higher weight for videos; A / B testing, using A / B testing to evaluate the impact of different weight combinations on user behavior, testing different weight combinations with multiple groups of users, and analyzing user behavior and feedback, gradually find the optimal weight configuration; clustering methods in big data analysis, using clustering algorithms to group users' resource usage data, divide users into different usage behavior groups (such as users who prefer videos or text), and set specific weights for different groups. In step S13, a first flow calculation is performed according to the first resource usage data to obtain a first flow usage value.

[0037] Preferably, performing a first flow calculation according to the first resource usage data to obtain a first flow usage value includes: The calculation formula for the first flow calculation is: In the formula, is the first flow usage value; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

[0038] It is worth noting that, in the present invention, the traffic conversion coefficient is a parameter used to quantify the impact of different types of network resource usage on traffic consumption. In network traffic management, users may generate various types of data interactions, including text messages, pictures, videos, etc., and these different types of data contribute differently to network traffic. The traffic conversion coefficient is used to convert these different types of data usage into a unified traffic consumption unit (bit or byte) so that they can be compared and accumulated.

[0039] Specifically, in the calculation process, the traffic conversion coefficient is introduced, which is used to represent the traffic consumption ratio corresponding to different types of resource usage (such as traffic, text, video and picture). Through these coefficients, different types of usage behaviors can be converted into a unified traffic usage value. It is worth noting that the determination of the traffic conversion coefficient is based on the user's resource usage data for calculation. This coefficient is set in advance and needs to be called directly. Specifically, the traffic conversion coefficient is used to convert different types of resource usage (such as traffic consumption, text usage times, video usage times, and picture usage times) into a unified traffic usage value. The traffic conversion coefficient can be determined in the following ways: data analysis, using big data analysis technology to analyze the user's historical resource usage data to determine the contribution ratio of different resource usage types (such as traffic consumption, text, video, and picture usage times) to the total traffic consumption; model verification, verifying and adjusting these weight coefficients through traffic usage in actual applications to ensure that they can accurately reflect the impact of different resource usage on traffic; dynamic adjustment, dynamically adjusting the traffic conversion coefficient according to changes in network usage patterns and new data analysis results to maintain the accuracy and effectiveness of the point gifting system. In step S14, when the first traffic usage value is greater than the first preset traffic threshold, a second points calculation is performed to obtain second gift points.

[0040] Preferably, when the first traffic usage value is greater than a first preset traffic threshold, performing a second points calculation to obtain a second gift points includes: The calculation formula of the second integral calculation is: In the formula, Points are awarded for the second gift; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the second integral weight coefficient, corresponding to the integral weights of traffic, text, video, and picture respectively.

[0041] Specifically, Represents the points a user earns based on resource usage after meeting certain conditions. Indicates the amount of traffic consumed by the user after exceeding the first traffic threshold. This value refers to the portion of the user's traffic usage that exceeds the preset limit. Indicates the number of times a user has used text after exceeding the text usage threshold. Similarly, if a user's text usage exceeds the preset threshold, This is the number of times that the part exceeds the limit. and The same goes for this.

[0042] It is worth noting that the second point calculation quantifies the user's use of different types of resources into points. This quantification method can effectively reflect the user's contribution to the platform and help platform managers better understand user behavior. By setting the point weights of different resources, the platform can guide users to use specific types of resources. For example, you may want users to use more videos instead of texts, so the point weight of videos can be set higher.

[0043] Specifically, the points weight can be determined based on the platform's incentive strategy. For example, if the platform wants to encourage the consumption of video content, it can increase the weight of video usage so that users who watch videos can get more points. Data analysis: by analyzing user usage data, observing the frequency of use of different resources and user retention rate, and adjusting weights based on data analysis results. For example, if it is found that the user retention rate of video usage is higher, you can consider increasing the points weight of the video. Balance user behavior: weight setting should take into account user diversity to avoid over-concentration on a single resource. Weight setting can promote balanced use of different types of resources by users.

[0044] In step S15, a second flow calculation is performed based on the second resource usage data to obtain a second flow usage value.

[0045] Preferably, performing a second flow calculation according to the second resource usage data to obtain a second flow usage value includes: The second flow calculation formula is: In the formula, is the second flow usage value; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

[0046] Specifically, the traffic conversion coefficient used in calculating the second traffic usage value is the same as the traffic conversion coefficient used in calculating the first traffic usage value. The traffic conversion coefficient can be determined in the following ways: data analysis, by analyzing the user's historical usage data, calculating the actual traffic consumption corresponding to each resource usage, and obtaining an average value based on the data as the traffic conversion coefficient; experiments and A / B tests, by setting different traffic conversion coefficients in different user groups, observing the changes in user behavior and the actual situation of traffic consumption, and selecting the optimal coefficient based on the test results; model-based prediction, using statistical models or machine learning models to predict the relationship between different resource usage and traffic consumption, so as to determine the appropriate traffic conversion coefficient; considering resource characteristics, different resources (such as video, text, and pictures) have different traffic characteristics, such as video consumption of more traffic than text consumption, and picture consumption of more traffic than picture consumption. When determining the conversion coefficient, consider their differences and set it reasonably.

[0047] In step S16, when the second traffic usage value is greater than the second preset traffic threshold, the point granting for the day is stopped, and based on the preset point granting conditions, point granting is performed according to the first granting points and the second granting points.

[0048] Specifically, the main purpose of this step is to control traffic consumption, reduce resource abuse, and optimize user behavior. In specific application scenarios, if no upper limit is set, users may use a large amount of resources (such as videos, pictures, etc.) under the incentive of points, resulting in excessive resource consumption or additional costs. By setting the second traffic threshold, the system can control daily traffic consumption within a reasonable range to avoid excessive resource use. Prevent malicious point brushing. Some users may use resources frequently in order to obtain more points, and even simulate usage behavior through programs. This threshold can effectively prevent such abuse, ensure the fairness and rationality of point gifts, and reduce the risk of "point brushing" or "traffic abuse" in the system. Improve user quality. Setting traffic threshold restrictions can guide users to use resources reasonably and avoid unnecessary behaviors caused by unreasonable point incentives. This can improve the user's usage quality and encourage more efficient and valuable resource utilization. Optimize user experience. Through traffic usage restrictions, the platform can reduce network congestion or service delays caused by large resource consumption and improve the overall user experience. Limiting the upper limit of daily point gifts also makes the user experience more balanced and controllable.

[0049] Preferably, the step of performing point granting based on the preset point granting condition and the first granting points and the second granting points includes: When the first traffic usage value is equal to a first preset traffic threshold, first gift points are gifted; When the first traffic usage value is greater than a first preset traffic threshold, second gift points are gifted based on a set time interval.

[0050] Specifically, this gifting mechanism means that when the user's traffic usage reaches a certain standard (the first preset traffic threshold), a point reward can be obtained. This can encourage users to use resources to meet this standard, but will not encourage them to over-consume. The first gift points are relatively simple to give, based on whether the user's traffic usage meets the threshold, and no time interval is involved. When the user's traffic usage exceeds the threshold, by setting the time interval point reward, the platform can avoid the rapid consumption of resources caused by the one-time point gift, and can also encourage users to continue to use resources. This point rule is suitable for scenarios where the platform wants users to continue to use actively after reaching the threshold. It can also help control the system load and avoid the imbalance of incentives caused by the one-time reward of too many points. In general, in the present invention, the point gift rule includes two stages. The first stage: when the user reaches the threshold, the first gift points are immediately awarded to ensure that the user can get the reward when the user reaches the basic usage standard preset by the platform. The second stage: after the user exceeds the threshold, the second gift points are continuously awarded at a set time interval to encourage the user to remain active above the basic usage standard, while controlling the frequency and magnitude of the point gift.

[0051] It is worth noting that the way to set the time interval will affect the rhythm of points awarding and user activity. It can be determined in the following ways: Analyze the user's active time. By analyzing the user's activity in different time periods, determine the best time interval. For example, if the average active time of the user is 2 hours, the time interval can be set to 30 minutes to 1 hour to ensure that the points can cover most of the user's active time. Product goals and user experience. If you want users to interact frequently in a short period of time, you can shorten the time interval, such as awarding points every 15 or 30 minutes. On the contrary, if you want users to use it for a longer time, you can set a longer time interval, such as 1 hour or longer. Too short time intervals may cause frequent reminders of points and disturb users, while too long time intervals may reduce users' interest in earning points. Therefore, it is necessary to balance according to the specific user experience goals of the product. System load and cost control. In the case of high concurrency, the time interval can be extended to avoid excessive system pressure. For example, when the user access volume is at a peak (such as 8 to 10 pm), the time interval is extended to relieve the load. Adaptive time interval. For highly active users, the system can shorten the time interval to increase stickiness; for low-active users, the time interval can be extended to maintain long-term use.

[0052] The following describes the working process of the present invention by taking a relatively common scenario as an example. The working process is as follows: When users use their mobile phones in daily life, they will perform various activities, such as browsing the web, sending text messages, watching videos, viewing pictures, etc. The system monitors and records the user's resource usage data in real time through the data acquisition module, including traffic consumption, text usage times, video usage times, and picture usage times.

[0053] When a user uses traffic, the system divides it into traffic consumption before the threshold (first resource usage data) according to the preset threshold. The system uses the first points calculation module to calculate the user's first gift points according to the first resource usage data and the preset first weight coefficient of points. At the same time, the first traffic calculation module calculates the user's first traffic usage value according to the first resource usage data and the traffic conversion coefficient.

[0054] If the user's first traffic usage value exceeds the preset first traffic threshold, the system will enter the second stage. The system starts to record the traffic consumption after the threshold (second resource usage data), and uses the second point calculation module to calculate the user's second gift points based on the second resource usage data and the second weight coefficient of the points. At the same time, the second traffic calculation module calculates the user's second traffic usage value based on the second resource usage data and the traffic conversion coefficient.

[0055] If the user's second traffic usage value exceeds the second preset traffic threshold, the system will stop the point granting for the day through the point granting module. If the user's second traffic usage value does not exceed the second preset traffic threshold, the system will grant points through the point granting module based on the preset point granting conditions according to the user's first granting points and second granting points.

[0056] When users reach or exceed the traffic threshold, they will receive corresponding points as rewards, which motivates them to continue using the service and may redeem rewards in the points mall. The system reasonably sets the traffic conversion coefficient and the points weight coefficient to ensure that the points distribution is consistent with the actual usage of the user, avoiding the problem of giving too many or too few points.

[0057] In summary, the present invention provides a method for granting mobile phone traffic points based on big data analysis, including: obtaining user resource usage data; wherein the resource usage data includes first resource usage data and second resource usage data; performing a first point calculation based on the first resource usage data to obtain a first gift point; performing a first traffic calculation based on the first resource usage data to obtain a first traffic usage value; when the first traffic usage value is greater than a first preset traffic threshold, performing a second point calculation to obtain a second gift point; performing a second traffic calculation based on the second resource usage data to obtain a second traffic usage value; when the second traffic usage value is greater than a second preset traffic threshold, stopping the gift of points for the day, and based on the preset point gift conditions, performing point gifting based on the first gift points and the second gift points.

[0058] In the present invention, the method can achieve reasonable point granting by obtaining the user's resource usage data and combining the preset traffic threshold and point calculation rules. When the user's traffic usage reaches the preset threshold, the corresponding point calculation is performed to dynamically adjust the number of points granted. The beneficial effect is that the method can avoid too many or too few points being granted, ensuring that the point allocation is consistent with the user's actual usage.

[0059] Reference Figure 2 The second embodiment of the present invention provides a mobile phone traffic points granting system based on big data analysis, including: A data acquisition module is used to obtain user resource usage data; A first points calculation module, configured to perform a first points calculation according to the first resource usage data to obtain a first gift point; A first flow calculation module, configured to perform a first flow calculation according to the first resource usage data to obtain a first flow usage value; A second integral calculation module, configured to perform a second integral calculation to obtain a second gift integral when the first flow usage value is greater than a first preset flow threshold; A second flow calculation module, configured to perform a second flow calculation according to the second resource usage data to obtain a second flow usage value; The points granting module is used to stop the points granting for the day when the second traffic usage value is greater than the second preset traffic threshold, and to grant points according to the first granting points and the second granting points based on the preset points granting conditions.

[0060] In an optional implementation, the first integral calculation module is specifically configured to: Perform a first integral calculation, the calculation formula of the first integral calculation is: In the formula, Points are awarded for the first gift; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; is the first integral weight coefficient, corresponding to the integral weights of traffic, text, video and picture respectively.

[0061] In an optional implementation, the first flow calculation module is specifically used to: Perform a first flow calculation, the calculation formula of the first flow calculation is: In the formula, is the first flow usage value; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

[0062] In an optional implementation, the second integral calculation module is specifically used to: Perform a second integral calculation, the calculation formula of the second integral calculation is: In the formula, Points are awarded for the second gift; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the second integral weight coefficient, corresponding to the integral weights of traffic, text, video, and picture respectively.

[0063] In an optional implementation, the second flow calculation module is specifically used to: Perform a second flow calculation, the second flow calculation formula is: In the formula, is the second flow usage value; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

[0064] In an optional implementation, the points granting module is specifically used for: When the first traffic usage value is equal to a first preset traffic threshold, first gift points are gifted; When the first traffic usage value is greater than a first preset traffic threshold, second gift points are gifted based on a set time interval.

[0065] It should be noted that the mobile phone traffic points giving system based on big data analysis provided in the embodiment of the present invention is used to execute all the process steps of the mobile phone traffic points giving method based on big data analysis in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0066] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for a method for giving mobile data points based on big data analysis. When the processor executes the computer program, the steps in each of the above-mentioned embodiments of the method for giving mobile data points based on big data analysis are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the points granting module.

[0067] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0068] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0069] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0070] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0071] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0072] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0073] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for giving mobile data points based on big data analysis, characterized in that: include: Acquire resource usage data of a user; wherein the resource usage data includes first resource usage data and second resource usage data; Performing a first points calculation according to the first resource usage data to obtain first gift points; Performing a first flow calculation according to the first resource usage data to obtain a first flow usage value; When the first traffic usage value is greater than a first preset traffic threshold, a second points calculation is performed to obtain second gift points; Perform a second flow calculation according to the second resource usage data to obtain a second flow usage value; When the second traffic usage value is greater than the second preset traffic threshold, the point granting for the day is stopped, and based on the preset point granting conditions, point granting is performed according to the first granting points and the second granting points.

2. The method for giving mobile data points based on big data analysis according to claim 1 is characterized in that: The first resource usage data includes pre-threshold traffic consumption, pre-threshold text usage times, pre-threshold video usage times, and pre-threshold picture usage times; The second resource usage data includes post-threshold traffic consumption, post-threshold text usage times, post-threshold video usage times, and post-threshold picture usage times.

3. The method for giving mobile data points based on big data analysis according to claim 1 is characterized in that: The step of performing a first points calculation according to the first resource usage data to obtain a first gift points includes: The calculation formula for the first integral calculation is: In the formula, Points are awarded for the first gift; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; is the first integral weight coefficient, corresponding to the integral weights of traffic, text, video and picture respectively.

4. The method for giving mobile data points based on big data analysis according to claim 1 is characterized in that: The performing a first flow calculation according to the first resource usage data to obtain a first flow usage value includes: The calculation formula for the first flow calculation is: In the formula, is the first flow usage value; is the traffic consumption before the threshold; is the number of times the text is used before the threshold; is the number of times the video is used before the threshold; is the number of times the image is used before the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

5. The method for giving mobile data points based on big data analysis according to claim 1 is characterized in that: When the first traffic usage value is greater than the first preset traffic threshold, performing a second point calculation to obtain a second gift point includes: The calculation formula of the second integral calculation is: In the formula, Points are awarded for the second gift; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the second integral weight coefficient, corresponding to the integral weights of traffic, text, video, and picture respectively.

6. The method for giving mobile data points based on big data analysis according to claim 1 is characterized in that: The performing a second flow calculation according to the second resource usage data to obtain a second flow usage value includes: The second flow calculation formula is: In the formula, is the second flow usage value; is the traffic consumption after the threshold; is the number of times the text is used after the threshold; is the number of times the video is used after the threshold; is the number of times the image is used after the threshold; and is the traffic conversion coefficient, which represents the traffic consumption ratio used by data, text, video and picture respectively.

7. The method for giving mobile data points based on big data analysis according to claim 1 is characterized in that: The step of performing point granting based on the preset point granting condition and the first granting points and the second granting points includes: When the first traffic usage value is equal to a first preset traffic threshold, first gift points are gifted; When the first traffic usage value is greater than a first preset traffic threshold, second gift points are gifted based on a set time interval.

8. A mobile phone traffic points granting system based on big data analysis, characterized in that: include: A data acquisition module, used to acquire resource usage data of a user; wherein the resource usage data includes first resource usage data and second resource usage data; A first points calculation module, configured to perform a first points calculation according to the first resource usage data to obtain a first gift point; A first flow calculation module, configured to perform a first flow calculation according to the first resource usage data to obtain a first flow usage value; A second integral calculation module, configured to perform a second integral calculation to obtain a second gift integral when the first flow usage value is greater than a first preset flow threshold; A second flow calculation module, configured to perform a second flow calculation according to the second resource usage data to obtain a second flow usage value; The points granting module is used to stop the points granting for the day when the second traffic usage value is greater than the second preset traffic threshold, and to grant points according to the first granting points and the second granting points based on the preset points granting conditions.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the mobile phone traffic points gifting method based on big data analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the mobile phone traffic points granting method based on big data analysis as described in any one of claims 1 to 7.

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