Data preloading method and apparatus, communication device, and readable storage medium

Abstract

The application relates to a data preloading method and device, a communication device and a computer readable storage medium. The method comprises the following steps: obtaining a current area of a terminal device; obtaining mobile prediction data by using a mobile trend prediction model to make a prediction according to the current area; combining the mobile prediction data, data stored in a network database and data stored in a user database to determine a maximum download data volume and a demand data volume of the terminal device during movement; obtaining a current setting threshold of the terminal device; comparing the current setting threshold, the maximum download data volume and the demand data volume to obtain a comparison result; and adjusting a network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration. The method can dynamically adjust the network configuration according to actual conditions, has foresight, improves the preloading efficiency of the terminal device and ensures that a user can normally use the terminal device in a low network quality area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, and in particular to a data preloading method and device, a communication device, and a computer readable storage medium. BACKGROUND

[0002] It is a common activity for people to watch videos, read literary works, and listen to music using mobile devices during daily travel. With the development of network technology, users can control mobile terminals to select various ways to access the network, so as to watch videos or browse web pages through mobile terminals. Among them, users can select operator networks to access the network.

[0003] Currently, the speed of loading data by an APP (Application) in a mobile terminal is directly related to the quality of an operator network (for example, poor quality of an operator network leads to slow speed of loading data by an APP), and if the speed of browsing data by a user is faster than the speed of loading data by an APP, data loading will be stalled, affecting the user experience.

[0004] To solve this problem, the related technology usually uses a preloading method, in which a mobile terminal preloads part of data to avoid the stall phenomenon caused by network fluctuations. However, the current preloading method generally preloads a fixed amount of data (for example, three pieces of short video data are preloaded), which cannot be adjusted according to actual conditions. In this way, the network configuration lacks foresight and cannot actively improve the network quality; the flexibility of preloading data by a mobile terminal is poor, and if the mobile terminal is in a poor network quality environment for a long time, data loading will still be stalled. SUMMARY

[0005] Therefore, it is necessary to provide a data preloading method, device, communication device, and computer readable storage medium to dynamically adjust network configuration according to actual conditions, thereby improving the preloading efficiency of a terminal device.

[0006] In a first aspect, the present application provides a data preloading method, comprising:

[0007] obtaining a current area of a terminal device;

[0008] using a mobile trend prediction model to predict the current area to obtain mobile prediction data;

[0009] determining a maximum download data amount of the terminal device in a mobile process according to the mobile prediction data and data stored in a network database;

[0010] determining a demand data amount of the terminal device in the mobile process according to the mobile prediction data, the data stored in the network database, and data stored in a user database;

[0011] obtaining a current setting threshold of the terminal device;

[0012] comparing the current setting threshold, the maximum download data amount and the demand data amount to obtain a comparison result;

[0013] adjusting a network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0014] In one of the embodiments, the maximum download data amount of the terminal device in the moving process is determined according to the moving prediction data and the data stored in the network database, comprising:

[0015] determining the moving speed and a plurality of target areas according to the moving prediction data;

[0016] querying the network bandwidth and the moving distance of each target area from the network database;

[0017] determining the target time length of passing through each target area according to the moving speed and the moving distance;

[0018] determining the maximum download data amount of the terminal device in the moving process according to the network bandwidth and the target time length.

[0019] In one of the embodiments, the demand data amount of the terminal device in the moving process is determined according to the moving prediction data, the data stored in the network database and the data stored in the user database, comprising:

[0020] querying the user habit data of the terminal device from the user database;

[0021] determining the user type of the terminal device by using a classification model to recognize the user habit data;

[0022] determining the demand bandwidth and the additional data amount corresponding to the user type;

[0023] determining the demand data amount according to the demand bandwidth, the target time length and the additional data amount.

[0024] In one of the embodiments, the method further comprises:

[0025] in the case that the maximum download data amount is greater than the demand data amount, and the demand data amount is greater than the current setting threshold, notifying the terminal device to increase the current setting threshold.

[0026] In one of the embodiments, the method further comprises:

[0027] in the case that the current setting threshold is greater than the demand data amount, and the demand data amount is greater than the maximum download data amount, calculating the guaranteed bit rate and the target rate upper limit;

[0028] According to the guaranteed bit rate and the target rate upper limit, a quality of service guarantee level policy is issued to a policy control network element, so that the policy control network element adjusts network bandwidth allocated to the terminal device.

[0029] In one of the embodiments, the method further comprises:

[0030] If the demand data amount is greater than the current set threshold, and the current set threshold is greater than the maximum download data amount, or the demand data amount is greater than the maximum download data amount, and the maximum download data amount is greater than the current set threshold, the terminal device is informed to increase the current set threshold, and the guaranteed bit rate and the target rate upper limit are calculated, according to which a quality of service guarantee level policy is issued to a policy control network element.

[0031] In one of the embodiments, the method further comprises:

[0032] Mobile data of each user device is collected;

[0033] A mobile trend prediction model is trained based on the mobile data, and a trained mobile trend prediction model is obtained.

[0034] In a second aspect, the application further provides a data preloading device, comprising:

[0035] An acquisition module is configured to acquire a current area of a terminal device;

[0036] A prediction module is configured to use a mobile trend prediction model to predict based on the current area, and obtain mobile prediction data;

[0037] A determination module is configured to determine a maximum download data amount of the terminal device during movement based on the mobile prediction data and data stored in a network database, and determine a demand data amount of the terminal device during movement based on the mobile prediction data, the data stored in the network database, and data stored in a user database;

[0038] The acquisition module is further configured to acquire a current set threshold of the terminal device;

[0039] A comparison module is configured to compare the current set threshold, the maximum download data amount, and the demand data amount, and obtain a comparison result;

[0040] An adjustment module is configured to adjust a network configuration corresponding to the terminal device based on the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0041] In a third aspect, the application further provides a communication device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0042] obtaining a current area of the terminal device;

[0043] predicting, by using a mobile trend prediction model, according to the current area to obtain mobile prediction data;

[0044] determining a maximum download data amount of the terminal device in a mobile process according to the mobile prediction data and data stored in a network database;

[0045] determining a demand data amount of the terminal device in the mobile process according to the mobile prediction data, the data stored in the network database and data stored in a user database;

[0046] obtaining a current setting threshold of the terminal device;

[0047] comparing the current setting threshold, the maximum download data amount and the demand data amount to obtain a comparison result;

[0048] adjusting a network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0049] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0050] obtaining a current area of the terminal device;

[0051] predicting, by using a mobile trend prediction model, according to the current area to obtain mobile prediction data;

[0052] determining a maximum download data amount of the terminal device in a mobile process according to the mobile prediction data and data stored in a network database;

[0053] determining a demand data amount of the terminal device in the mobile process according to the mobile prediction data, the data stored in the network database and data stored in a user database;

[0054] obtaining a current setting threshold of the terminal device;

[0055] comparing the current setting threshold, the maximum download data amount and the demand data amount to obtain a comparison result;

[0056] adjusting a network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0057] The data preloading method, device, communication device and computer readable storage medium described above, obtain the current area of the terminal device; use a mobile trend prediction model to predict according to the current area to obtain mobile prediction data; determine the maximum download data volume of the terminal device in the mobile process according to the mobile prediction data and the data stored in the network database; determine the demand data volume of the terminal device in the mobile process according to the mobile prediction data, the data stored in the network database and the data stored in the user database; obtain the current set threshold of the terminal device; compare the current set threshold, the maximum download data volume and the demand data volume to obtain a comparison result; adjust the network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration. In this way, the movement of the terminal device is predicted, the maximum download data volume that can be provided by the network during the movement of the terminal device is determined in combination with the data stored in the network database, the future use is predicted based on the data in the user database, and the demand data volume of the terminal device in the future is determined. By comparing the current set threshold, the maximum download data volume and the demand data volume of the terminal device, whether the current set threshold is too low, whether the current network condition meets the normal playback request of the user and the like can be determined. The network configuration is dynamically adjusted according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration. This method can dynamically adjust the network configuration according to the actual situation, has foresight, improves the preloading efficiency of the terminal device, ensures that the user can normally use the terminal device in a low network quality area, and improves the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0059] Figure 1 A flowchart of a data preloading method in an embodiment;

[0060] Figure 2 A mobile area schematic diagram in an embodiment;

[0061] Figure 3 A flowchart of the step of adjusting the network configuration in an embodiment;

[0062] Figure 4 A flowchart of a data preloading method in another embodiment;

[0063] Figure 5A structural block diagram of a data preloading device in an embodiment;

[0064] Figure 6 An internal structural diagram of a communication device in an embodiment. DETAILED DESCRIPTION

[0065] For the purpose, technical solutions and advantages of the present application to be clearer, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0066] In an exemplary embodiment, as shown in Figure 1 a data preloading method is provided, comprising:

[0067] Step 102, obtaining the current area of the terminal device.

[0068] It can be understood that the data preloading method provided by the present application can be applied to the NWDAF (Network Data Analytics Function, network data analysis function) network element, and can also be applied to other devices with network analysis capability and network configuration adjustment capability. The present application does not limit this. The present embodiment explains and describes the data preloading method applied to the NWDAF network element.

[0069] Among them, the terminal device refers to a mobile terminal using an operator network access interface, which can be but is not limited to various personal computers, notebook computers, smart phones, tablet computers, vehicle-mounted devices and portable wearable devices. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc.

[0070] The NWDAF network element collects historical network quality information of each mobile area, establishes a network database, and contains area length information L_i and average network bandwidth S_i of each area, i being the area identifier (ID). The NWDAF network element obtains the current positioning information of the terminal device, and determines the current area where the terminal device is located according to the current positioning information.

[0071] Step 104, using a mobile trend prediction model to predict according to the current area to obtain mobile prediction data.

[0072] Among them, the mobile trend prediction model is a model trained in advance, the input of which is the current area, and the output of which is the target area passed by the terminal device in a future period of time. It can be understood that the NWDAF network element uses the mobile trend prediction model to predict the mobile trend of the terminal device, and outputs the mobile prediction data. The mobile prediction data includes the target area, and can also include the moving speed and other information.

[0073] Step 106, according to the mobile prediction data and the data stored in the network database, the maximum download data amount of the terminal device in the moving process is determined.

[0074] In order to optimize the wireless network, after the network deployment is completed, the network signal quality of each area in the network needs to be detected to form a network quality data set for subsequent network optimization. In the embodiment, the NWDAF network element periodically collects network data to establish a network database, which stores the length information of each area and the average network bandwidth of each area.

[0075] The bandwidth resources provided by each area passed by the terminal device are analyzed in combination with the mobile prediction data and the data stored in the network database, so as to determine the maximum download data amount of the terminal device in the moving process.

[0076] Step 108, according to the mobile prediction data, the data stored in the network database and the data stored in the user database, the demand data amount of the terminal device in the moving process is determined.

[0077] It can be understood that the data collection module DCCF (Data Collection Coordination Function) of the NWDAF acquires the mobile area data information TAI (Tracking Area Identifier) and Cell ID (Cell Identifier) of each user equipment (UE) and calculates the user moving speed and other data by collecting the AMF (Access and Mobility Management Function), SMF (Session Management Function), UPF (User Plane Function) and other network element data and terminal GPS (Global Positioning System) data. At the same time, the DCCF module collects the historical use habit data of the user (for example, the user historical video watching drag progress bar, the user fast switching interface content, etc.), and establishes a user database according to the collected mobile data and user habit data.

[0078] According to the mobile prediction data and the data stored in the network database, the time length of the user passing through each area can be determined, and by analyzing the data stored in the user database, the future use behavior can be predicted according to the historical use of the user. In combination with the two, the demand data amount of the terminal device in the future can be effectively inferred.

[0079] In an optional implementation, the user database stores the average use bandwidth corresponding to the terminal device, the total moving time length of the terminal device is determined according to the mobile prediction data and the data stored in the network database, and the demand data amount is determined according to the average use bandwidth, the total moving time length and the use habit additional amount.

[0080] Step 110, the current set threshold of the terminal device is acquired.

[0081] The current setting threshold refers to a data volume threshold set by the terminal device for performing the preloading operation, and can be understood as a maximum data volume for a single preloading.

[0082] In step 112, the current setting threshold, the maximum download data volume, and the required data volume are compared to obtain a comparison result.

[0083] In step 114, the network configuration corresponding to the terminal device is adjusted according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0084] By comparing the current setting threshold, the maximum download data volume, and the required data volume, it can be determined whether the current setting threshold is too low, whether the current network condition meets the user's normal playback request, and the like. The adjustment of the network configuration corresponding to the terminal device can be adjusting the current setting threshold of the terminal device, or adjusting the QoS (Quality of Service) guarantee level policy of the PCF network element (Policy Control Network Element). By adjusting the current setting threshold, it can be avoided that the preloaded data volume does not meet the actual browsing requirement, resulting in data loading lag. By adjusting the QoS guarantee level policy, the network quality of the area to be passed through by the user in the future can be improved, and it can be ensured that the terminal device can complete the data preloading operation in each area to be passed through, so that the user can normally use the terminal device in the area with low network quality.

[0085] In an optional implementation, if the current setting threshold is greater than the maximum download data volume, and the maximum download data volume is greater than the required data volume, or the maximum download data volume is greater than the current setting threshold, and the current setting threshold is greater than the required data volume, it indicates that the terminal device can normally preload data when passing through each area, and thus the step of adjusting the network configuration corresponding to the terminal device is not required.

[0086] In the above data preloading method, the movement of the terminal device is predicted, the maximum download data volume that can be provided by the network during the movement of the terminal device is determined based on the data stored in the network database, the future use condition is predicted based on the data in the user database, and thus the required data volume of the terminal device in the future is determined. By comparing the current setting threshold, the maximum download data volume, and the required data volume of the terminal device, it can be determined whether the current setting threshold is too low, whether the current network condition meets the user's normal playback request, and the like. The network configuration is dynamically adjusted according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration. By using the method, the network configuration can be dynamically adjusted according to the actual condition, which is forward-looking, improves the preloading efficiency of the terminal device, ensures that the user can normally use the terminal device in the area with low network quality, and improves the user's use experience.

[0087] In one example embodiment, step 106 comprises: determining a moving speed and a plurality of target areas according to the moving prediction data; querying network bandwidth and moving distance of each target area from a network database; determining a target duration for passing through each target area according to the moving speed and the moving distance; and determining a maximum download data amount of the terminal device in the moving process according to the network bandwidth and the target duration.

[0088] The moving prediction data comprises area identifiers of the plurality of target areas, and the area identifiers are used for Figure 2 , Figure 2 Fig. 1 is a schematic diagram of moving areas in one embodiment, in which the terminal device sequentially passes through areas with strong network signals , areas with weak network signals , areas with strong network signals , areas with weak network signals , and so on in the moving direction. Optionally, the moving prediction data further comprises a moving speed. Optionally, the user database stores moving data of the terminal device, and the moving speed is determined by analyzing the moving data.

[0089] The moving distance is the area length of the target area, and the network bandwidth and the moving distance of each target area can be determined by querying the network database according to the area identifiers of the plurality of target areas.

[0090] The target duration for passing through each target area is determined according to the following formula:

[0091] ;

[0092] In the formula, Lk is the moving distance of the kth target area, v is the moving speed, and T is the target duration for passing through the kth target area. The maximum download data amount of the terminal device in the moving process is determined according to the following formula:

[0093]

[0094] ;

[0095] In the formula, N is the total number of the target areas passed through, and Bk is the network bandwidth of the kth target area.

[0096] In this embodiment, the maximum download data amount of the terminal device in the moving process provided by the network can be determined by combining the moving prediction data and the data stored in the network database, thereby providing data support for adjusting the network configuration.

[0097] ​In an example embodiment, the step 108 comprises: querying the user database to obtain user habit data of the terminal device; determining a user type of the terminal device according to the user habit data by using a classification model; determining a required bandwidth and an additional data amount corresponding to the user type; and determining a required data amount according to the required bandwidth, the target time length and the additional data amount.

[0098] It can be understood that the step 108 specifically comprises the following steps: determining a moving speed and a plurality of target areas according to the moving prediction data; querying a network database to obtain a moving distance of each target area; determining a target time length for passing through each target area according to the moving speed and the moving distance; querying the user database to obtain user habit data of the terminal device; determining a user type of the terminal device according to the user habit data by using a classification model; determining a required bandwidth and an additional data amount corresponding to the user type; and determining a required data amount according to the required bandwidth, the target time length and the additional data amount.

[0099] The classification model is a pre-trained model, and the input of the classification model is feature information corresponding to the user habit data, and the output of the classification model is the user type. In an optional implementation, the classification model is trained by using the user habit data stored in the user database. Specifically, the user habit data stored in the user database is extracted by using a genetic algorithm, and the initial classification model is trained according to the extracted features. After a plurality of iterations of learning, the trained classification model is obtained.

[0100] The step of determining the user type by using the classification model according to the user habit data comprises: extracting features of the user habit data of the terminal device by using a genetic algorithm, inputting the extracted feature information into the classification model, and obtaining the user type output by the classification model.

[0101] A plurality of user types and a required bandwidth and an additional data amount corresponding to each user type are stored in advance. The user types are, for example, a deep watching user, a skip watching user, a multitasking processing user, a fast watching user and a low participation user. The deep watching user tends to watch the content completely without frequently dragging the progress bar when watching a video or other pages. The skip watching user tends to frequently drag the progress bar to skip the content part that is not interesting or known. The multitasking processing user tends to frequently switch the interface content and synchronously view the content of a plurality of application programs. The fast watching user tends to quickly view the interface content. The low participation user has a low frequency of accessing the network. For example, the required bandwidth configured for the deep watching user is less than the required bandwidth configured for the skip watching user, and the required bandwidth configured for the low participation user is the smallest. In this way, the required bandwidth and the additional data amount that match the user usage habit can be determined.

[0102] The required data amount is determined by the following formula:

[0103] ;

[0104] In the formula, N is the total number of target areas passed, is the target duration of passing the kth target area, S is the required bandwidth, is the additional data amount.

[0105] In the embodiment, the network usage of the terminal device in the future is predicted by combining the mobile prediction data, the data stored in the network database and the data stored in the user database, the required data amount of the terminal device is inferred, and data support is provided for adjusting the network configuration.

[0106] In an exemplary embodiment, as shown in Figure 3 , the method further comprises:

[0107] Step 302, in the case that the maximum download data amount is greater than the required data amount, and the required data amount is greater than the current set threshold, the terminal device is notified to increase the current set threshold.

[0108] It can be understood that, Figure 3 three parallel network configuration adjustment modes are shown in the embodiment, in a specific implementation, the NWDAF network element can be equipped with only any one of the network configuration adjustment modes, can be equipped with any two of the network configuration adjustment modes, and can be equipped with all the three parallel network configuration adjustment modes, and the embodiment of the application does not limit this.

[0109] In the formula, the current set threshold is assumed to be , the maximum download data amount is D, and the required data amount is If , it indicates that the current set threshold of the terminal device is too low to meet the data browsing requirement of the user, at this time, the terminal device is notified to increase the current set threshold, so that enough data is preloaded, the data loading lag phenomenon of the terminal device is avoided, and the data browsing requirement of the user is met.

[0110] In an optional implementation, the terminal device is notified to adjust the current set threshold to the target data amount, the target data amount is not less than the required data amount and not greater than the maximum download data amount. In an optional implementation, the terminal device is notified to adjust the current set threshold to the required data amount.

[0111] Step 304, in the case that the current set threshold is greater than the required data amount, and the required data amount is greater than the maximum download data amount, the guaranteed bit rate and the target rate upper limit are calculated; according to the guaranteed bit rate and the target rate upper limit, the quality of service guarantee level policy is issued to the policy control network element, so that the policy control network element adjusts the network bandwidth allocated to the terminal device.

[0112] Among them, if This indicates that the current network conditions cannot meet the user's normal playback needs. At this time, the dynamic QoS guarantee function of the NWDAF network element is used to calculate the Guaranteed Bit Rate (GBR) and Maximum Bit Rate (MBR) based on the required bandwidth. If the calculated QoS guarantee level is greater than the current QoS guarantee level of the terminal device, the QoS guarantee level policy is issued to the PCF network element to adjust the user's bandwidth and ensure the user's normal use.

[0113] Step 306: If the required data volume is greater than the currently set threshold and the currently set threshold is greater than the maximum download data volume, or if the required data volume is greater than the maximum download data volume and the maximum download data volume is greater than the currently set threshold, then notify the terminal device to increase the currently set threshold, calculate the guaranteed bit rate and target rate upper limit, and issue the service quality assurance level policy to the policy control network element based on the guaranteed bit rate and target rate upper limit.

[0114] Among them, if ,or, This indicates that the current threshold set by the terminal device is too low and the current network conditions cannot meet the user's normal playback needs. At this time, the NWDAF network element sends a QoS guarantee policy to the PCF network element and sends a suggestion to the terminal device to increase the threshold.

[0115] In this embodiment, the preloading threshold of the terminal device can be dynamically adjusted to preload enough data, avoiding data loading lag on the terminal device and meeting the user's data browsing needs. By issuing QoS guarantee policies to the PCF network element, network quality can be proactively improved, ensuring that users can use the terminal device normally in areas with low network quality, thus enhancing the user experience.

[0116] In one exemplary embodiment, the method further includes: collecting mobile data from each user device; and training a mobile trend prediction model based on the mobile data to obtain a trained mobile trend prediction model.

[0117] The NWDAF network element collects mobile data from each user device in real time, including historical mobile area data and mobile speed. Based on the mobile data, the mobile trend prediction model is trained, and after multiple iterations of learning, a well-trained mobile trend prediction model is obtained.

[0118] In its implementation, the NWDAF network element's training module MTLF (Model Training Logic Function) uses the TiSASRec algorithm to train the historical mobile area data of the collected user equipment to obtain a mobile trend prediction model.

[0119] Optionally, the NWDAF network element provides a model retraining capability, which can determine the prediction result of the mobile trend prediction model, and if the accuracy is low, the mobile trend prediction model is optimized according to the dynamically updated mobile data in the user database.

[0120] In this embodiment, the mobile trend prediction model can be dynamically trained according to the real-time collected data, and the prediction logic of the mobile trend prediction model is updated, thereby improving the accuracy of the mobile prediction and providing data support for subsequent adjustment of network configuration.

[0121] In one exemplary embodiment, with reference to Figure 4 , a data preloading method is provided, comprising the following steps:

[0122] The NWDAF network element periodically collects user data and network data to establish a database;

[0123] The NWDAF network element predicts user mobile trends and model retraining, and when the system determines that the prediction accuracy is low, the NWDAF network element optimizes the mobile trend prediction model based on the user database;

[0124] The NWDAF network element determines user mobile speed, mobile distance, network bandwidth, and other data information in combination with the data provided by the database and the user mobile trend prediction data;

[0125] The NWDAF network element calculates dynamic QoS guarantee level data and dynamic preloading threshold data respectively, and pushes the dynamic preloading threshold to the terminal application;

[0126] The terminal application compares whether the dynamic preloading threshold is greater than the set preloading data volume threshold, and if it is greater, the terminal application dynamically increases the threshold;

[0127] The NWDAF network element compares whether the required dynamic QoS guarantee level is greater than the current QoS guarantee level of the user equipment, and if it is greater, the GBR value and MBR value are calculated, and the QoS guarantee level strategy is issued to the PCF network element to adjust the user bandwidth, so as to ensure the normal use of the user.

[0128] Exemplarily, with reference to Figure 3 , during the user's movement, the terminal device travels through areas with different network signal strengths, the terminal device travel speed is v, the length of each area is , the network bandwidth of each area is , and the user usage habit additional amount is . The terminal device will pass through the area with strong network signal and the area with weak network signal , and the time taken is and . Specifically, the calculation is performed by the following formula:

[0129] ;

[0130] wherein k = 1 or 2.

[0131] The maximum download data amount D that the terminal can provide after passing through the area and the area is calculated:

[0132] ;

[0133] The data amount required for the user to normally use the area and the area is calculated , wherein S is the bandwidth required for normal use:

[0134] ;

[0135] The terminal device application acquires the current set threshold amount , and the above three values are compared to obtain the following results:

[0136] (1) If or , the user can normally use the area and the area , and no operation needs to be performed.

[0137] (2) If , it indicates that the preloading threshold is set too low, and the terminal device application needs to be notified to increase the set preloading threshold.

[0138] (3) If , it indicates that the current network condition cannot meet the user's normal playback demand, at this time, through the dynamic QoS guarantee function of the NWDAF network element, the GBR value and the MBR value are calculated according to the required bandwidth, the QoS guarantee level policy is issued to the PCF network element to adjust the user bandwidth, and the normal use of the user is guaranteed.

[0139] (4) If or , at this time, the NWDAF network element needs to issue the QoS guarantee policy to the PCF network element, and send a threshold value improvement suggestion to the terminal device application.

[0140] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0141] Based on the same inventive concept, the embodiments of the present application also provide a data preloading device for implementing the data preloading method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more data preloading device embodiments provided below can refer to the limitations of the data preloading method described above, which will not be repeated here.

[0142] In one exemplary embodiment, as shown in Figure 5 A data preloading device is provided, comprising:

[0143] The acquisition module 502 is configured to acquire the current area of the terminal device.

[0144] The prediction module 504 is configured to use a mobile trend prediction model to predict the current area to obtain mobile prediction data.

[0145] The determination module 506 is configured to determine the maximum download data amount of the terminal device during the movement according to the mobile prediction data and the data stored in the network database, and determine the demand data amount of the terminal device during the movement according to the mobile prediction data, the data stored in the network database, and the data stored in the user database.

[0146] The acquisition module 502 is further configured to acquire the current set threshold of the terminal device.

[0147] The comparison module 508 is configured to compare the current set threshold, the maximum download data amount, and the demand data amount to obtain a comparison result.

[0148] The adjustment module 510 is configured to adjust the network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0149] In the data preloading apparatus, the movement of the terminal device is predicted, the maximum download data amount provided by the network during the movement of the terminal device is determined in combination with the data stored in the network database, the future use is predicted based on the data in the user database, and the required data amount of the terminal device in the future is determined. By comparing the current set threshold value of the terminal device, the maximum download data amount, and the required data amount, whether the current set threshold value is too low, whether the current network condition meets the normal play request of the user, and the like can be determined. Network configuration is dynamically adjusted according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration. The method can dynamically adjust the network configuration according to the actual situation, has foresight, improves the preloading efficiency of the terminal device, ensures normal use of the terminal device by the user in a low network quality area, and improves the use experience of the user.

[0150] In an example embodiment, the determining module 506 is further configured to determine a moving speed and a plurality of target areas according to the moving prediction data, query network bandwidths and moving distances of the target areas from the network database, determine target time lengths for passing through the target areas according to the moving speed and the moving distances, and determine the maximum download data amount of the terminal device during the movement according to the network bandwidths and the target time lengths.

[0151] In an example embodiment, the determining module 506 is further configured to query user habit data of the terminal device from the user database, determine a user type of the terminal device by recognizing the user habit data according to a classification model, determine a required bandwidth and an additional data amount corresponding to the user type, and determine the required data amount according to the required bandwidth, the target time length, and the additional data amount.

[0152] In an example embodiment, the adjusting module 510 is further configured to, in a case where the maximum download data amount is greater than the required data amount and the required data amount is greater than the current set threshold value, notify the terminal device to increase the current set threshold value.

[0153] In an example embodiment, the adjusting module 510 is further configured to, in a case where the current set threshold value is greater than the required data amount and the required data amount is greater than the maximum download data amount, calculate a guaranteed bit rate and a target rate upper limit, and issue a quality of service guarantee level policy to a policy control network element according to the guaranteed bit rate and the target rate upper limit, so that the policy control network element adjusts network bandwidth allocated to the terminal device.

[0154] In an example embodiment, the adjusting module 510 is further configured to, if the required data volume is greater than the current set threshold and the current set threshold is greater than the maximum download data volume, or the required data volume is greater than the maximum download data volume and the maximum download data volume is greater than the current set threshold, notify the terminal device to increase the current set threshold, and calculate the guaranteed bit rate and the target rate upper limit, and according to the guaranteed bit rate and the target rate upper limit, send a quality of service guarantee level policy to a policy control network element.

[0155] In an example embodiment, the data preloading apparatus further comprises a training module.

[0156] The training module is configured to collect mobile data of each user equipment, and train a mobile trend prediction model based on the mobile data to obtain a trained mobile trend prediction model.

[0157] The modules in the data preloading apparatus described above can be implemented in whole or in part by software, hardware, and combinations thereof. The modules described above can be embedded in or independent of a processor in a communication device in hardware form, or stored in a memory in a communication device in software form, so as to be called and executed by a processor to perform the operations corresponding to the modules.

[0158] In an example embodiment, a communication device, which can be a server, has an internal structure diagram as shown in Figure 6 The communication device comprises a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the communication device is configured to provide computing and control capabilities. The memory of the communication device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the communication device is configured to exchange information between the processor and external devices. The communication interface of the communication device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a data preloading method.

[0159] Those skilled in the art can understand that Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the communication device to which the scheme of the present application is applied. A specific communication device can comprise more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0160] In one example embodiment, a communication device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program: obtaining a current area of a terminal device; predicting, by using a mobile trend prediction model, to obtain mobile prediction data according to the current area; determining a maximum download data volume of the terminal device in a mobile process according to the mobile prediction data and data stored in a network database; determining a demand data volume of the terminal device in the mobile process according to the mobile prediction data, the data stored in the network database and data stored in a user database; obtaining a current setting threshold of the terminal device; comparing the current setting threshold, the maximum download data volume and the demand data volume to obtain a comparison result; and adjusting a network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0161] In one embodiment, the processor further implements the following steps when executing the computer program: determining a mobile speed and a plurality of target areas according to the mobile prediction data; querying network bandwidths and mobile distances of the target areas from the network database; determining target time lengths for passing through the target areas according to the mobile speed and the mobile distances; and determining the maximum download data volume of the terminal device in the mobile process according to the network bandwidths and the target time lengths.

[0162] In one embodiment, the processor further implements the following steps when executing the computer program: querying user habit data of the terminal device from the user database; determining a user type of the terminal device by using a classification model to identify according to the user habit data; determining a demand bandwidth and an additional data volume corresponding to the user type; and determining the demand data volume according to the demand bandwidth, the target time lengths and the additional data volume.

[0163] In one embodiment, the processor further implements the following steps when executing the computer program: in a case where the maximum download data volume is greater than the demand data volume, and the demand data volume is greater than the current setting threshold, notifying the terminal device to increase the current setting threshold.

[0164] In one embodiment, the processor further implements the following steps when executing the computer program: in a case where the current setting threshold is greater than the demand data volume, and the demand data volume is greater than the maximum download data volume, calculating a guaranteed bit rate and a target rate upper limit; and according to the guaranteed bit rate and the target rate upper limit, issuing a quality of service guarantee level policy to a policy control network element, so that the policy control network element adjusts a network bandwidth allocated to the terminal device.

[0165] In one embodiment, the processor, when executing the computer program, also implements the following steps: if the required data amount is greater than the current set threshold and the current set threshold is greater than the maximum download data amount, or the required data amount is greater than the maximum download data amount and the maximum download data amount is greater than the current set threshold, the terminal device is notified to increase the current set threshold, and the guaranteed bit rate and the target rate upper limit are calculated, and the quality of service guarantee level policy is issued to the policy control network element according to the guaranteed bit rate and the target rate upper limit.

[0166] In one embodiment, the processor, when executing the computer program, also implements the following steps: collecting mobile data of each user equipment; and training a mobile trend prediction model based on the mobile data to obtain a trained mobile trend prediction model.

[0167] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements the following steps: obtaining a current area of a terminal device; predicting, by using a mobile trend prediction model, the current area to obtain mobile prediction data; determining a maximum download data amount of the terminal device in a mobile process according to the mobile prediction data and data stored in a network database; determining a required data amount of the terminal device in the mobile process according to the mobile prediction data, the data stored in the network database and data stored in a user database; obtaining a current set threshold of the terminal device; comparing the current set threshold, the maximum download data amount and the required data amount to obtain a comparison result; and adjusting a network configuration corresponding to the terminal device according to the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

[0168] In one embodiment, the computer program, when executed by the processor, also implements the following steps: determining a mobile speed and a plurality of target areas according to the mobile prediction data; querying network bandwidth and a mobile distance of each target area from the network database; determining a target time length for passing through each target area according to the mobile speed and the mobile distance; and determining the maximum download data amount of the terminal device in the mobile process according to the network bandwidth and the target time length.

[0169] In one embodiment, the computer program, when executed by the processor, also implements the following steps: querying user habit data of the terminal device from the user database; determining a user type of the terminal device by using a classification model to identify the user habit data; determining a required bandwidth and an additional data amount corresponding to the user type; and determining the required data amount according to the required bandwidth, the target time length and the additional data amount.

[0170] In one embodiment, the computer program, when executed by the processor, also implements the following steps: in a case where the maximum download data amount is greater than the required data amount and the required data amount is greater than the current set threshold, the terminal device is notified to increase the current set threshold.

[0171] In one embodiment, the computer program, when executed by the processor, further implements the following steps: in the case that the current set threshold is greater than the demand data amount and the demand data amount is greater than the maximum download data amount, calculating the guaranteed bit rate and the target rate upper limit; and according to the guaranteed bit rate and the target rate upper limit, issuing the quality of service guarantee level policy to the policy control network element, so that the policy control network element adjusts the network bandwidth allocated to the terminal device.

[0172] In one embodiment, the computer program, when executed by the processor, further implements the following steps: if the demand data amount is greater than the current set threshold and the current set threshold is greater than the maximum download data amount, or the demand data amount is greater than the maximum download data amount and the maximum download data amount is greater than the current set threshold, notifying the terminal device to increase the current set threshold, and calculating the guaranteed bit rate and the target rate upper limit, and according to the guaranteed bit rate and the target rate upper limit, issuing the quality of service guarantee level policy to the policy control network element.

[0173] In one embodiment, the computer program, when executed by the processor, further implements the following steps: collecting the mobile data of each user device; and training the mobile trend prediction model based on the mobile data to obtain the trained mobile trend prediction model.

[0174] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of the related data need to comply with relevant regulations.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0176] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0177] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A data preloading method, characterized by, The method comprises: obtaining a current area of a terminal device; using a mobile trend prediction model to predict based on the current area to obtain mobile prediction data; determining a maximum download data volume of the terminal device in a mobile process based on the mobile prediction data and data stored in a network database; determining a demand data volume of the terminal device in the mobile process based on the mobile prediction data, the data stored in the network database and data stored in a user database; obtaining a current set threshold of the terminal device; comparing the current set threshold, the maximum download data volume and the demand data volume to obtain a comparison result; adjusting a network configuration corresponding to the terminal device based on the comparison result to enable the terminal device to perform data preloading based on the adjusted network configuration.

2. The method of claim 1, wherein, Determining the maximum download data volume of the terminal device in the mobile process based on the mobile prediction data and the data stored in the network database comprises: determining a mobile speed and a plurality of target areas based on the mobile prediction data; querying network bandwidth and mobile distance of each target area from the network database; determining a target time length for passing through each target area based on the mobile speed and the mobile distance; determining the maximum download data volume of the terminal device in the mobile process based on the network bandwidth and the target time length.

3. The method of claim 2, wherein, Determining the demand data volume of the terminal device in the mobile process based on the mobile prediction data, the data stored in the network database and the data stored in the user database comprises: querying user habit data of the terminal device from the user database; determining a user type of the terminal device based on the user habit data by using a classification model; determining a demand bandwidth and an additional data volume corresponding to the user type; determining the demand data volume based on the demand bandwidth, the target time length and the additional data volume.

4. The method of claim 1, wherein, The method further comprises: in a case where the maximum download data volume is greater than the demand data volume and the demand data volume is greater than the current set threshold, notifying the terminal device to increase the current set threshold.

5. The method of claim 1, wherein, The method further comprises: in a case where the current set threshold is greater than the demand data volume and the demand data volume is greater than the maximum download data volume, calculating a guaranteed bit rate and a target rate upper limit; based on the guaranteed bit rate and the target rate upper limit, issuing a quality of service guarantee level policy to a policy control network element to enable the policy control network element to adjust a network bandwidth allocated to the terminal device.

6. The method of claim 1, wherein, The method further comprises: if the demand data volume is greater than the current set threshold and the current set threshold is greater than the maximum download data volume, or the demand data volume is greater than the maximum download data volume and the maximum download data volume is greater than the current set threshold, notifying the terminal device to increase the current set threshold and calculating a guaranteed bit rate and a target rate upper limit, and based on the guaranteed bit rate and the target rate upper limit, issuing a quality of service guarantee level policy to a policy control network element.

7. The method according to any one of claims 1 to 6, characterized in that, The method further comprises: collecting mobile data of each user device; Train a mobile trend prediction model based on the mobile data to obtain a trained mobile trend prediction model.

8. A data preloading apparatus, characterized by comprising: The apparatus comprises: An acquisition module configured to acquire a current region of a terminal device; A prediction module configured to use a mobile trend prediction model to predict based on the current region to obtain mobile prediction data; A determination module configured to determine a maximum download data volume of the terminal device during movement based on the mobile prediction data and data stored in a network database, and determine a demand data volume of the terminal device during movement based on the mobile prediction data, the data stored in the network database, and data stored in a user database; The acquisition module is further configured to acquire a current set threshold of the terminal device; A comparison module configured to compare the current set threshold, the maximum download data volume, and the demand data volume to obtain a comparison result; An adjustment module configured to adjust a network configuration corresponding to the terminal device based on the comparison result, so that the terminal device performs data preloading based on the adjusted network configuration.

9. A communication device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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