Mobile phone traffic points gifting method and system based on big data analysis

Abstract

The present invention relates to the field of traffic calculation technology, and discloses a method and system for granting mobile phone traffic points based on big data analysis. The method includes obtaining a user's first resource usage data and second resource usage data; performing a first points 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 points 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 points granting, and performing the points granting based on the preset points granting conditions. The method can calculate and grant points through traffic threshold management, and optimize the points granting rules for users using traffic in different scenarios.

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 communications, mobile phones are playing an increasingly important role in people's lives and are irreplaceable in their daily lives. As competition in the mobile phone market becomes increasingly fierce, major operators are offering a variety of mobile phone packages to attract and retain more mobile phone users.

[0003] Traditionally, mobile phone credits are awarded based on mobile data usage. Once a certain number of points are accumulated, they can be redeemed in a points mall for various products, such as mobile phones and data. Existing technology offers a single point-giving system, with the association of data usage with points limited to simple, pre-defined rules. This system lacks adaptability, offers limited flexibility in point allocation, and fails to meet users' needs for point giving and earning in diverse 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 users' needs for giving and obtaining points in different usage scenarios.

[0005] In a 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:

[0006] Acquire resource usage data of the user; wherein the resource usage data includes first resource usage data and second resource usage data;

[0007] Performing a first points calculation based on the first resource usage data to obtain first gift points;

[0008] Performing a first flow calculation based on the first resource usage data to obtain a first flow usage value;

[0009] When the first traffic usage value is greater than a first preset traffic threshold, performing a second points calculation to obtain second gift points;

[0010] Performing a second flow calculation based on the second resource usage data to obtain a second flow usage value;

[0011] 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.

[0012] 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;

[0013] 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.

[0014] Preferably, performing a first points calculation based on the first resource usage data to obtain first gift points includes:

[0015] The calculation formula for the first integral calculation is:

[0016]

[0017] Where, 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.

[0018] Preferably, performing a first traffic calculation based on the first resource usage data to obtain a first traffic usage value includes:

[0019] The calculation formula for the first flow calculation is:

[0020]

[0021] Where, 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.

[0022] Preferably, when the first traffic usage value is greater than a first preset traffic threshold, performing a second points calculation to obtain second bonus points includes:

[0023] The calculation formula for the second integral calculation is:

[0024]

[0025] Where, Points are awarded for the second; 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.

[0026] Preferably, performing a second traffic calculation based on the second resource usage data to obtain a second traffic usage value includes:

[0027] The second flow calculation formula is:

[0028]

[0029] Where, 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.

[0030] Preferably, the step of awarding points based on the preset points awarding conditions and according to the first awarding points and the second awarding points includes:

[0031] When the first traffic usage value is equal to a first preset traffic threshold, first gift points are awarded;

[0032] When the first traffic usage value is greater than a first preset traffic threshold, second gift points are awarded based on a set time interval.

[0033] In a second aspect, the present invention provides a mobile data points granting system based on big data analysis, comprising:

[0034] Data acquisition module, used to obtain user resource usage data;

[0035] A first points calculation module, configured to perform a first points calculation based on the first resource usage data to obtain first gift points;

[0036] a first flow calculation module, configured to perform a first flow calculation based on the first resource usage data to obtain a first flow usage value;

[0037] A second points calculation module, configured to perform a second points calculation to obtain second bonus points when the first traffic usage value is greater than a first preset traffic threshold;

[0038] A second flow calculation module, configured to perform a second flow calculation based on the second resource usage data to obtain a second flow usage value;

[0039] 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 based on the preset points granting conditions according to the first granting points and the second granting points.

[0040] 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.

[0041] 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.

[0042] Compared with the prior art, the present invention has the following beneficial effects: an embodiment of the present invention provides a method and system for granting mobile data points based on big data analysis. The method includes: obtaining 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 based on the first resource usage data to obtain first gift points; 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 points calculation to obtain a second gift points; 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 gifting of points for the day, and granting points based on the first gift points and the second gift points based on preset points gifting conditions.

[0043] In the present invention, the method can achieve reasonable point allocation by obtaining user resource usage data and combining it with preset traffic thresholds and point calculation rules. When a user's traffic usage reaches the preset threshold, the corresponding point calculation is performed, thereby dynamically adjusting the number of points allocated. The beneficial effect is that the method can avoid excessive or insufficient point allocation, ensuring that point allocation is consistent with the user's actual usage, thereby meeting the user's needs for point allocation and acquisition in different usage scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a method for giving mobile data points based on big data analysis provided by the first embodiment of the present invention;

[0045] Figure 2 It is a structural diagram of a mobile phone traffic points giving system based on big data analysis provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] 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:

[0048] S11, obtaining the user's resource usage data.

[0049] S12: Perform a first points calculation based on the first resource usage data to obtain first gift points.

[0050] S13: Perform a first traffic calculation based on the first resource usage data to obtain a first traffic usage value.

[0051] 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.

[0052] S15: Perform a second traffic calculation based on the second resource usage data to obtain a second traffic usage value.

[0053] 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, grant points according to the first granting points and the second granting points.

[0054] To facilitate understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0055] 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.

[0056] 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;

[0057] 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.

[0058] 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 image usage. This data reflects the user's basic usage and provides a basis for the system to calculate the initial points. The second resource usage data mainly refers to the data that the system will continue to monitor after the user exceeds the traffic threshold to control the upper limit of the points. This includes traffic consumption after exceeding the threshold and the frequency of use of various multimedia content.

[0059] 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 will record 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 the application interface or usage 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 an image. 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. The system evaluates the first-stage point granting based on this data.

[0060] 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 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. This type of data helps the system reasonably stop awarding points after reaching the upper limit of the award.

[0061] 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.

[0062] In step S12, a first points calculation is performed based on the first resource usage data to obtain first gift points.

[0063] Preferably, performing a first points calculation based on the first resource usage data to obtain first gift points includes:

[0064] The calculation formula for the first integral calculation is:

[0065]

[0066] Where, 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.

[0067] Specifically, the formula is used to calculate points based on each consumption data item in the first resource usage data, thereby generating the first bonus points. Each usage data item is multiplied by the corresponding weight coefficient, and then the various components are summed to generate a comprehensive point value. In this way, the system can quantify the user's usage before the traffic threshold and assign different point weights to different resource usage.

[0068] Optionally, the system records traffic consumption values ​​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. The number of text uses before the threshold 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 point weight of video use times will be higher than the text use weight to more accurately reflect the impact of video use 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.

[0069] It's worth noting that the weights of various resource usage can be calculated through the following methods: historical data analysis, which uses statistical analysis based on a large amount of historical user data to calculate the average consumption of various resource types. For example, the frequency of use and contribution to traffic for traffic, text, video, and images can be analyzed; machine learning model optimization, which uses machine learning models (such as regression analysis and cluster analysis) to analyze the relationship between resource usage and points demand. The model can then be trained to automatically adjust the weights of various resources; business rules and policies, which manually set weights based on the company or platform's business strategy. For example, if the platform wants to encourage users to use more video resources, it can artificially set a higher weight for video; A / B testing, which uses A / B testing to evaluate the impact of different weight combinations on user behavior. By testing different weight combinations with multiple groups of users and analyzing user behavior and feedback, the optimal weight configuration can be gradually found; clustering methods in big data analysis, which use clustering algorithms to group user resource usage data, dividing users into different usage behavior groups (such as those who prefer video or text), and setting specific weights for different groups.

[0070] In step S13, a first flow calculation is performed based on the first resource usage data to obtain a first flow usage value.

[0071] Preferably, performing a first traffic calculation based on the first resource usage data to obtain a first traffic usage value includes:

[0072] The calculation formula for the first flow calculation is:

[0073]

[0074] Where, 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.

[0075] It's 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 interact with various types of data, including text messages, images, and videos. 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 (bits or bytes), enabling comparison and accumulation.

[0076] Specifically, the calculation process introduces traffic conversion coefficients, which represent the traffic consumption ratios corresponding to different types of resource usage (such as data, text, video, and image usage). These coefficients can be used to convert different types of usage into a unified traffic usage value. It's important to note that the traffic conversion coefficient is calculated based on user resource usage data. This coefficient is pre-set and needs to be directly applied. Specifically, the traffic conversion coefficient is used to convert different types of resource usage (such as data consumption, text usage, video usage, and image usage) into a unified traffic usage value. The traffic conversion coefficient can be determined through the following methods: data analysis: using big data analytics to analyze users' historical resource usage data to determine the contribution of different resource usage types (such as data consumption, text, video, and image usage) to total traffic consumption; model validation: verifying and adjusting these weighting coefficients through actual traffic usage to ensure they accurately reflect the impact of different resource usage on traffic; and dynamic adjustment: dynamically adjusting the traffic conversion coefficient based on changes in network usage patterns and new data analysis results to maintain the accuracy and effectiveness of the points reward system.

[0077] In step 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.

[0078] Preferably, when the first traffic usage value is greater than a first preset traffic threshold, performing a second points calculation to obtain second bonus points includes:

[0079] The calculation formula for the second integral calculation is:

[0080]

[0081] Where, Points are awarded for the second; 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.

[0082] Specifically, Represents the points a user earns based on resource usage after meeting certain conditions. Indicates the amount of traffic consumed by a user after exceeding the first traffic threshold. This value indicates 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.

[0083] It's worth noting that the second point calculation quantifies a user's usage of different resource types into points. This quantification method effectively reflects a user's contribution to the platform and helps platform managers better understand user behavior. By assigning point weights to different resources, the platform can guide users to use specific resource types. For example, if you want users to use more videos than text, you can assign a higher point weight to videos.

[0084] Specifically, the points weight can be determined based on: platform incentive strategies. 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 the weight according to the 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; balancing user behavior, the weight setting should take into account the diversity of users to avoid excessive concentration on a certain resource. The weight setting can promote balanced use of different types of resources by users.

[0085] In step S15, a second traffic calculation is performed based on the second resource usage data to obtain a second traffic usage value.

[0086] Preferably, performing a second traffic calculation based on the second resource usage data to obtain a second traffic usage value includes:

[0087] The second flow calculation formula is:

[0088]

[0089] Where, 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.

[0090] 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 deriving an average value based on the data as the traffic conversion coefficient; experiments and A / B testing, by setting different traffic conversion coefficients for different user groups, observing 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, thereby determining 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, these differences should be considered and set reasonably.

[0091] 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.

[0092] Specifically, the main purpose of this step is to control data usage, 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 and images) under the incentive of points, resulting in excessive resource consumption or incurring additional costs. By setting a second data usage threshold, the system can control daily data usage within a reasonable range and avoid excessive resource use. This also prevents malicious point-scaling. Some users may use resources frequently to obtain more points, even using programs to simulate usage. This threshold effectively prevents such abuse, ensures the fairness and rationality of point allocation, and reduces the risk of "point-scaling" or "data abuse" in the system. It also improves user experience. Setting a data usage threshold can guide users to use resources rationally and avoid unnecessary behavior caused by unreasonable point incentives. This improves user experience and encourages more efficient and valuable resource utilization. By optimizing data usage limits, the platform can reduce network congestion and service delays caused by excessive resource consumption, improving the overall user experience. Limiting the daily point allocation limit also provides a more balanced and controllable user experience.

[0093] Preferably, the step of awarding points based on the preset points awarding conditions and according to the first awarding points and the second awarding points includes:

[0094] When the first traffic usage value is equal to a first preset traffic threshold, first gift points are awarded;

[0095] When the first traffic usage value is greater than a first preset traffic threshold, second gift points are awarded based on a set time interval.

[0096] Specifically, this bonus mechanism means that when a user's data usage reaches a certain standard (a first preset data usage threshold), they can earn points as a reward. This encourages users to meet this standard without incentivizing excessive consumption. The first bonus points are relatively simple to award, based on whether the user's data usage meets the threshold and without any time interval. Once a user's data usage exceeds the threshold, by awarding points at set intervals, the platform can avoid the rapid depletion of resources caused by a one-time bonus, while also incentivizing continued resource usage. This bonus rule is suitable for scenarios where the platform expects users to continue active usage after reaching the threshold. It also helps manage system load and avoid incentive imbalances caused by awarding too many points at once. In general, the bonus point rule in this invention consists of two phases. In the first phase, when a user reaches the threshold, the first bonus points are immediately awarded, ensuring that the user meets the platform's preset minimum usage standard to receive the bonus. In the second phase, after the user exceeds the threshold, the second bonus points are continuously awarded at set intervals, encouraging users to maintain active usage above the minimum standard while also controlling the frequency and magnitude of bonus points.

[0097] It's worth noting that the interval setting affects the pace of point rewards and user activity. This can be determined in several ways: Analyze user activity time and determine the optimal interval by analyzing user activity during different time periods. For example, if a user's average active time is two hours, set the interval to 30 minutes to one hour to ensure that points are awarded during the majority of their active time. Regarding product goals and user experience, if you expect frequent user interaction within a short period of time, you can shorten the interval, such as awarding points every 15 or 30 minutes. Conversely, if you expect users to use the app for longer periods, you can set a longer interval, such as one hour or longer. Too short an interval may lead to frequent and annoying point reminders, while too long an interval may reduce user interest in earning points. Therefore, a balance needs to be struck based on the specific user experience goals of the product. Regarding system load and cost control, in high-concurrency scenarios, the interval can be extended to avoid excessive system pressure. For example, during peak user traffic hours (such as 8:00 PM to 10:00 PM), the interval can be extended to alleviate load. Regarding adaptive intervals, for highly active users, the system can shorten the interval to increase stickiness; for less active users, the interval can be extended to maintain long-term usage.

[0098] The following describes the working process of the present invention using a relatively common scenario as an example. The working process is as follows:

[0099] When using their mobile phones daily, users perform various activities such as browsing the web, sending text messages, watching videos, viewing pictures, etc. The system uses the data acquisition module to monitor and record the user's resource usage data in real time, including data usage, text usage, video usage, and picture usage.

[0100] When a user uses data, the system classifies data usage as pre-threshold data (first resource usage data) based on a preset threshold. The system uses a first points calculation module to calculate the user's first bonus points based on the first resource usage data and a preset first weighting coefficient for points. Simultaneously, the first traffic calculation module calculates the user's first traffic usage value based on the first resource usage data and the traffic conversion coefficient.

[0101] If the user's first traffic usage exceeds the preset first traffic threshold, the system enters the second phase. The system begins recording traffic consumption after the threshold (second resource usage data) and uses the second points calculation module to calculate the user's second bonus points based on the second resource usage data and the second weighting coefficient for points. Simultaneously, 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.

[0102] If the user's second traffic usage value exceeds the second preset traffic threshold, the system will stop the point awarding for that day through the point awarding module. If the user's second traffic usage value does not exceed the second preset traffic threshold, the system will award points based on the user's first and second gift points through the point awarding module based on the preset point awarding conditions.

[0103] When users reach or exceed traffic thresholds, they are rewarded with corresponding points, which encourages them to continue using the service and can redeem rewards in the points mall. The system uses reasonable settings for traffic conversion coefficients and point weighting coefficients to ensure that point distribution matches actual user usage, avoiding the problem of giving out too many or too few points.

[0104] In summary, the present invention provides a method for giving 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 points calculation based on the first resource usage data to obtain first gift points; 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 points calculation to obtain a second gift points; 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 points gifting for the day, and based on the preset points gifting conditions, performing points gifting based on the first gift points and the second gift points.

[0105] In the present invention, the method can achieve reasonable point allocation by obtaining user resource usage data and combining it with preset traffic thresholds and point calculation rules. When a user's traffic usage reaches the preset threshold, the corresponding points calculation is performed, thereby dynamically adjusting the number of points allocated. The beneficial effect is that the method can avoid excessive or insufficient point allocation, ensuring that point allocation is consistent with the user's actual usage.

[0106] Reference Figure 2 The second embodiment of the present invention provides a mobile data points donation system based on big data analysis, including:

[0107] Data acquisition module, used to obtain user resource usage data;

[0108] A first points calculation module, configured to perform a first points calculation based on the first resource usage data to obtain first gift points;

[0109] a first flow calculation module, configured to perform a first flow calculation based on the first resource usage data to obtain a first flow usage value;

[0110] A second points calculation module, configured to perform a second points calculation to obtain second bonus points when the first traffic usage value is greater than a first preset traffic threshold;

[0111] A second flow calculation module, configured to perform a second flow calculation based on the second resource usage data to obtain a second flow usage value;

[0112] 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 based on the preset points granting conditions according to the first granting points and the second granting points.

[0113] In an optional implementation, the first integral calculation module is specifically configured to:

[0114] Perform a first integral calculation, where the calculation formula for the first integral calculation is:

[0115]

[0116] Where, 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.

[0117] In an optional embodiment, the first flow calculation module is specifically configured to:

[0118] Perform a first flow calculation, the calculation formula for the first flow calculation is:

[0119]

[0120] Where, 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.

[0121] In an optional implementation, the second integral calculation module is specifically configured to:

[0122] Perform a second integral calculation, the calculation formula for the second integral calculation is:

[0123]

[0124] Where, Points are awarded for the second; 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.

[0125] In an optional implementation, the second flow calculation module is specifically configured to:

[0126] Perform a second flow calculation, the second flow calculation formula is:

[0127]

[0128] Where, 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.

[0129] In an optional implementation, the points granting module is specifically configured to:

[0130] When the first traffic usage value is equal to a first preset traffic threshold, first gift points are awarded;

[0131] When the first traffic usage value is greater than a first preset traffic threshold, second gift points are awarded based on a set time interval.

[0132] It should be noted that the mobile phone traffic points giving system based on big data analysis provided in an 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, so they will not be repeated here.

[0133] 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 away 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 away 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.

[0134] 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, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0135] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0136] 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), 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. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0137] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0138] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0139] It should be noted that the device embodiments described above are merely illustrative, 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 across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the 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 can understand and implement the present invention without inventive effort.

[0140] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for giving mobile data points based on big data analysis, characterized in that: include: Acquire resource usage data of the user; wherein the resource usage data includes first resource usage data and second resource usage data; Performing a first points calculation based on the first resource usage data to obtain first gift points; Performing a first flow calculation based on 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, performing a second points calculation to obtain second gift points; Performing a second flow calculation based on 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, the points granting is carried out according to the first gift points and the second gift points; The step of performing a first point calculation based on the first resource usage data to obtain first gift points includes: The calculation formula for the first integral calculation is: Where, 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.

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 image 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 performing a first traffic calculation based on the first resource usage data to obtain a first traffic usage value includes: The calculation formula for the first flow calculation is: Where, 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.

4. 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 second gift points includes: The calculation formula for the second integral calculation is: Where, Points are awarded for the second; 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.

5. The method for giving mobile data points based on big data analysis according to claim 1 is characterized in that: The performing a second traffic calculation based on the second resource usage data to obtain a second traffic usage value includes: The second flow calculation formula is: Where, 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.

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

7. A mobile phone traffic points gifting system based on big data analysis, characterized in that: A method for granting mobile data points based on big data analysis according to any one of claims 1 to 6, comprising: A data acquisition module, configured 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 based on the first resource usage data to obtain first gift points; a first flow calculation module, configured to perform a first flow calculation based on the first resource usage data to obtain a first flow usage value; A second points calculation module, configured to perform a second points calculation to obtain second bonus points when the first traffic usage value is greater than a first preset traffic threshold; A second flow calculation module, configured to perform a second flow calculation based on 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 based on the preset points granting conditions according to the first granting points and the second granting points.

8. 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. 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 6.

9. 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 gifting method based on big data analysis as described in any one of claims 1 to 6.

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