A mobile terminal flow reminder method and system

Abstract

The present invention discloses a mobile terminal flow reminder method and system, which relates to the field of data communication technology. After the flow consumption prediction mechanism is enabled, the predicted value of the flow usage in the current sub-stage is obtained. If it exceeds the preset energy consumption quota threshold, the reminder density is generated by the state data of the received abnormal reminder instruction. When the reminder density exceeds the expectation, the corresponding flow quota is determined for each application software of the mobile terminal; the flow quota of the corresponding application software is adjusted or the network speed is automatically adjusted according to the obtained quota value, and after the upper limit value of the network speed is constrained by the matching degree, the target application is screened out in the application software of the mobile terminal, and the information push strategy in the current recording period of the application software is adjusted. By reallocating the flow quota, the usable time is extended under the condition of cooperating with the speed limit, which has the effect of saving flow.

Description

Technical Field

[0001] The present invention relates to the field of data communication technology, and in particular to a method and system for reminding a mobile terminal of traffic flow. Background Art

[0002] The data usage reminder function of mobile terminals is a practical tool designed to improve the efficiency of user data usage management and avoid unnecessary expenses. This function monitors the user's data usage in real time and reminds the user in a timely manner through push notifications, SMS reminders, in-app warnings, etc. when the user approaches or reaches the preset data usage limit. This not only helps users to keep track of their data consumption at any time, but also effectively avoids additional fees caused by excessive data usage, allowing users to manage their data usage more flexibly and improve the convenience of data management and cost control capabilities.

[0003] In the Chinese invention patent with application publication number CN117793783A, a 5G mobile phone data traffic monitoring method and system are disclosed, including: collecting user traffic data, establishing a traffic reference library, an APP value library, and an APP traffic and network request relationship library; obtaining the remaining traffic and date information of this month, calculating the required traffic and usage coefficient; real-time monitoring of the usage coefficient, and reminding the user if it changes; if the usage coefficient does not exceed the standard and meets the preset conditions, ask the user whether to turn on the traffic limit; if the user agrees, the APP is subjected to flow restriction. The daily traffic demand is calculated based on the remaining days of this month. Secondly, the daily traffic demand is divided into two categories: weekdays and holidays, so as to more carefully reflect the actual traffic demand of users in different time periods; by comparing the calculated daily traffic demand with the remaining traffic, it is possible to intuitively judge whether the remaining traffic is sufficient, and remind the user when necessary, eliminating the trouble of users having to make their own judgments.

[0004] For example, when smart devices and mobile terminals such as mobile phones, tablets, and even smart watches use data traffic to connect to and access the Internet, if you need to consider traffic charges or data traffic is limited, you usually need to set a traffic reminder for the smart device. When the data traffic of the smart device is about to run out, the user will be reminded and the user will take appropriate action.

[0005] In the existing traffic reminder methods, the traffic usage of smart devices is usually monitored and predicted in real time. After the user sets the traffic usage upper limit, if the traffic usage data is about to reach the upper limit, the user is notified, or the data connection is directly disconnected. Although this reminder method is relatively direct, the traffic usage is still completely determined by the user's actual use. At the same time, since the existing reminder methods are all one-time static reminders, the reminder effect is also poor. After the reminder is completed, due to the lack of active intervention in the smart device, it has no additional effect except for the reminder effect. When the data traffic is about to be consumed, it cannot effectively extend the usage time of the smart device.

[0006] To this end, the present invention provides a mobile terminal traffic reminder method and system. Summary of the invention

[0007] 1. Technical issues to be solved

[0008] In view of the deficiencies of the prior art, the present invention provides a method and system for reminding traffic of a mobile terminal. After the traffic consumption prediction mechanism is enabled, the predicted value of the traffic usage in the current sub-stage is obtained. If it exceeds the preset energy consumption quota threshold, the reminder density is generated by the status data of the received abnormal reminder instruction. When the reminder density exceeds the expectation, the corresponding traffic quota is determined for each application software of the mobile terminal; the traffic quota of the corresponding application software is adjusted or the network speed is automatically adjusted according to the obtained quota value, and after the upper limit value of the network speed is constrained by the matching degree, the target application is screened out in the application software of the mobile terminal, and the information push strategy within the current recording period of the application software is adjusted. By reallocating the traffic quota, the usable time is extended under the condition of cooperating with the speed limit, thereby solving the technical problems recorded in the background technology.

[0009] (II) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] A mobile terminal traffic reminder method, comprising: generating a matching degree from similarity data between browsing content and preference information, arranging a plurality of consecutive matching degrees along a time axis, and issuing a consumption reminder instruction to an external party if the matching degree is on an upward trend;

[0012] If the traffic consumption speed of the mobile terminal exceeds the speed threshold, the traffic consumption prediction mechanism is enabled to obtain the predicted value of the traffic usage in the current sub-phase. If it exceeds the preset energy consumption quota threshold, an abnormal reminder instruction is issued to the outside;

[0013] Generate a reminder density Mpj from the state data of the received abnormal reminder instruction, and when the reminder density Mpj exceeds expectations, determine a corresponding traffic quota for each application software of the mobile terminal;

[0014] Generate a quota value Poz based on the data consumption and usage time data of each application software, and adjust the traffic quota of the corresponding application software or automatically adjust the network speed based on the obtained quota value Poz;

[0015] After constraining the upper limit of the network speed with the matching degree, the target application is screened out from the application software of the mobile terminal, and the information push strategy within the current recording period of the application software is adjusted.

[0016] Furthermore, the preference information and browsing content data of the user when browsing information are collected, and the similarity data between the browsing content and the preference information are obtained through similarity analysis, and the matching degree Ppm is generated from the similarity data in the following manner:

[0017]

[0018] Where: s(t) is the similarity data at time t, σ is the standard deviation of similarity, w(t) is the time weight function, w(t) = e -λt , e = 2.713, the value of λ falls into [0,1], W is the sum of weight functions; T is the length of the sub-period, k is the constant coefficient that adjusts the influence of the cumulative factor, and max(σ) is the maximum possible value of the standard deviation of the similarity.

[0019] Furthermore, after receiving the consumption reminder instruction, the traffic consumption speed in each sub-stage within the recording period is obtained, and the speed threshold of the traffic consumption is limited. If the traffic consumption in the current sub-stage exceeds the speed threshold [Ba, Bb], the trained traffic consumption prediction model is used to predict the traffic usage.

[0020] Furthermore, when the number of abnormal reminder instructions received in the current recording period exceeds expectations, a reminder density Mpj is generated based on the status data of the received abnormal reminder instructions. If the reminder density Mpj exceeds the density threshold, a traffic quota instruction is issued to the outside.

[0021] Furthermore, the method of generating the reminder density Mpj according to the state data of the received abnormal reminder instruction is as follows:

[0022]

[0023] Where: D0 is the reminder density at t = 0, T is the length of the recording period, N is the total number of abnormal reminders, S i is the severity of the i-th abnormality alert, Δt iis the time interval of the ith abnormality reminder, β is the time weighting coefficient, t i is the time when the i-th reminder occurs.

[0024] Furthermore, after receiving the traffic quota instruction, the corresponding traffic quota is determined for each application software of the mobile terminal based on the historical traffic consumption data; when the traffic consumption of the application software approaches or reaches the traffic quota, a limit notification is issued to the outside.

[0025] Furthermore, after receiving the limit notification, the user confirms it, collects the user's data consumption and usage time data on each application software, and generates a data usage information set after aggregation, and generates a corresponding quota value Poz from the data usage information set.

[0026] Furthermore, if the obtained quota value exceeds the quota threshold, a quota scheduling instruction is issued externally;

[0027] If it does not exceed, its usage status needs to be restricted, and the upper limit of network speed can be generated based on historical network speed data. The network speed of the application software can be automatically adjusted so that the maximum network speed does not exceed the upper limit of network speed.

[0028] Furthermore, after receiving the quota scheduling instruction, the usage time of the mobile terminal is used as the scheduling target, and the traffic consumption time is not less than the expected consumption time as the constraint condition. The pre-trained ant colony algorithm is used to schedule and adjust the traffic quota of each application software to obtain the traffic quota of each application software after scheduling.

[0029] Furthermore, the network speed upper limit Hs is constrained by the matching degree within the sub-period. When the user browses the content of interest, the network speed is automatically controlled so that the access speed does not exceed the network speed upper limit. The method is as follows:

[0030]

[0031] Weight coefficient, 0≤α≤1, 0≤β≤1; number of n sub-periods, Ppm i is the matching degree on the ith sub-period, Ppm a is its average value.

[0032] Furthermore, after obtaining the application software with the largest increase in traffic quota, it is used as the target application, and the expected remaining traffic usage time is determined in combination with the traffic consumption speed of the target application, and the remaining time is used as the remaining time;

[0033] After sending a request and obtaining permission from the target application, the information push strategy within the current recording period of the application software is adjusted with the extension of the remaining time as the optimization goal.

[0034] A mobile terminal traffic reminder system includes a matching analysis unit, which generates a matching degree from similarity data between browsing content and preference information, arranges a plurality of consecutive matching degrees along a time axis, and sends a consumption reminder instruction to the outside if the matching degree is on an upward trend;

[0035] The abnormality reminder unit, if the traffic consumption speed of the mobile terminal exceeds the speed threshold, obtains the predicted value of the traffic usage in the current sub-phase after the traffic consumption prediction mechanism is enabled, and if it exceeds the preset energy consumption quota threshold, issues an abnormality reminder instruction to the outside;

[0036] The density analysis unit generates a reminder density Mpj from the state data of the received abnormal reminder instruction, and determines a corresponding traffic quota for each application software of the mobile terminal when the reminder density Mpj exceeds the expectation;

[0037] The traffic quota unit generates a quota value Poz based on the data consumption and usage duration of each application software, and adjusts the traffic quota of the corresponding application software or automatically adjusts the network speed based on the obtained quota value Poz;

[0038] The feedback processing unit constrains the upper limit of the network speed with the matching degree, selects the target application from the application software of the mobile terminal, and adjusts the information push strategy within the current recording period of the application software.

[0039] (III) Beneficial effects

[0040] The present invention provides a mobile terminal flow reminder method and system, which have the following beneficial effects:

[0041] 1. Predict the traffic consumption of mobile terminals in advance based on the changing trend of the matching degree. When using it as feedback, consider the user's interest level and continuous activation status to restrict the corresponding application software.

[0042] 2. Determine whether there is any abnormality in the traffic consumption in the current stage. If there is abnormal traffic consumption, it can be handled in a timely manner to avoid continuous abnormalities and implement preliminary traffic consumption reminders; by enabling the traffic consumption prediction mechanism, the traffic usage status of the current sub-stage is predicted, and on this basis, the consumable traffic of each application software is reallocated.

[0043] 3. By generating the reminder density Mpj from the status data of the abnormal reminder instruction, a secondary reminder mechanism is formed on the basis of the abnormal reminder instruction to avoid users constantly ignoring the primary reminder when receiving it, thereby improving the effectiveness of the reminder.

[0044] 4. After enabling the multi-level alarm mechanism, continue to enable the traffic quota mechanism to allocate quotas for the available traffic for each application on the mobile terminal to avoid excessive traffic consumption by some applications, which makes it difficult to continue using other applications, thereby ensuring the overall usage status of the mobile terminal.

[0045] 5. Based on the quota value Poz, a comprehensive assessment can be made on the traffic usage of each application software on the mobile terminal to determine whether some applications consume too much traffic, and the traffic quota can be adjusted to make it more reasonable.

[0046] 6. By limiting the network speed of some application software, when multiple traffic reminders have been issued continuously and the traffic of the mobile terminal is close to being consumed, it can save traffic; by reallocating traffic quotas, under the condition of speed limit, the available time of application software can be extended, thus saving traffic.

[0047] 7. Use the matching degree generated by the user's interest as feedback to constrain the upper limit of the network speed, so that the upper limit of the network speed is more adaptable to the actual usage scenario, so that the user's interest is limited when accessing the pushed content of interest, and the data consumption of the mobile terminal is reduced. After issuing continuous data traffic reminders, the user's data traffic content is used as the starting point to reduce the use intensity of application software or application software, thereby increasing the effectiveness of data traffic reminders. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for reminding a mobile terminal flow rate according to the present invention;

[0049] Figure 2 It is a structural schematic diagram of the mobile terminal flow reminder system of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] See also Figure 1 The present invention provides a mobile terminal flow reminder method, comprising:

[0052] Step 1: Generate a matching degree from the similarity data between the browsing content and the preference information, and arrange multiple consecutive matching degrees along the time axis. If the matching degree is on an upward trend, send a consumption reminder instruction to the outside;

[0053] The step 1 includes the following contents:

[0054] Step 101: collecting and recording the user's preference information and browsing content data during information browsing within a preset recording period, obtaining similarity data between the browsing content and the preference information through similarity analysis, and aggregating the similarity data at multiple consecutive time nodes to generate an information matching data set;

[0055] When in use, the obtained similarity data can be used to make a preliminary judgment on the current user's interest in browsing information. If the interest level is higher, the browsing time may be longer;

[0056] Step 102: divide the recording period into several sub-periods of equal length, generate the matching degree Ppm in the sub-period from the continuous similarity data in the information matching data set, and mark the sub-period with the matching degree, in the following manner:

[0057]

[0058] Where: s(t) is the similarity data at time t, the value falls into [0,1], σ is the standard deviation of similarity, w(t) is the time weight function, w(t) = e -λt , e = 2.713, the value of λ falls within [0,1], W is the sum of weight functions; T is the length of the sub-period, l is the constant coefficient that adjusts the influence of the cumulative factor, and its value falls within [0,1], max(σ) is the maximum possible value of the standard deviation of the similarity;

[0059] Pre-set matching thresholds based on historical data and expectations for information recommendation browsing and data traffic consumption management;

[0060] If the obtained matching degree Ppm exceeds the matching threshold, it means that during the recording period, the user has been browsing the content that he is very interested in after activating the application software. In this case, the consumption of data traffic will further increase, especially when the matching degree is on an upward trend after arranging multiple consecutive matching degrees along the time axis. The matching degree between the browsing content and the user's interest points gradually increases, and the possibility of a rapid increase in data traffic consumption will be greater. At this time, a consumption reminder instruction is issued to the outside;

[0061] When using, combine the contents in steps 101 and 102:

[0062] After comprehensively evaluating the user's interest in the pushed content using the generated matching degree Ppm, the traffic consumption of the mobile terminal is predicted in advance based on the changing trend of the matching degree. When it is used as feedback, the corresponding application software can be restricted considering the user's interest level and continuous activation status.

[0063] In the existing traffic reminder methods, the traffic usage of smart devices is usually monitored and predicted in real time. After the user sets the traffic usage upper limit, if the traffic usage data is about to reach the upper limit, the user is notified, or the data connection is directly disconnected. Although this reminder method is relatively direct, the traffic usage is still completely determined by the user's actual use. At the same time, since the existing reminder methods are all one-time static reminders, the reminder effect is also poor. After the reminder is completed, due to the lack of active intervention in the smart device, it has no additional effect except for the reminder effect. When the data traffic is about to be consumed, it cannot effectively extend the usage time of the smart device.

[0064] Step 2: If the traffic consumption speed of the mobile terminal exceeds the speed threshold, the traffic consumption prediction mechanism is enabled to obtain the predicted value of the traffic usage in the current sub-phase. If it exceeds the preset energy consumption quota threshold, an abnormal reminder instruction is issued to the outside;

[0065] The step 2 includes the following contents:

[0066] Step 201: After receiving the consumption reminder instruction, obtain the flow consumption speed in each sub-stage within the recording period, and define the flow consumption speed threshold [Ba, Bb] in the following manner:

[0067]

[0068] Among them, Xoj max is the maximum value of the traffic consumption speed, Xoj min is the minimum value of traffic consumption speed, Xoj a is the average value of traffic consumption speed, Xoj i is the traffic consumption rate in the i-th sub-stage;

[0069] According to the relationship between the traffic consumption speed and the speed threshold, the abnormal degree of traffic consumption and traffic usage is judged. If the traffic consumption in the current sub-stage exceeds the speed threshold [Ba, Bb], it indicates that there is abnormal traffic consumption in the current sub-stage. At this time, an abnormal consumption reminder is issued to the outside;

[0070] When in use, by constructing a speed threshold [Ba, Bb] based on historical data, it is possible to determine whether there is an abnormality in the current stage of traffic consumption. When there is abnormal traffic consumption, it can be processed in a timely manner to avoid continuous abnormalities and achieve preliminary traffic consumption reminders;

[0071] Step 202: after recording and collecting the historical traffic consumption data and information traffic data in the most recent recording periods, the data are summarized as a traffic usage data set; part of the data is extracted from the traffic usage data set as sample data, and a neural convolution network is trained by the sample data to obtain a trained traffic consumption prediction model;

[0072] After receiving the abnormal consumption reminder, the traffic consumption prediction mechanism is enabled, that is, the traffic consumption is predicted using the trained traffic consumption prediction model to obtain the predicted value of the traffic usage in the current sub-stage;

[0073] If the predicted value of traffic usage exceeds the preset energy consumption quota threshold, it means that the traffic consumption status may be abnormal. At this time, an abnormal reminder instruction is issued to the outside;

[0074] When using, combine the contents in steps 201 and 202:

[0075] When it is determined that there is abnormal traffic consumption in the current stage, the traffic consumption prediction mechanism is enabled to predict the traffic usage status of the current sub-stage, and the traffic usage status of each application software is understood in advance. On this basis, the consumable traffic of each application software can be reallocated.

[0076] Step 3: Generate a reminder density Mpj from the state data of the received abnormal reminder instruction, and when the reminder density Mpj exceeds expectations, determine a corresponding traffic quota for each application software of the mobile terminal;

[0077] The step three includes the following contents:

[0078] Step 301: When the number of abnormal reminder instructions received in the current recording period exceeds the expected number, a reminder density Mpj is generated according to the state data of the received abnormal reminder instructions in the following manner:

[0079]

[0080] Where: D0 is the reminder density at t = 0, e = 2.713, the value of λ falls within [0,1], T is the length of the recording period, N is the total number of abnormal reminders, S i is the severity of the i-th abnormality alert, Δt i is the time interval of the i-th abnormality reminder, β is the time weighting coefficient, and its value falls within [0,1]; t i is the time when the i-th reminder occurs;

[0081] Pre-set density thresholds based on historical data and management expectations for abnormal traffic consumption;

[0082] If the obtained reminder density Mpj exceeds the density threshold, it means that the frequency of abnormal traffic consumption is high in the current stage, and the current traffic usage and consumption status needs to be adjusted. At this time, a traffic quota instruction is issued to the outside;

[0083] When in use, by generating reminder density Mpj from the status data of abnormal reminder instructions, a secondary reminder mechanism can be formed based on the abnormal reminder instructions to prevent users from constantly ignoring the primary reminder when receiving it, thereby causing users to miss the traffic reminder information, which in turn causes abnormal or excessive traffic consumption, thereby improving the effectiveness of the reminder;

[0084] Step 302: after receiving the traffic quota instruction, determine the corresponding traffic quota for each application software of the mobile terminal according to the historical traffic consumption data;

[0085] When the application is enabled, the available time is displayed in real time based on its traffic consumption speed and traffic quota. When the traffic consumption of the current application software approaches or reaches the traffic quota, a limit notification is issued to the outside world.

[0086] When used, combine the contents in steps 301 and 302:

[0087] When the traffic usage status of the mobile terminal is already in an abnormal state, the traffic quota mechanism will continue to be enabled after the multi-level alarm mechanism is enabled to allocate quotas for the available traffic for each application on the mobile terminal, so as to avoid excessive traffic consumption by some applications, which makes it difficult for the remaining applications to continue to use, thereby safeguarding the overall usage status of the mobile terminal.

[0088] Step 4: Generate a quota value Poz based on the data consumption and usage duration data of each application software, and adjust the traffic quota of the corresponding application software or automatically adjust the network speed based on the obtained quota value Poz;

[0089] The step 4 includes the following contents:

[0090] Step 401: After receiving the limit notification, the user confirms it. After the user confirms it, the data consumption and usage duration data of the user on each application software are collected and summarized to generate a data usage information set;

[0091] The corresponding quota value Poz is generated from the data usage information set, and the corresponding application software is marked in the following way: linear normalization is performed on the data consumption Co and the usage time Xt, and the corresponding data value is mapped to the interval [0,1] according to the following method:

[0092]

[0093] Weight coefficient, 0≤F1≤1, 0≤F2≤1, and F1+F2=1, i=1, 2, …n, n is the number of application software, Co i is the data consumption of the ith application software, Xt i is the usage time of the i-th application software;

[0094] When in use, the quota value Poz is constructed from the data usage information of the application software. Based on the quota value Poz, a comprehensive evaluation can be made on the traffic usage of each application software of the mobile terminal to determine whether some application software consumes too much traffic. In this case, the traffic quota can be adjusted to make the traffic quota more reasonable;

[0095] Step 402: pre-set a quota threshold based on historical data and expected traffic consumption management of application software; if the obtained quota value exceeds the quota threshold, issue a quota scheduling instruction to the outside; if it does not exceed, it is necessary to restrict its usage status, and at this time, issue a network speed adjustment instruction to the outside;

[0096] After receiving the network speed adjustment instruction, the network speed upper limit value is generated based on the historical network speed data. When the application software is enabled, the network speed of the application software is automatically adjusted so that the maximum network speed does not exceed the network speed upper limit value;

[0097] When in use, the maximum network speed is limited to delay the usage time. Different from the rescheduling and optimization of the traffic usage of each application software, by limiting the network speed of some application software, it can save traffic when multiple traffic reminders have been issued continuously and the traffic of the mobile terminal is almost consumed.

[0098] Step 403: after receiving the quota scheduling instruction, the usage time of the mobile terminal is used as the scheduling target, and the traffic consumption time is not less than the expected consumption time as the constraint condition, and the pre-trained ant colony algorithm is used to schedule and adjust the traffic quota of each application software to obtain the scheduled traffic quota of each application software;

[0099] When using, combine the contents in steps 401 to 403:

[0100] Taking into account the different usage requirements and intensities of different applications, when the traffic quota of some applications is close to being used up and there is not much remaining traffic on the mobile terminal, the traffic quota can be reallocated and the available time of the application can be extended under the condition of speed limit, thereby saving traffic.

[0101] Step 5: After constraining the network speed upper limit value by the matching degree, select the target application from the application software of the mobile terminal, and adjust the information push strategy within the current recording period of the application software;

[0102] The step five includes the following contents:

[0103] Step 501: Constrain the network speed upper limit value Hs by the matching degree in the sub-period, so as to achieve real-time adjustment of the network speed upper limit value Hs, in the following manner:

[0104]

[0105] Weight coefficient, 0≤α≤1, 0≤β≤1; number of n sub-periods, Ppm i is the matching degree on the ith sub-period, Ppm a is its average value.

[0106] When users browse content they are interested in, the network speed is automatically controlled so that the access speed does not exceed the upper limit of the network speed.

[0107] Step 502: After obtaining the application software with the largest increase in traffic quota, use it as the target application, and determine the expected remaining traffic usage time in combination with the traffic consumption speed of the target application, and use it as the remaining time;

[0108] After making a request and obtaining permission from the target application, the information push strategy within the current recording period of the application software is adjusted and optimized with the extension of the remaining time as the optimization goal, so as to reduce the user's interest in the pushed content. By reducing the user's interest in the pushed content, the user's continuous access time is reduced;

[0109] When using, combine the contents in steps 501 and 502:

[0110] After the application software or target application completes traffic quota optimization or speed limit setting, if it is still in use, the matching degree generated by the user's interest is used as feedback to constrain the network speed limit value. This can make the network speed limit value more adaptable to the actual usage scenario, limit the user's interest when accessing the pushed content of interest, and reduce the traffic consumption of the mobile terminal. Therefore, after issuing continuous traffic reminders, the user's traffic content is used as the starting point to reduce the usage intensity of the application software or application software and increase the effectiveness of the traffic reminder.

[0111] See also Figure 2 The present invention provides a mobile terminal flow reminder system, comprising:

[0112] The matching analysis unit generates a matching degree from the similarity data between the browsing content and the preference information, arranges multiple consecutive matching degrees along the time axis, and issues a consumption reminder instruction to the outside if the matching degree is on an upward trend;

[0113] The abnormality reminder unit, if the traffic consumption speed of the mobile terminal exceeds the speed threshold, obtains the predicted value of the traffic usage in the current sub-phase after the traffic consumption prediction mechanism is enabled, and if it exceeds the preset energy consumption quota threshold, issues an abnormality reminder instruction to the outside;

[0114] The density analysis unit generates a reminder density Mpj from the state data of the received abnormal reminder instruction, and determines a corresponding traffic quota for each application software of the mobile terminal when the reminder density Mpj exceeds the expectation;

[0115] The traffic quota unit generates a quota value Poz based on the data consumption and usage duration of each application software, and adjusts the traffic quota of the corresponding application software or automatically adjusts the network speed based on the obtained quota value Poz;

[0116] The feedback processing unit constrains the upper limit of the network speed with the matching degree, selects the target application from the application software of the mobile terminal, and adjusts the information push strategy within the current recording period of the application software.

[0117] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0119] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A mobile terminal traffic reminder method, characterized in that: include, Generate matching degree from the similarity data between the browsing content and the preference information, arrange multiple consecutive matching degrees along the time axis, and send consumption reminder instructions to the outside if the matching degree is on an upward trend; If the traffic consumption speed of the mobile terminal exceeds the speed threshold, the traffic consumption prediction mechanism is enabled to obtain the predicted value of the traffic usage in the current sub-phase. If it exceeds the preset energy consumption quota threshold, an abnormal reminder instruction is issued to the outside; Generate reminder density from the status data of the received abnormal reminder instruction , in the reminder density When it exceeds expectations, determine the corresponding traffic quota for each application software of the mobile terminal; Generate quota values ​​based on data consumption and usage duration of each application , based on the obtained quota value Adjust the traffic quota of the corresponding application software or automatically adjust the network speed; After constraining the upper limit of the network speed with the matching degree, the target application is screened out from the application software of the mobile terminal, and the information push strategy within the current recording period of the application software is adjusted.

2. A mobile terminal traffic reminder method according to claim 1, characterized in that: Collect the user's preference information and browsing content data when browsing information, obtain the similarity data between the browsing content and the preference information through similarity analysis, and generate the matching degree from the similarity data , as follows: ； Where: For time The similarity data at is the standard deviation of similarity, is the time weight function, , The value of falls into , is the sum of weight functions; is the length of the sub-period, To adjust the constant coefficient of the cumulative factor, is the maximum possible value of the standard deviation of the similarity.

3. A mobile terminal traffic reminder method according to claim 2, characterized in that: After receiving the consumption reminder instruction, obtain the traffic consumption speed in each sub-stage within the recording period, set the speed threshold of traffic consumption, and if the traffic consumption in the current sub-stage exceeds the speed threshold ,The traffic usage is predicted using the trained traffic consumption prediction model.

4. A mobile terminal traffic reminder method according to claim 3, characterized in that: When the number of abnormal reminder instructions received in the current recording period exceeds the expected number, the reminder density is generated based on the status data of the received abnormal reminder instructions. , if the reminder density When the density threshold is exceeded, a traffic quota instruction is issued to the outside.

5. A mobile terminal traffic reminder method according to claim 4, characterized in that: Generate reminder density based on the status data of the received abnormal reminder instruction The way is as follows: ； Where: for The reminder intensity is the length of the recording period, is the total number of abnormal reminders, is the severity of the i-th abnormal reminder, is the time interval for the i-th abnormal reminder, is the time weighting coefficient, is the time when the i-th reminder occurs, , The value of falls into , is the time weighting coefficient, and its value falls into .

6. A mobile terminal traffic reminder method according to claim 5, characterized in that: After receiving the traffic quota instruction, the corresponding traffic quota is determined for each application software of the mobile terminal based on the historical traffic consumption data. When the traffic consumption of the application software approaches or reaches the traffic quota, a limit notification is issued to the outside.

7. A mobile terminal traffic reminder method according to claim 6, characterized in that: After receiving the limit notification, the user confirms it, collects the user's data consumption and usage time data on each application software, and generates a data usage information set after aggregation. The corresponding quota value is generated from the data usage information set .

8. A mobile terminal traffic reminder method according to claim 7, characterized in that: If the quota value is obtained If the quota threshold is exceeded, a quota scheduling instruction is issued externally; If it does not exceed, its usage status needs to be restricted, and the upper limit of network speed can be generated based on historical network speed data. The network speed of the application software can be automatically adjusted so that the maximum network speed does not exceed the upper limit of network speed.

9. A mobile terminal traffic reminder method according to claim 8, characterized in that: After receiving the quota scheduling instruction, the usage time of the mobile terminal is used as the scheduling target, and the traffic consumption time is not less than the expected consumption time as the constraint condition. The pre-trained ant colony algorithm is used to schedule and adjust the traffic quota of each application software to obtain the scheduled traffic quota of each application software.

10. A mobile terminal traffic reminder method according to claim 9, characterized in that: The upper limit of network speed is calculated based on the matching degree within the sub-period To constrain, when users browse content of interest, the network speed is automatically controlled so that the access speed does not exceed the upper limit of the network speed. The method is as follows: ； Weight coefficient, , ; The number of sub-periods, It is The matching degree on the sub-period is is its average value.

11. A mobile terminal traffic reminder method according to claim 10, characterized in that: After obtaining the application software with the largest increase in traffic quota, use it as the target application, and determine the expected remaining traffic usage time based on the traffic consumption speed of the target application; After sending a request and obtaining permission from the target application, the information push strategy within the current recording period of the application software is adjusted with the extension of the remaining time as the optimization goal.

12. A mobile terminal traffic reminder system, characterized in that: include, The matching analysis unit generates a matching degree from the similarity data between the browsing content and the preference information, arranges multiple consecutive matching degrees along the time axis, and issues a consumption reminder instruction to the outside if the matching degree is on an upward trend; The abnormality reminder unit, if the traffic consumption speed of the mobile terminal exceeds the speed threshold, obtains the predicted value of the traffic usage in the current sub-phase after the traffic consumption prediction mechanism is enabled, and if it exceeds the preset energy consumption quota threshold, issues an abnormality reminder instruction to the outside; The density analysis unit generates a reminder density from the status data of the received abnormal reminder instruction , in the reminder density When it exceeds expectations, determine the corresponding traffic quota for each application software of the mobile terminal; Traffic quota unit, which generates quota values ​​based on the data consumption and usage duration of each application software , based on the obtained quota value Adjust the traffic quota of the corresponding application software or automatically adjust the network speed; The feedback processing unit constrains the upper limit of the network speed with the matching degree, selects the target application from the application software of the mobile terminal, and adjusts the information push strategy within the current recording period of the application software.

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