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

Abstract

The invention relates to a data preloading method and device, communication equipment and a computer readable storage medium. The method comprises the following steps: acquiring a current area of the terminal equipment; performing prediction according to the current area by using a movement trend prediction model to obtain movement prediction data; determining the maximum downloading data volume and the required data volume of the terminal equipment in the moving process in combination with the moving prediction data, the data stored in the network database and the data stored in the user database; acquiring a current set threshold value of the terminal equipment; comparing the current set threshold value, the maximum downloading data volume and the required data volume to obtain a comparison result; and adjusting the network configuration corresponding to the terminal equipment according to the comparison result, so that the terminal equipment performs data preloading based on the adjusted network configuration. By adopting the method, the network configuration can be dynamically adjusted according to the actual situation, the method has perspectiveness, the preloading efficiency of the terminal equipment is improved, and a user is ensured to normally use the terminal equipment in a low-network-quality area.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a data preloading method, apparatus, communication device and computer-readable storage medium. Background Art

[0002] Using mobile devices to watch videos, read literature and listen to music is a common activity in people's daily travel. With the development of network technology, users can control mobile terminals to select multiple ways to access the network, so as to watch videos or browse web pages through mobile terminals, among which users can choose operator networks to access the network.

[0003] At present, the speed at which APP (Application) loads data in mobile terminals is directly related to the quality of the operator's network (for example, poor operator network quality leads to slow APP loading data). If the user browses data faster than the APP loads data, data loading will be stuck, affecting the user experience.

[0004] In order to solve this problem, the related technology usually adopts the preloading method, where the mobile terminal preloads some data to avoid the jamming caused by network fluctuations. However, currently, a certain amount of data is generally preloaded (for example, three short video data are preloaded), and it cannot be adjusted according to the actual situation. In this way, the network configuration lacks foresight and cannot actively improve the network quality; the flexibility of preloading data on the mobile terminal is poor, and if it is in a poor network quality environment for a long time, data loading jams will still occur. Summary of the invention

[0005] Based on this, it is necessary to provide a data preloading method, device, communication equipment and computer-readable storage medium to address the above technical problems, which can dynamically adjust the network configuration according to actual conditions and improve the preloading efficiency of terminal devices.

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

[0007] Get the current region of the terminal device;

[0008] Use the mobile trend prediction model to make predictions based on the current area to obtain mobile prediction data;

[0009] Determine the maximum amount of data to be downloaded by the terminal device during movement based on the movement prediction data and the data stored in the network database;

[0010] Determine the amount of data required by the terminal device during movement based on the movement prediction data, the data stored in the network database, and the data stored in the user database;

[0011] Get the current set threshold of the terminal device;

[0012] Compare the current set threshold, the maximum download data volume, and the required data volume to obtain a comparison result;

[0013] The network configuration corresponding to the terminal device is adjusted according to the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

[0014] In one embodiment, determining the maximum amount of data to be downloaded by the terminal device during movement according to the movement prediction data and the data stored in the network database includes:

[0015] Determine the movement speed and several target areas based on the movement prediction data;

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

[0017] Determine the target time to pass through each target area based on the moving speed and moving distance;

[0018] Determine the maximum amount of data that the terminal device can download during movement based on network bandwidth and target duration.

[0019] In one embodiment, determining the amount of data required by the terminal device during movement according to the movement prediction data, the data stored in the network database, and the data stored in the user database includes:

[0020] Query user habit data of terminal devices from the user database;

[0021] Use the classification model to identify the user type of the terminal device based on user habit data;

[0022] Determine the required bandwidth and additional data volume corresponding to the user type;

[0023] Determine the required data volume based on the required bandwidth, target duration, and additional data volume.

[0024] In one embodiment, the method further comprises:

[0025] When the maximum download data volume is greater than the required data volume, and the required data volume is greater than the currently set threshold, the terminal device is notified to increase the currently set threshold.

[0026] In one embodiment, the method further comprises:

[0027] When the currently set threshold is greater than the required data volume, and the required data volume is greater than the maximum download data volume, the guaranteed bit rate and the target rate upper limit are calculated;

[0028] According to the guaranteed bit rate and the target rate upper limit, the service quality 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.

[0029] In one embodiment, the method further comprises:

[0030] 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 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, the terminal device is notified to increase the currently set threshold, and the guaranteed bit rate and the target rate upper limit are calculated. Based on the guaranteed bit rate and the target rate upper limit, the service quality assurance level policy is sent to the policy control network element.

[0031] In one embodiment, the method further comprises:

[0032] Collecting mobile data from each user’s device;

[0033] The mobile trend prediction model is trained based on the mobile data to obtain a trained mobile trend prediction model.

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

[0035] An acquisition module is used to obtain the current region of the terminal device;

[0036] A prediction module is used to make predictions based on the current area using a mobile trend prediction model to obtain mobile prediction data;

[0037] A determination module, used to determine the maximum amount of data to be downloaded by the terminal device during movement according to the movement prediction data and the data stored in the network database; and to determine the amount of data required by the terminal device during movement according to the movement prediction data, the data stored in the network database and the data stored in the user database;

[0038] The acquisition module is also used to obtain the current set threshold of the terminal device;

[0039] A comparison module is used to compare the current set threshold, the maximum download data volume and the required data volume to obtain a comparison result;

[0040] The adjustment module is used to adjust the network configuration corresponding to the terminal device according to the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

[0041] In a third aspect, the present application further provides a communication device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0042] Get the current region of the terminal device;

[0043] Use the mobile trend prediction model to make predictions based on the current area to obtain mobile prediction data;

[0044] Determine the maximum amount of data to be downloaded by the terminal device during movement based on the movement prediction data and the data stored in the network database;

[0045] Determine the amount of data required by the terminal device during movement based on the movement prediction data, the data stored in the network database, and the data stored in the user database;

[0046] Get the current set threshold of the terminal device;

[0047] Compare the current set threshold, the maximum download data volume, and the required data volume to obtain a comparison result;

[0048] The network configuration corresponding to the terminal device is adjusted according to the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

[0049] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0050] Get the current region of the terminal device;

[0051] Use the mobile trend prediction model to make predictions based on the current area to obtain mobile prediction data;

[0052] Determine the maximum amount of data to be downloaded by the terminal device during movement based on the movement prediction data and the data stored in the network database;

[0053] Determine the amount of data required by the terminal device during movement based on the movement prediction data, the data stored in the network database, and the data stored in the user database;

[0054] Get the current set threshold of the terminal device;

[0055] Compare the current set threshold, the maximum download data volume, and the required data volume to obtain a comparison result;

[0056] The network configuration corresponding to the terminal device is adjusted according to the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

[0057] The above-mentioned data preloading method, device, communication device and computer-readable storage medium obtain the current area of ​​the terminal device; use the 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 during the movement process according to the mobile prediction data and the data stored in the network database; determine the required data volume of the terminal device during the movement 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 setting threshold of the terminal device; compare the current setting threshold, the maximum download data volume and the required data volume to obtain the 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. Through the above-mentioned method, the movement of the terminal device is predicted, and the maximum download data volume that the network can provide during the movement of the terminal device is determined in combination with the data stored in the network database; based on the data in the user database, the future usage is predicted, so as to determine the required data volume of the terminal device in the future; 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 situation meets the user's normal playback request, etc.; support dynamic adjustment of the network configuration 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, is forward-looking, improves the preloading efficiency of the terminal device, ensures that users can use the terminal device normally in areas with low network quality, and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0059] FIG1 is a schematic diagram of a flow chart of a data preloading method in one embodiment;

[0060] FIG2 is a schematic diagram of a moving area in one embodiment;

[0061] FIG3 is a schematic diagram of a flow chart of steps for adjusting network configuration in one embodiment;

[0062] FIG4 is a schematic diagram of a flow chart of a data preloading method in another embodiment;

[0063] FIG5 is a structural block diagram of a data preloading device in one embodiment;

[0064] FIG. 6 is a diagram showing the internal structure of a communication device in one embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] In an exemplary embodiment, as shown in FIG1 , a data preloading method is provided, including:

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

[0068] It is understandable that the data preloading method provided in this application can be applied to NWDAF (Network Data Analytics Function) network elements, and can also be applied to other devices with network analysis capabilities and network configuration adjustment capabilities, and this embodiment of the application does not limit this. This embodiment is explained by applying the data preloading method to the NWDAF network element.

[0069] Among them, terminal devices refer to mobile terminals that use the operator's network access interface, which can be but are not limited to various personal computers, laptops, smart phones, tablets, vehicle-mounted devices and portable wearable devices. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc.

[0070] The NWDAF network element collects historical network quality information of each mobile area and establishes a network database, which includes area length information L_i and average network bandwidth S_i of each area, where i is 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 based on the current positioning information.

[0071] Step 104, using the mobile trend prediction model to make predictions based on the current area to obtain mobile prediction data.

[0072] The mobile trend prediction model is a pre-trained model, whose input is the current area and output is the target area that the terminal device will pass through in the future. 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 mobile prediction data. The mobile prediction data includes the target area and may also include information such as the moving speed.

[0073] Step 106, determining the maximum download data volume of the terminal device during the movement process according to the movement prediction data and the data stored in the network database.

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

[0075] By combining the mobile prediction data with the data stored in the network database, the bandwidth resources that can be provided by each area that the terminal device passes through are analyzed to determine the maximum amount of data that the terminal device can download during the movement.

[0076] Step 108, determining the data volume required by the terminal device during the movement process according to the movement prediction data, the data stored in the network database and the data stored in the user database.

[0077] It can be understood that the data collection module DCCF (data collection coordination function) of NWDAF obtains the mobile area data information TAI (tracking area identifier) ​​and Cell ID (cell identifier) ​​of each user equipment (UE) and calculates data such as user movement speed through the collected network element data such as AMF (access and mobility management function), SMF (session management function), UPF (user plane function) and terminal GPS (global positioning system) data. At the same time, the DCCF module collects the user's historical usage habit data (for example, the user's historical video viewing and dragging progress bar, the user's quick switching of interface content, etc.), and establishes a user database based on the collected mobile data and user habit data.

[0078] Among them, based on the mobile prediction data and the data stored in the network database, the length of time the user spends passing through each area can be determined. By analyzing the data stored in the user database, future usage behavior can be predicted based on the user's historical usage. Combining the two can effectively infer the amount of data required by the terminal device in the future.

[0079] In an optional implementation, the user database stores the average bandwidth usage corresponding to the terminal device, and determines the total movement time of the terminal device based on the movement prediction data and the data stored in the network database. The required data volume is determined based on the average bandwidth usage, the total movement time and the additional amount of usage habits.

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

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

[0082] Step 112, comparing the current set threshold, the maximum download data volume and the required data volume to obtain a comparison result.

[0083] Step 114, adjusting 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.

[0084] Among them, by comparing the current set threshold, the maximum amount of downloaded data, and the required amount of data, it is possible to determine whether the current set threshold is too low, whether the current network conditions meet the user's normal playback request, etc., wherein adjusting the network configuration corresponding to the terminal device can be to adjust the current set threshold of the terminal device, and can also be to adjust the QoS (Quality of Service) guarantee level policy of the PCF network element (policy control network element). By adjusting the current set threshold, it can be avoided that the amount of preloaded data does not meet the actual browsing needs, resulting in data loading jams. By adjusting the QoS guarantee level policy, the network quality of the areas that the user will pass through in the future can be improved, ensuring that the terminal device can complete the data preloading operation in each area it passes through, so that the user can use the terminal device normally in areas with low network quality.

[0085] In an optional implementation, if the currently set threshold is greater than the maximum downloaded data amount, and the maximum downloaded data amount is greater than the required data amount, or the maximum downloaded data amount is greater than the currently set threshold, and the currently set threshold is greater than the required data amount, indicating that the terminal device can preload data normally when passing through each area, there is no need to execute the steps of adjusting the network configuration corresponding to the terminal device.

[0086] In the above data preloading method, the movement of the terminal device is predicted, and the maximum amount of download data that the network can provide during the movement of the terminal device is determined in combination with the data stored in the network database; based on the data in the user database, the future usage is predicted to determine the amount of data required by the terminal device in the future; by comparing the current set threshold, the maximum amount of download data, and the required amount of data of the terminal device, it can be determined whether the current set threshold is too low, whether the current network situation meets the user's normal playback request, etc.; it supports dynamic adjustment of the network configuration based on the comparison results, so that the terminal device can preload data based on the adjusted network configuration. This method can dynamically adjust the network configuration according to the actual situation, is forward-looking, improves the preloading efficiency of the terminal device, ensures that users can use the terminal device normally in low network quality areas, and improves the user experience.

[0087] In an exemplary embodiment, step 106 includes: determining the moving speed and several target areas based on the movement prediction data; querying the network bandwidth and moving distance of each target area from the network database; determining the target time for passing through each target area based on the moving speed and the moving distance; and determining the maximum amount of data downloaded by the terminal device during the movement based on the network bandwidth and the target time.

[0088] The movement prediction data includes the area identification of several target areas. Referring to FIG. 2 , FIG. 2 is a schematic diagram of a movement area in an embodiment. The terminal device passes through the areas with strong network signals in the direction of travel. , areas with weak network signals , areas with strong network signals , areas with weak network signals Etc. Optionally, the movement prediction data also includes the movement speed. Optionally, the user database stores movement data of the terminal device, and the movement speed is determined by analyzing the movement data.

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

[0090] Determine the target time to pass through each target area using the following formula:

[0091] ;

[0092] In the formula, is the moving distance of the kth target area, v is the moving speed, is the target duration to pass through the kth target area.

[0093] The maximum amount of data that can be downloaded by a terminal device during movement is determined using the following formula:

[0094] ;

[0095] Where N is the total number of target areas passed. is the network bandwidth of the kth target area.

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

[0097] In an exemplary embodiment, step 108 includes: querying user habit data of the terminal device from the user database; using a classification model to identify the user type of the terminal device based on the user habit data; determining the required bandwidth and additional data volume corresponding to the user type; and determining the required data volume based on the required bandwidth, target duration, and additional data volume.

[0098] It can be understood that step 108 specifically includes the following steps: determining the moving speed and several target areas based on the mobile prediction data; querying the moving distance of each target area from the network database; determining the target time for passing through each target area based on the moving speed and the moving distance; querying the user habit data of the terminal device from the user database; using the classification model to identify the user type of the terminal device based on the user habit data; determining the required bandwidth and additional data volume corresponding to the user type; and determining the required data volume based on the required bandwidth, target time and additional data volume.

[0099] The classification model is a pre-trained model, whose input is feature information corresponding to user habit data, and whose output is user type. In an optional implementation, the classification model is trained using user habit data stored in a user database. Specifically, a genetic algorithm is used to extract features from the user habit data stored in the user database, and the initial classification model is trained based on the extracted features. After multiple iterative learning, a trained classification model is obtained.

[0100] The step of using the classification model to identify based on the user habit data includes: using a genetic algorithm to extract features from the user habit data of the terminal device, inputting the extracted feature information into the classification model, and obtaining the user type output by the classification model.

[0101] A variety of user types and the required bandwidth and additional data volume corresponding to each user type are stored in advance. User types include deep viewing users, jump viewing users, multitasking users, fast viewing users, low-engagement users, and the like. Among them, deep viewing users seldom drag the progress bar when watching videos or other pages, and tend to watch the content completely; jump viewing users tend to drag the progress bar frequently and skip uninteresting or known content parts; multitasking users tend to frequently switch interface content and view the content of multiple applications simultaneously; fast viewing users tend to quickly view interface content; and low-engagement users access the network less frequently. Exemplarily, the required bandwidth configured for deep viewing users is less than the required bandwidth configured for jump viewing users, and the required bandwidth configured for low-engagement users is the smallest. In this way, the required bandwidth and additional data volume that match the user's usage habits can be determined.

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

[0103] ;

[0104] Where N is the total number of target areas passed. is the target time to pass through the kth target area, S is the required bandwidth, The amount of additional data.

[0105] In this embodiment, the mobile prediction data, the data stored in the network database and the data stored in the user database are combined to predict the future network usage of the terminal device, infer the required data volume of the terminal device, and provide data support for adjusting the network configuration.

[0106] In an exemplary embodiment, as shown in FIG3 , the method further includes:

[0107] Step 302: When the maximum download data volume is greater than the required data volume and the required data volume is greater than the currently set threshold, notify the terminal device to increase the currently set threshold.

[0108] It can be understood that Figure 3 shows three parallel network configuration adjustment methods. In a specific implementation, the NWDAF network element can be equipped with only any one of the network configuration adjustment methods, or can be equipped with any two of the network configuration adjustment methods, or can be equipped with the three parallel network configuration adjustment methods at the same time. The embodiments of the present application are not limited to this.

[0109] Assume that the current threshold is , the maximum download data volume is D, the required data volume is ,like , indicating that the current setting threshold of the terminal device is too low and cannot meet the user's data browsing needs. At this time, the terminal device is notified to increase the current setting threshold, so as to preload enough data, avoid data loading jams on the terminal device, and meet the user's data browsing needs.

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

[0111] Step 304, when the currently set threshold is greater than the required data volume, and the required data volume is greater than the maximum download data volume, calculate the guaranteed bit rate and the target rate upper limit; based on the guaranteed bit rate and the target rate upper limit, send the service quality assurance level policy 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 , indicating that the current network situation 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 the target rate limit (MBR) according to the required bandwidth. If the calculated QoS (quality of service) guarantee level is greater than the current QoS guarantee level of the terminal device, the QoS guarantee level policy is sent to the PCF network element to adjust the user bandwidth to ensure normal use of the user.

[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 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, notify the terminal device to increase the currently set threshold, and calculate the guaranteed bit rate and the target rate upper limit, and send the service quality assurance level policy to the policy control network element based on the guaranteed bit rate and the target rate upper limit.

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

[0115] In this embodiment, the preloading threshold of the terminal device can be dynamically adjusted to preload enough data to avoid data loading jams in the terminal device and meet the user's data browsing needs. By issuing QoS guarantee policies to the PCF network element, the network quality can be actively improved to ensure that users can use the terminal device normally in areas with low network quality, thereby improving the user experience.

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

[0117] Among them, the NWDAF network element collects the mobile data of each user device in real time. The mobile data includes historical mobile area data and mobile speed, etc.; the mobile trend prediction model is trained based on the mobile data, and after multiple iterative learning, a trained mobile trend prediction model is obtained.

[0118] In the specific implementation, the training module MTLF (model training logic function) of the NWDAF network element uses the TiSASRec algorithm to train the collected historical mobile area data of user devices to obtain a mobile trend prediction model.

[0119] Optionally, the NWDAF network element provides a model retraining capability, and can determine the prediction result of the mobility trend prediction model. If the accuracy is low, the mobility trend prediction model is optimized according to the dynamically updated mobility 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 can be updated, thereby improving the accuracy of mobile prediction and providing data support for subsequent adjustment of network configuration.

[0121] In an exemplary embodiment, referring to FIG. 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 mobility trends and retrains the model. When the system determines that the prediction accuracy is low, the NWDAF network element optimizes the mobility trend prediction model based on the user database;

[0124] The NWDAF network element combines the data provided by the database and the user movement trend prediction data to determine the user's movement speed, movement distance, network bandwidth and other data information;

[0125] The NWDAF network element calculates the 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 so, 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. If so, it calculates the GBR value and the MBR value and sends the QoS guarantee level policy to the PCF network element to adjust the user bandwidth to ensure normal use of the user.

[0128] For example, referring to FIG3 , during the user's movement, the user passes through areas with different network signal strengths. The terminal device travels at a speed of v, and the length of each area is , the network bandwidth of each area is , the additional amount of user usage habits is . Predict that the terminal device will pass through an area with a strong network signal and areas with weak network signals , respectively, and . It is calculated specifically using the following formula:

[0129] ;

[0130] Where k=1 or 2.

[0131] The computing terminal passes through the area and Region The maximum download data volume D that can be provided:

[0132] ;

[0133] Calculation ensures that users can use the area normally and Region The amount of data required , where S is the bandwidth required for normal use:

[0134] ;

[0135] Get the current threshold value of the terminal device application , compare the above three values ​​to obtain the following results:

[0136] (1) If or , the user passes through the area and Region Can be used normally, no operation is required;

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

[0138] (3) If , it means that the current network situation cannot meet the normal playback needs of users. At this time, the dynamic QoS guarantee function of the NWDAF network element calculates the GBR value and MBR value based on the required bandwidth, and sends the QoS guarantee level policy to the PCF network element to adjust the user bandwidth to ensure normal use of users;

[0139] (4) If or , then the NWDAF network element needs to issue a QoS guarantee policy to the PCF network element and send a threshold increase recommendation to the terminal device application.

[0140] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

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

[0142] In an exemplary embodiment, as shown in FIG5 , a data preloading device is provided, comprising:

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

[0144] The prediction module 504 is used to use the movement trend prediction model to make predictions according to the current area to obtain movement prediction data.

[0145] Determination module 506 is used to determine the maximum amount of data downloaded by the terminal device during the movement process based on the movement prediction data and the data stored in the network database; determine the required data amount of the terminal device during the movement process based on the movement prediction data, the data stored in the network database and the data stored in the user database.

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

[0147] The comparison module 508 is used to compare the current set threshold, the maximum download data volume and the required data volume to obtain a comparison result.

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

[0149] In the above-mentioned data preloading device, the movement of the terminal device is predicted, and the maximum amount of download data that the network can provide during the movement of the terminal device is determined in combination with the data stored in the network database; based on the data in the user database, the future usage is predicted, thereby determining the amount of data required by the terminal device in the future; by comparing the current set threshold, the maximum amount of download data, and the required amount of data of the terminal device, it is possible to determine whether the current set threshold is too low, whether the current network situation meets the user's normal playback request, etc.; it supports dynamic adjustment of the network configuration according to the comparison result, so that the terminal device preloads data based on the adjusted network configuration. This method can dynamically adjust the network configuration according to the actual situation, is forward-looking, improves the preloading efficiency of the terminal device, ensures that users can use the terminal device normally in low network quality areas, and improves the user experience.

[0150] In an exemplary embodiment, the determination module 506 is also used to determine the moving speed and several target areas based on the movement prediction data; query the network bandwidth and moving distance of each target area from the network database; determine the target time for passing through each target area based on the moving speed and moving distance; determine the maximum download data volume of the terminal device during the movement based on the network bandwidth and the target time.

[0151] In an exemplary embodiment, the determination module 506 is also used to query the user habit data of the terminal device from the user database; use the classification model to identify the user type of the terminal device based on the user habit data; determine the required bandwidth and additional data volume corresponding to the user type; and determine the required data volume based on the required bandwidth, target duration, and additional data volume.

[0152] In an exemplary embodiment, the adjustment module 510 is further configured to notify the terminal device to increase the current set threshold when the maximum download data amount is greater than the required data amount and the required data amount is greater than the current set threshold.

[0153] In an exemplary embodiment, the adjustment module 510 is also used to calculate the guaranteed bit rate and the target rate upper limit when the currently set threshold is greater than the required data volume and the required data volume is greater than the maximum download data volume; based on the guaranteed bit rate and the target rate upper limit, the service quality assurance level policy is sent to the policy control network element so that the policy control network element adjusts the network bandwidth allocated to the terminal device.

[0154] In an exemplary embodiment, the adjustment module 510 is also used to notify the terminal device to increase the current set threshold if the required data volume is greater than the current set threshold and the current set threshold is greater than the maximum downloaded data volume, or the required data volume is greater than the maximum downloaded data volume and the maximum downloaded data volume is greater than the current set threshold, and calculate the guaranteed bit rate and the target rate upper limit, and send the service quality assurance level policy to the policy control network element based on the guaranteed bit rate and the target rate upper limit.

[0155] In an exemplary embodiment, the data preloading device further includes a training module;

[0156] The training module is used to collect the mobile data of each user device; train the mobile trend prediction model based on the mobile data to obtain a trained mobile trend prediction model.

[0157] Each module in the above-mentioned data preloading device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the communication device in the form of hardware, or can be stored in the memory in the communication device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0158] In an exemplary embodiment, a communication device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The communication device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the communication device is used to provide computing and control capabilities. The memory of the communication device includes 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 operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the communication device is used to exchange information between the processor and an external device. The communication interface of the communication device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a data preloading method is implemented.

[0159] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial 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. The specific communication device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0160] In an exemplary embodiment, a communication device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining a current area of ​​a terminal device; making a prediction based on the current area using a mobile trend prediction model to obtain mobile prediction data; determining a maximum amount of downloaded data of the terminal device during movement based on the mobile prediction data and data stored in a network database; determining a required amount of data of the terminal device during movement based on the mobile prediction data, 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 amount of downloaded data, and the required amount of data to obtain a comparison result; and adjusting a network configuration corresponding to the terminal device based on the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

[0161] In one embodiment, the processor also implements the following steps when executing the computer program: determining the moving speed and several target areas based on the movement prediction data; querying the network bandwidth and moving distance of each target area from the network database; determining the target time for passing through each target area based on the moving speed and moving distance; and determining the maximum amount of data downloaded by the terminal device during the movement based on the network bandwidth and the target time.

[0162] In one embodiment, when the processor executes the computer program, the following steps are also implemented: querying the user habit data of the terminal device from the user database; using the classification model to identify the user type of the terminal device based on the user habit data; determining the required bandwidth and additional data volume corresponding to the user type; and determining the required data volume based on the required bandwidth, target duration, and additional data volume.

[0163] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the maximum download data amount is greater than the required data amount, and the required data amount is greater than the currently set threshold, the terminal device is notified to increase the currently set threshold.

[0164] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the currently set threshold is greater than the required data volume, and the required data volume is greater than the maximum download data volume, the guaranteed bit rate and the target rate upper limit are calculated; based on the guaranteed bit rate and the target rate upper limit, the service quality assurance 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.

[0165] In one embodiment, the processor also implements the following steps when executing the computer program: 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 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, and calculate the guaranteed bit rate and the target rate upper limit, and based on the guaranteed bit rate and the target rate upper limit, send the service quality assurance level policy to the policy control network element.

[0166] In one embodiment, when the processor executes the computer program, the following steps are also implemented: collecting movement data of each user device; training the movement trend prediction model based on the movement data to obtain a trained movement trend prediction model.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: obtaining a current area of ​​a terminal device; using a mobile trend prediction model to make predictions based on the current area to obtain mobile prediction data; determining a maximum amount of downloaded data of the terminal device during movement based on the mobile prediction data and data stored in a network database; determining a required amount of data of the terminal device during movement based on the mobile prediction data, 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 amount of downloaded data, and the required amount of data to obtain a comparison result; and adjusting a network configuration corresponding to the terminal device based on the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

[0168] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the moving speed and several target areas based on the movement prediction data; querying the network bandwidth and moving distance of each target area from the network database; determining the target time for passing through each target area based on the moving speed and moving distance; determining the maximum amount of data downloaded by the terminal device during the movement process based on the network bandwidth and the target time.

[0169] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: querying the user habit data of the terminal device from the user database; using the classification model to identify the user type of the terminal device based on the user habit data; determining the required bandwidth and additional data volume corresponding to the user type; and determining the required data volume based on the required bandwidth, target duration, and additional data volume.

[0170] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the maximum download data amount is greater than the required data amount, and the required data amount is greater than the currently set threshold, the terminal device is notified to increase the currently set threshold.

[0171] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the currently set threshold is greater than the required data volume, and the required data volume is greater than the maximum download data volume, the guaranteed bit rate and the target rate upper limit are calculated; based on the guaranteed bit rate and the target rate upper limit, the service quality assurance 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.

[0172] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: 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 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, and calculate the guaranteed bit rate and the target rate upper limit, and based on the guaranteed bit rate and the target rate upper limit, send the service quality assurance level policy to the policy control network element.

[0173] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: collecting movement data of each user device; training a movement trend prediction model based on the movement data to obtain a trained movement 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 used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0175] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the 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. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0177] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data preloading method, characterized in that: The method comprises: Get the current region of the terminal device; Using a mobile trend prediction model to make a prediction based on the current area to obtain mobile prediction data; Determining a maximum amount of data to be downloaded by the terminal device during movement according to the movement prediction data and data stored in a network database; Determining the amount of data required by the terminal device during movement according to the movement prediction data, the data stored in the network database, and the data stored in the user database; Obtaining a currently set threshold value of the terminal device; Compare the current set threshold, the maximum download data volume and the required data volume to obtain a comparison result; The network configuration corresponding to the terminal device is adjusted according to the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

2. The method according to claim 1, characterized in that Determining the maximum amount of data downloaded by the terminal device during movement according to the movement prediction data and the data stored in the network database includes: Determining a movement speed and a plurality of target areas according to the movement prediction data; Query the network bandwidth and moving distance of each target area from the network database; Determine a target time for passing through each target area according to the moving speed and the moving distance; The maximum amount of data to be downloaded by the terminal device during movement is determined according to the network bandwidth and the target duration.

3. The method according to claim 2, characterized in that Determining the amount of data required by the terminal device during movement according to the movement prediction data, the data stored in the network database, and the data stored in the user database includes: Querying the user habit data of the terminal device from the user database; Using a classification model to identify the user type of the terminal device according to the user habit data; Determine the required bandwidth and additional data volume corresponding to the user type; The required data volume is determined according to the required bandwidth, the target duration, and the additional data volume.

4. The method according to claim 1, characterized in that The method further comprises: 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 currently set threshold, the terminal device is notified to increase the currently set threshold.

5. The method according to claim 1, characterized in that The method further comprises: When the currently set threshold is greater than the required data volume, and the required data volume is greater than the maximum download data volume, calculating a guaranteed bit rate and a target rate upper limit; According to the guaranteed bit rate and the target rate upper limit, a service quality assurance level policy is issued to a policy control network element, so that the policy control network element adjusts the network bandwidth allocated to the terminal device.

6. The method according to claim 1, characterized in that The method further comprises: 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 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 the target rate upper limit, and send the service quality assurance level policy to the policy control network element based on the guaranteed bit rate and the target rate upper limit.

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

8. A data preloading device, characterized in that: The device comprises: An acquisition module is used to obtain the current region of the terminal device; A prediction module, used to make a prediction based on the current area using a mobile trend prediction model to obtain mobile prediction data; a determination module, configured to determine the maximum amount of data to be downloaded by the terminal device during movement according to the movement prediction data and the data stored in the network database; and to determine the amount of data required by the terminal device during movement according to the movement prediction data, the data stored in the network database and the data stored in the user database; The acquisition module is further used to acquire the current set threshold of the terminal device; A comparison module, used for comparing the current set threshold, the maximum download data volume and the required data volume to obtain a comparison result; An adjustment module is used to adjust the network configuration corresponding to the terminal device according to the comparison result, so that the terminal device preloads data based on the adjusted network configuration.

9. A communication device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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