UE communication state prompting method and device, communication equipment, medium and product

Abstract

The invention relates to a UE communication state prompting method and device, communication equipment, a medium and a product, and relates to the technical field of communication. The method comprises the following steps: predicting movement trend data of first UE at a current prediction moment according to historical positioning data and historical advancing data of the first UE at a historical reference moment under the condition of network communication between the first UE and second UE; according to the movement trend data, determining a to-be-marched area of the first UE and an area duration in the to-be-marched area; sending a communication state message to the second UE based on the region duration under the condition that the to-be-marched region is a communication abnormal region; the communication state message is used for prompting that the communication state of the first UE is about to be abnormal. According to the technology provided by the invention, the second UE can predict the abnormal communication condition of the first UE in advance, and the situation that the abnormal communication condition suddenly occurs in the network communication process of the first UE and the second UE to influence the communication perception of both users is avoided.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a UE communication status prompt method, device, communication equipment, medium and product. Background Art

[0002] With the continuous development of mobile communication technology, the communication method between users is no longer limited to using the traditional telephone service provided by the network provider to make calls, but can also make calls through the network calling service provided by the application.

[0003] Since the call quality of the Internet call service is closely related to the network quality in the areas where the communicating parties are located, if the network quality of either party is poor, the call experience of both parties will be affected. Summary of the invention

[0004] Based on this, it is necessary to provide a UE communication status prompt method, device, communication equipment, medium and product to address the above technical problems, so as to effectively deal with the situation where network calls are affected by poor regional network quality.

[0005] In a first aspect, the present application provides a UE communication status prompt method, comprising:

[0006] In case of network-based communication between the first UE and the second UE, predicting the movement trend data of the first UE at a current prediction time based on the historical positioning data and the historical travel data of the first UE at a historical reference time;

[0007] Determine, according to the movement trend data, an area to which the first UE is about to travel and a duration of time that the first UE is in the area to which it is about to travel;

[0008] When the area to be traveled is a communication abnormal area, based on the duration of the area, a communication status message is sent to the second UE;

[0009] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0010] In one of the embodiments, when the area to be traveled is a communication abnormality area, a communication status message is sent to the second UE based on the duration of the area, including: when the area to be traveled is a communication abnormality area, determining the cause of the communication abnormality in the communication abnormality area; sending a communication status message to the second UE based on the cause of the communication abnormality and the duration of the area.

[0011] In one of the embodiments, a communication status message is sent to the second UE based on the cause of the communication abnormality and the duration of the area, including: obtaining historical movement trend data and corresponding real movement trend data predicted at different historical prediction moments within a historical verification period; the historical verification period is a neighborhood period before the current prediction moment; the prediction accuracy is determined based on the consistency between the historical movement trend data and the corresponding real movement trend data at each historical prediction moment within the historical verification period; when the prediction accuracy is greater than a preset accuracy threshold, the area to which the first UE is about to travel and the duration of the area in which the first UE is about to travel are determined based on the movement trend data. In one of the embodiments, the mobile trend data of the first UE is predicted based on the historical positioning data and historical travel data of the first UE, including: based on a trained mobile trend prediction model, predicting the mobile trend data of the first UE at a current prediction moment based on the historical positioning data and historical travel data of the first UE at a historical reference moment; accordingly, the method also includes: when the prediction accuracy is not greater than a preset accuracy threshold, adjusting the network parameters of the trained mobile trend prediction model based on the historical positioning data and the corresponding historical travel data at the historical reference moment corresponding to each historical prediction moment, and the actual mobile trend data at the corresponding historical prediction moment; based on the adjusted mobile trend prediction model, re-predicting the mobile trend data of the first UE at the current prediction moment based on the historical positioning data and the historical travel data of the first UE at the historical reference moment.

[0012] In one of the embodiments, the mobile trend data includes a traveling direction and a traveling speed; based on the mobile trend data, determining the upcoming traveling area of ​​the first UE and the duration of time that the first UE is in the upcoming traveling area, including: when the first UE is at a current position and moves at a traveling speed along the traveling direction, treating the mobile area that the first UE is about to reach as the upcoming traveling area; determining the duration of time that the first UE is in the upcoming traveling area based on the traveling speed and the area length of the upcoming traveling area in the traveling direction.

[0013] In a second aspect, the present application provides a UE communication status prompting device, comprising:

[0014] A prediction module, configured to predict movement trend data of the first UE at a current prediction time based on historical positioning data and historical travel data of the first UE at a historical reference time in a case where the first UE and the second UE are in communication based on a network;

[0015] A determination module, configured to determine, based on the movement trend data, an area to which the first UE is about to travel and a duration for which the first UE is in the area to which it is about to travel;

[0016] A sending module, configured to send a communication status message to a second UE based on the duration of the area when the area to be traveled is a communication abnormal area;

[0017] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0018] In a third aspect, the present application further provides a communication device, the communication device comprising a memory, a transceiver and a processor, the memory storing a computer program, the transceiver being used to receive data or send data under the control of the processor, and the processor implementing the following steps when executing the computer program:

[0019] In case of network-based communication between the first UE and the second UE, predicting the movement trend data of the first UE at a current prediction time based on the historical positioning data and the historical travel data of the first UE at a historical reference time;

[0020] Determine, according to the movement trend data, an area to which the first UE is about to travel and a duration of time that the first UE is in the area to which it is about to travel;

[0021] When the area to be traveled is a communication abnormal area, based on the duration of the area, a communication status message is sent to the second UE;

[0022] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0023] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0024] In case of network-based communication between the first UE and the second UE, predicting the movement trend data of the first UE at a current prediction time based on the historical positioning data and the historical travel data of the first UE at a historical reference time;

[0025] Determine, according to the movement trend data, an area to which the first UE is about to travel and a duration of time that the first UE is in the area to which it is about to travel;

[0026] When the area to be traveled is a communication abnormal area, based on the duration of the area, a communication status message is sent to the second UE;

[0027] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0028] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0029] In case of network-based communication between the first UE and the second UE, predicting the movement trend data of the first UE at a current prediction time based on the historical positioning data and the historical travel data of the first UE at a historical reference time;

[0030] Determine, according to the movement trend data, an area to which the first UE is about to travel and a duration of time that the first UE is in the area to which it is about to travel;

[0031] When the area to be traveled is a communication abnormal area, based on the duration of the area, a communication status message is sent to the second UE;

[0032] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0033] The above-mentioned UE communication status prompting method, device, communication equipment, medium and product predict the movement trend data of the first UE at the current prediction moment based on the historical positioning data and historical travel data of the first UE at the historical reference moment in the case of network communication between the first UE and the second UE, and determine the upcoming area of ​​the first UE and the duration of the area in which the first UE is in the upcoming area based on the movement trend data; in the case that the upcoming area is a communication abnormality area, based on the duration of the area, a communication status message is sent to the second UE to prompt that the communication status of the first UE is about to be abnormal, so that the second UE can predict the communication abnormality of the first UE in advance, thereby avoiding sudden communication interruption or communication quality degradation and other communication abnormalities during the network communication between the first UE and the second UE, which affects the communication perception of users of both parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of a method for prompting a UE communication status in one embodiment;

[0035] Figure 2 A schematic diagram of a flow chart of steps for sending a communication status message in one embodiment;

[0036] Figure 3 A schematic flow chart of the steps for determining the area to be traveled and the duration of the area in one embodiment;

[0037] Figure 4A It is a flowchart of a method for prompting a UE communication status in another embodiment;

[0038] Figure 4B A schematic diagram of the regional network status and regional length of each mobile area in an embodiment;

[0039] Figure 5 It is a structural block diagram of a UE communication status prompting device in one embodiment;

[0040] Figure 6 FIG. 4 is a diagram showing the internal structure of a communication device in one embodiment. DETAILED DESCRIPTION

[0041] 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.

[0042] To facilitate understanding, the functional network elements involved in this application are first introduced.

[0043] UE (User Equipment) accesses the data network (DN) and the like by accessing the operator network, and can use the services provided by the operator or a third party on the DN. For the convenience of explanation, the user terminal, user equipment, terminal equipment or terminal in the embodiments of the present application may be collectively referred to as UE. That is, unless otherwise specified, the UE described in the embodiments of the present application can be replaced by a user terminal, user equipment, terminal equipment or terminal, and of course they can also be interchanged.

[0044] NWDAF (Network Data Analytics Function) was introduced by 3GPP (3rd Generation Partnership Project) in R15 (Release 15, the 15th public version of the protocol) to simplify the generation and consumption of core network data, and to be able to perceive, analyze and take actions to provide endogenous intelligence to the network and enhance the end-user experience. It collects, processes and analyzes various data from within the network to provide insights and basis for network optimization, decision support and automation.

[0045] In an alternative embodiment, if Figure 1 As shown, a UE communication status prompt method is provided, which can be applied to NWDAF. In this embodiment, the method includes the following steps:

[0046] S110, predicting the movement trend data of the first UE at the current prediction time according to the historical positioning data and historical travel data of the first UE at the historical reference time in the case of network communication between the first UE and the second UE.

[0047] Among them, network communication is a communication method for audio and video calls based on network communication services. Among them, the first UE is any communication party in the network communication process, and the second UE is the other communication party in the network communication process. If the first UE is the communication initiator, the second UE is the communication receiver; if the first UE is the communication receiver, the second UE is the communication receiver.

[0048] The historical reference moment can be understood as a preset number of historical moments adjacent to the current prediction moment. The preset number can be set or adjusted by technicians according to needs or experience, or determined through a large number of experiments, and this application does not impose any limitation on this.

[0049] Among them, the historical positioning data can be understood as the positioning data of the first UE at the historical reference moment. Optionally, the historical positioning data may include a Cell ID (Cell Identity) for identifying the cell where the first UE is located in the mobile communication network. Or optionally, the historical positioning data may include a TAI (Tracking Area Identity) for characterizing the tracking area where the first UE is located in the mobile communication network. Among them, TAI includes PLMN (Public Land Mobile Network) and TAC (Tracking Area Code).

[0050] The historical travel data may include travel speed data of the first UE at a historical reference time, which is used to numerically quantify the travel efficiency of the first UE.

[0051] The movement trend data is used to characterize the movement trend of the first UE at the current predicted moment, and may include at least one of a moving direction and a moving speed.

[0052] In an optional embodiment, the mobile trend data of the first UE at the current prediction moment can be predicted based on the historical positioning data and historical travel data of the first UE at the historical reference moment based on a pre-trained mobile trend prediction model. The mobile trend prediction model can be implemented based on a traditional machine learning model or a deep learning model, and the present application does not impose any limitation on the specific network structure of the mobile trend prediction model. Exemplarily, the mobile trend prediction model can be implemented based on the TiSASRec (Time Interval Aware Self-Attention for Sequential Recommendation) algorithm.

[0053] In an optional embodiment, the mobile trend prediction model can be trained in the following manner: obtaining the real mobile trend data corresponding to the sample prediction moment; and obtaining the historical positioning sample data and the historical travel sample data at the historical reference moment corresponding to the sample prediction moment; using the historical positioning sample data and the historical travel sample data at the historical reference moment corresponding to the sample prediction moment as training samples, and using the real mobile trend data at the corresponding sample prediction moment as labels, adjusting the network parameters of the pre-constructed mobile trend prediction model until the training cutoff condition is met. Among them, the training cutoff condition may include at least one of the number of training samples reaching a preset number threshold, the number of iterations of model training reaching a preset number threshold, the accuracy of the model reaching a preset accuracy threshold, and the model tending to converge. Among them, the number threshold, the preset number threshold, and the preset accuracy threshold can be set or adjusted by technical personnel according to needs or experience, or determined repeatedly through a large number of experiments, and this application does not impose any restrictions on this.

[0054] S120: Determine, based on the movement trend data, an area to which the first UE is about to travel and a duration for which the first UE is in the area to which it is about to travel.

[0055] Exemplarily, when the first UE moves at a traveling speed along the traveling direction at the current position, the moving area that the first UE is about to reach can be regarded as the upcoming traveling area; based on the traveling speed and the area length of the upcoming traveling area in the traveling direction, the duration of the first UE being in the upcoming traveling area is determined.

[0056] The area duration is used to characterize the predicted duration of the first UE's travel in the area to be traveled.

[0057] Specifically, according to the current position of the first UE, at least one moving area within a preset area around the current position is obtained; in at least one moving area, the moving area that the first UE is about to reach when the first UE moves along the traveling direction at the traveling speed is taken as the upcoming traveling area; the area length of the upcoming traveling area in the traveling direction is obtained; and the ratio of the above area length to the traveling speed is taken as the duration of time that the first UE is in the upcoming traveling area.

[0058] Optionally, the area lengths of different moving areas in different directions may be pre-stored in a network database, and may be retrieved from the network database when needed.

[0059] S130, when the area to be traveled is a communication abnormality area, based on the duration of the area, a communication status message is sent to the second UE; the communication status message is used to prompt the first UE that the communication status is about to be abnormal.

[0060] The communication abnormal area is used to characterize the poor network quality of the corresponding area, for example, the index value of the network evaluation index is less than the minimum index threshold of the corresponding index. The network evaluation index can be at least one, including at least one of network speed and network signal strength, etc. This application does not impose any restrictions on the number and index dimensions of the network evaluation index.

[0061] In an optional embodiment, different mobile areas are pre-marked with network quality conditions; when the network quality conditions of the area to be traveled characterize the area as an area of ​​abnormal communication, a communication status message is generated based on the duration of the area to prompt the first UE that the communication status is about to become abnormal; and the communication status message is sent to the second UE.

[0062] Optionally, a communication status message including the area duration can be generated; or optionally, when the area duration is greater than a preset duration threshold, a communication status message including the area duration is generated; or optionally, when the area duration is greater than a preset duration threshold, a pre-set communication status message is obtained.

[0063] It is worth noting that sending communication status messages can be achieved through at least one of voice, information overlay, SMS and pop-up windows, and this application does not impose any restrictions on the message sending method.

[0064] Exemplarily, the network quality conditions of different mobile areas can be obtained in real time or at regular intervals, and the area category of the corresponding mobile area can be determined according to the network quality conditions of each mobile area; wherein the area category includes abnormal communication areas and normal communication areas; the area category of each mobile area is associated with the corresponding mobile area and stored in the network database. It is worth noting that the computing device that performs the network database data storage operation can be NWDAF or other devices, and this application does not impose any restrictions on this. Accordingly, NWDAF uses the area identifier of the area to be traveled to search the area category of the area to be traveled from the network database; based on the search result of the area category, it is determined whether the area to be traveled is an abnormal communication area.

[0065] The above optional embodiment predicts the movement trend data of the first UE at the current prediction moment based on the historical positioning data and historical travel data of the first UE at the historical reference moment in the case of network communication between the first UE and the second UE, and determines the area that the first UE is about to go to and the duration of the area in which the first UE is about to go based on the movement trend data; in the case that the area to be traveled is an abnormal communication area, based on the duration of the area, a communication status message is sent to the second UE to prompt that the communication status of the first UE is about to become abnormal, so that the second UE can predict the abnormal communication status of the first UE in advance, thereby avoiding abnormal communication conditions such as sudden communication interruption or degradation of communication quality during the network communication between the first UE and the second UE, which affects the communication perception of users of both parties.

[0066] On the basis of the technical solutions of the above-mentioned embodiments, the present application also provides another optional embodiment. In this optional embodiment, the communication status message sending step of S130 is refined.

[0067] See also Figure 2 The steps of sending the communication status message shown include:

[0068] S210, when the upcoming traveling area is a communication abnormality area, determining a communication abnormality cause of the communication abnormality area.

[0069] Among them, the reasons for communication abnormality may include at least one of network instability, low signal strength, network equipment failure, network congestion and weather impact.

[0070] Optionally, the network quality conditions of different moving areas and the causes of network abnormalities when the network quality is poor can be pre-stored in the network database; when the area to be traveled is a communication abnormality area, NWDAF obtains the network abnormality cause corresponding to the area to be traveled from the network database, and uses the network abnormality cause as the communication abnormality cause.

[0071] Alternatively, network quality conditions of different moving areas may be pre-stored in a network database; NWDAF obtains network quality conditions corresponding to the area to be moved from the network database; and performs attribution analysis on the network quality conditions to obtain the cause of the communication abnormality.

[0072] Exemplarily, the network quality situation may include the index value of the network evaluation index at the time to be evaluated; based on a pre-trained attribution analysis model, the index value of the network evaluation index at the time to be evaluated is used as the input data of the attribution analysis model, and the cause of the communication abnormality is determined according to the output of the attribution analysis model. Among them, the attribution analysis model can be implemented based on a traditional machine learning model or a deep learning model, and this application does not impose any restrictions on the model structure of the attribution analysis model.

[0073] Optionally, the attribution analysis model can use the index values ​​of the network evaluation indicators at different sample evaluation times as training samples, and use the communication anomaly causes at the corresponding sample evaluation times as labels to train the pre-built attribution analysis model until the attribution analysis model has the ability to determine the cause of the communication anomaly.

[0074] S220, sending a communication status message to the second UE according to the cause of the communication abnormality and the duration of the area.

[0075] Exemplarily, if the cause of the communication abnormality belongs to the preset warning cause, or the duration of the area is greater than the preset warning duration, a communication status message is sent to the second UE; if the cause of the communication abnormality does not belong to the preset warning cause, and the duration of the area is not greater than the preset warning duration, the communication status message is no longer sent to the second UE. Among them, the preset warning duration can be set or adjusted by the technician according to needs or experience, or repeatedly determined through a large number of experiments, and this application does not impose any limitation on this.

[0076] Exemplarily, the communication anomaly score can be determined according to the cause of the communication anomaly and / or the duration of the area; when the communication anomaly score is greater than the communication warning threshold, a communication status message including the cause of the communication anomaly and the duration of the area is generated, and the communication status message is sent to the second UE. The communication warning threshold can be set or adjusted by the technician according to needs or experience, or repeatedly determined through a large number of experiments, and this application does not impose any limitation on this.

[0077] In an optional implementation, a first mapping relationship between different communication anomaly causes and communication anomaly scores can be pre-constructed; accordingly, based on the first mapping relationship, the communication anomaly cause of the first UE in the area to be traveled and the corresponding communication anomaly score are determined; when the communication anomaly score is greater than the communication warning threshold, a communication status message including the communication anomaly cause and the duration of the area is generated.

[0078] In another optional implementation, a second mapping relationship between the duration of different areas and the communication anomaly score can be pre-constructed; accordingly, based on the second mapping relationship, the cause of the communication anomaly in the area to be traveled by the first UE and the corresponding communication anomaly score are determined; when the communication anomaly score is greater than the communication warning threshold, a communication status message including the cause of the communication anomaly and the duration of the area is generated.

[0079] In another optional implementation, a third mapping relationship between different communication anomaly causes, area durations and communication anomaly scores can be pre-constructed; accordingly, based on the third mapping relationship, the communication anomaly cause and area duration of the first UE in the area to be traveled, and the corresponding communication anomaly score are determined; when the communication anomaly score is greater than the communication warning threshold, a communication status message including the communication anomaly cause and area duration is generated.

[0080] In another optional implementation, communication anomaly scores corresponding to different communication anomaly causes and communication anomaly scores corresponding to the duration of the area can be pre-set; the communication anomaly scores corresponding to the communication anomaly causes of the first UE in the area to be traveled and the communication anomaly scores corresponding to the duration of the area are weighted and summed; the weighted result is used as the final communication anomaly score as a basis for judging whether a subsequent communication status message is generated.

[0081] It can be understood that by introducing the cause of the communication anomaly and / or the duration of the area, the communication anomaly score is determined, and based on the relationship between the communication anomaly score and the communication warning threshold, the generation of the communication status message is controlled, thereby avoiding the generation of unnecessary communication status messages, resulting in a waste of computing resources, and the sending of unnecessary communication status messages, resulting in the occupation of communication bandwidth resources.

[0082] In the above optional embodiment, the cause of the communication abnormality in the communication abnormality area is determined only when the area to be traveled is a communication abnormality area, and the communication status message sent to the second UE is comprehensively determined based on the cause of the communication abnormality and the duration of the area, so that the information carried in the communication status message is richer, which is convenient for the user of the second UE to fully understand the communication status with the first UE, and provides a strong basis for whether to maintain a call with the first UE.

[0083] Based on the technical solutions of the above embodiments, the present application also provides another optional embodiment, in which the steps for determining the area to be traveled and the duration of the area are refined.

[0084] See also Figure 3 The steps for determining the upcoming travel area and the duration of the area shown include:

[0085] S310, obtaining historical movement trend data predicted at different historical prediction moments within a historical verification period and corresponding real movement trend data; the historical verification period is a neighborhood period before the current prediction moment.

[0086] The number of historical prediction moments is at least one. Exemplarily, the NWDAF may obtain the real movement trend data of the first UE in real time or periodically.

[0087] S320, determining the prediction accuracy according to the consistency between the historical movement trend data at each historical prediction moment in the historical verification period and the corresponding real movement trend data.

[0088] Exemplarily, for each historical prediction moment, determine whether the historical mobile trend data predicted at the historical prediction moment and the corresponding actual mobile trend data are consistent; determine the prediction accuracy based on the proportion of consistent data within the historical verification period, or based on the proportion of inconsistent data within the historical verification period.

[0089] Among them, the prediction accuracy can characterize the prediction ability of the mobile trend data in the historical verification period, and accordingly, it can map whether the predicted mobile trend data at the current prediction moment is credible.

[0090] S330: When the prediction accuracy is greater than a preset accuracy threshold, determine the area to which the first UE is about to travel and the duration of time that the first UE is in the area to which it is about to travel according to the movement trend data.

[0091] Among them, the preset accuracy threshold can be set or adjusted by technicians according to needs or experience, or repeatedly determined through a large number of experiments, and this application does not impose any limitation on this.

[0092] When the prediction accuracy is greater than the preset accuracy threshold, it is determined that the predicted mobility trend data at the current prediction moment is relatively accurate. At this time, the area to which the first UE is about to travel and the duration of time the first UE is in the area to which it is about to travel can be determined based on the mobility trend data. When the prediction accuracy is not greater than the preset accuracy threshold, it is determined that the predicted mobility trend data at the current prediction moment is not accurate enough. At this time, subsequent calculations are no longer performed based on the mobility trend data to avoid unnecessary increase in the amount of calculations.

[0093] In an optional implementation, when the mobile trend data is determined based on a pre-trained mobile trend prediction model, the prediction accuracy can reflect the model accuracy of the mobile trend prediction model. When the prediction accuracy is greater than the prediction accuracy threshold, it indicates that the prediction accuracy of the mobile trend prediction model meets the prediction requirements, and the subsequent area to be traveled and the duration of the area can be determined directly based on the mobile trend data. Optionally, when the prediction accuracy is not greater than the prediction accuracy threshold, it indicates that the prediction accuracy of the mobile prediction trend model cannot meet the prediction requirements, and at this time, the subsequent area to be traveled and the duration of the area are no longer determined based on the mobile trend data.

[0094] Furthermore, when the prediction accuracy is not greater than the prediction accuracy threshold, the network parameters of the trained mobile trend prediction model can be adjusted according to the historical positioning data and the corresponding historical travel data at the historical reference time corresponding to each historical prediction time, and the real mobile trend data at the corresponding historical prediction time, so as to optimize the mobile trend prediction model. Accordingly, based on the adjusted mobile trend prediction model, the mobile trend data of the first UE at the current prediction time is predicted again according to the historical positioning data and the historical travel data of the first UE at the historical reference time, so as to improve the prediction result accuracy of the mobile trend data at the current prediction time.

[0095] Exemplarily, for each historical prediction moment, the historical positioning data and corresponding historical travel data at the historical reference moment corresponding to the historical prediction moment are used as secondary training samples, and the real mobile trend data at the corresponding historical prediction moment is used as training labels, and the trained mobile trend prediction model is trained again to adjust the network parameters of the trained mobile prediction model, thereby achieving the purpose of model optimization. Accordingly, the optimized mobile prediction model is used to re-predict the mobile trend data of the first UE at the current prediction moment based on the historical positioning data and historical travel data of the first UE at the historical reference moment, thereby improving the accuracy of the prediction result.

[0096] In the above optional embodiment, by introducing relevant data at historical prediction moments when the prediction accuracy is not greater than the prediction accuracy threshold, the trained mobile trend prediction model is optimized for the second time, thereby improving the model accuracy of the mobile trend prediction model. Then, based on the optimized mobile trend prediction model, the mobile trend data at the current prediction moment is determined again, and the re-determined mobile trend data is used as the basis for determining the subsequent area to be traveled and the duration of the area, thereby laying the foundation for the accurate determination of the subsequent area to be traveled and the duration of the area, thereby helping to improve the accuracy of the determination results of the communication status message.

[0097] The above-mentioned optional embodiment determines the prediction accuracy by introducing data within the historical verification period related to the current prediction moment, which indirectly characterizes the accuracy of the movement trend data at the current prediction moment, and only when the prediction accuracy is greater than the prediction accuracy threshold, the subsequent determination of the upcoming travel area and the duration of the area is performed, thereby ensuring the accuracy of the determined upcoming travel area and the duration of the area.

[0098] On the basis of the technical solutions of the above-mentioned embodiments, the present application also provides an optional embodiment, in which the UE communication status prompt method is described in detail from the perspective of multi-party interaction.

[0099] See also Figure 4AThe UE communication status prompt method shown includes:

[0100] S401, a first UE and a second UE perform network communication based on an application;

[0101] S402, during the network communication process, the NWDAF periodically obtains the area length information, regional network status and communication abnormality reasons of each mobile area, and updates the network database;

[0102] Among them, each moving area may include a mountainous area and a tunnel light area.

[0103] S403, during the network communication process, the NWDAF periodically obtains the area type of each mobile area and the historical travel speed of the first UE, and updates the map and traffic database;

[0104] S404, during the network communication process, the NWDAF periodically obtains the TAI and Cell ID of the first UE and updates the user database;

[0105] S405, the NWDAF obtains the TAI and Cell ID of the first UE at the historical reference time from the user database, and obtains the historical travel speed of the first UE at the historical reference time from the map and traffic condition database;

[0106] S406, the NWDAF predicts the mobility trend data at the current prediction time based on the mobility trend prediction model and the TAI, CellID and historical travel speed of the first UE at the historical reference time;

[0107] S407, NWDAF obtains the real movement trend data of the first UE at each historical prediction time in the neighborhood period before the current prediction time from the map and traffic condition database;

[0108] S408, NWDAF determines the prediction accuracy of the mobility trend prediction model according to the real mobility trend data at each historical prediction moment and the predicted historical mobility trend data;

[0109] S409, NWDAF determines whether the prediction accuracy is greater than a preset accuracy threshold; if not, execute S410; if yes, execute S411;

[0110] S410, NWDAF optimizes the mobility trend prediction model using the TAI, Cell ID and historical travel speed at the historical prediction time corresponding to the historical reference time as training samples and the real mobility trend data at the historical prediction time as labels; and continues to execute S406;

[0111] S411, the NWDAF determines the area to which the first UE will travel according to the travel direction in the movement trend data;

[0112] S412, NWDAF obtains the length of the area to be traveled in the travel direction and the cause of the communication abnormality of the area to be traveled from the network database;

[0113] S413, NWDAF determines the duration of the area according to the ratio of the area length and the moving speed;

[0114] See also Figure 4B The schematic diagram of the regional network status and regional length of each mobile area shown in the figure can determine the area to be traveled to by the first UE according to the travel direction, and determine the duration of the area according to the ratio of the area length L and the moving speed v .

[0115] S414, when the area to be traveled is a communication abnormal area, the NWDAF determines whether the duration of the area is greater than a preset duration threshold; if so, execute S415; otherwise, return to execute S405.

[0116] S415, generating a communication status message indicating that the communication status of the first UE is about to become abnormal according to the cause of the communication abnormality and the duration of the area;

[0117] S416, NWDAF sends a communication status message to the second UE.

[0118] Exemplarily, NWDAF can also generate a network analysis report based on data such as the cause of the communication anomaly and the duration of the regional anomaly, and send it to the second UE together with the communication status message, so that the second UE can fully understand the network communication status of the first UE.

[0119] Optionally, the NWDAF may also synchronize the communication status message and / or the network analysis report to the first UE.

[0120] It is worth noting that sending communication status messages can be achieved through at least one of voice, information overlay, SMS and pop-up windows, and this application does not impose any restrictions on the message sending method.

[0121] It should be understood that, although the steps in the flowcharts involved in the above 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 is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may 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 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.

[0122] Based on the same inventive concept, the embodiment of the present application also provides a UE communication status prompting device for implementing the UE communication status prompting 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 UE communication status prompting device embodiments provided below can refer to the limitations of the UE communication status prompting method above, and will not be repeated here.

[0123] In one embodiment, Figure 5 As shown, a UE communication status prompting device is provided, comprising: a prediction module 510, a determination module 520 and a sending module 530. Among them,

[0124] A prediction module 510, configured to predict movement trend data of the first UE at a current prediction time based on historical positioning data and historical travel data of the first UE at a historical reference time in a case where the first UE and the second UE are based on network communication;

[0125] A determination module 520, configured to determine, based on the movement trend data, an area to which the first UE is about to travel and a duration of time that the first UE is in the area to which it is about to travel;

[0126] A sending module 530, configured to send a communication status message to a second UE based on the duration of the area when the area to be traveled is a communication abnormal area;

[0127] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0128] In one of the embodiments, the sending module 530 includes: a first determination unit, used to determine the cause of the communication abnormality in the communication abnormality area when the area to be traveled is a communication abnormality area; a sending unit, used to send a communication status message to the second UE according to the cause of the communication abnormality and the duration of the area.

[0129] In one of the embodiments, the sending unit includes: a first determination subunit, used to determine a communication anomaly score based on the cause of the communication anomaly and / or the duration of the area; a sending subunit, used to generate a communication status message including the cause of the communication anomaly and the duration of the area when the communication anomaly score is greater than a communication warning threshold, and send the communication status message to the second UE.

[0130] In one embodiment, the determination module 520 includes: an acquisition unit, used to acquire historical mobile trend data and corresponding real mobile trend data predicted at different historical prediction moments within a historical verification period; the historical verification period is a neighborhood period before the current prediction moment; a second determination unit, used to determine the prediction accuracy according to the consistency between the historical mobile trend data and the corresponding real mobile trend data at each historical prediction moment within the historical verification period; a third determination unit, used to determine the upcoming travel area of ​​the first UE and the duration of the first UE in the upcoming travel area according to the mobile trend data when the prediction accuracy is greater than a preset accuracy threshold.

[0131] In one of the embodiments, the prediction module 510 is specifically used to predict the mobility trend data of the first UE at the current prediction moment based on the trained mobility trend prediction model and the historical positioning data and the historical travel data of the first UE at the historical reference moment; accordingly, the device also includes an adjustment module for adjusting the network parameters of the trained mobility trend prediction model according to the historical positioning data and the corresponding historical travel data at the historical reference moment corresponding to each historical prediction moment, and the actual mobility trend data at the corresponding historical prediction moment when the prediction accuracy is not greater than a preset accuracy threshold; the prediction module 510 is also used to re-predict the mobility trend data of the first UE at the current prediction moment based on the historical positioning data and the historical travel data of the first UE at the historical reference moment based on the adjusted mobility trend prediction model.

[0132] In one of the embodiments, the determination module 520 includes: an area determination unit, which is used to determine the moving area that the first UE is about to arrive at as the upcoming travel area when the first UE moves at a travel speed along the travel direction at a current position; and a duration determination unit, which is used to determine the duration of the area that the first UE is in the upcoming travel area based on the travel speed and the area length of the upcoming travel area in the travel direction.

[0133] Each module in the above-mentioned UE communication status prompt 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 operations corresponding to each of the above modules.

[0134] In one embodiment, a communication device is provided. The communication device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The communication device includes a processor, a memory, a network interface and a transceiver connected through a system bus. Among them, 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 transceiver of the communication device is used to perform operations of receiving data or sending data under the control of the processor. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the communication device is used to store data such as uplink text messages and downlink text messages. The network interface of the communication device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a UE communication status prompt method is implemented.

[0135] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0136] In one embodiment, a communication device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the processing logic in the computer program, the following steps are implemented:

[0137] In case of network-based communication between the first UE and the second UE, predicting the movement trend data of the first UE at a current prediction time based on the historical positioning data and the historical travel data of the first UE at a historical reference time;

[0138] Determine, according to the movement trend data, an area to which the first UE is about to travel and a duration of time that the first UE is in the area to which it is about to travel;

[0139] When the area to be traveled is a communication abnormal area, based on the duration of the area, a communication status message is sent to the second UE;

[0140] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0141] In one embodiment, when the processor executes the processing logic in the computer program, the following steps are also implemented: when the area to be traveled is a communication abnormality area, determining the cause of the communication abnormality in the communication abnormality area; and sending a communication status message to the second UE based on the cause of the communication abnormality and the duration of the area.

[0142] In one embodiment, when the processor executes the processing logic in the computer program, the following steps are also implemented: determining a communication anomaly score based on the cause of the communication anomaly and / or the duration of the area; when the communication anomaly score is greater than the communication warning threshold, generating a communication status message including the cause of the communication anomaly and the duration of the area, and sending the communication status message to the second UE.

[0143] In one embodiment, when the processor executes the processing logic in the computer program, the following steps are also implemented: obtaining historical mobile trend data and corresponding real mobile trend data predicted at different historical prediction moments within a historical verification period; the historical verification period is a neighborhood period before the current prediction moment; according to the consistency degree of the historical mobile trend data and the corresponding real mobile trend data at each historical prediction moment within the historical verification period, the prediction accuracy is determined; when the prediction accuracy is greater than a preset accuracy threshold, the area to which the first UE is about to travel and the duration of time that the first UE is in the area to which it is about to travel are determined according to the mobile trend data.

[0144] In one embodiment, when the processor executes the processing logic in the computer program, the following steps are also implemented: based on the trained mobile trend prediction model, according to the historical positioning data and historical travel data of the first UE at the historical reference time, predict the mobile trend data of the first UE at the current prediction time; when the prediction accuracy is not greater than the preset accuracy threshold, according to the historical positioning data and the corresponding historical travel data at the historical reference time corresponding to each historical prediction time, and the actual mobile trend data at the corresponding historical prediction time, adjust the network parameters of the trained mobile trend prediction model; based on the adjusted mobile trend prediction model, re-predict the mobile trend data of the first UE at the current prediction time according to the historical positioning data and the historical travel data of the first UE at the historical reference time.

[0145] In one embodiment, when the processor executes the processing logic in the computer program, the following steps are also implemented: when the first UE moves at the traveling speed along the traveling direction at the current position, the moving area that the first UE is about to reach is regarded as the upcoming traveling area; according to the traveling speed and the area length of the upcoming traveling area in the traveling direction, the duration of the first UE being in the upcoming traveling area is determined.

[0146] In one embodiment, a computer readable storage medium or a computer program product is provided, on which a computer program is stored, and when the processing logic in the computer program is executed by a processor, the following steps are implemented:

[0147] In case of network-based communication between the first UE and the second UE, predicting the movement trend data of the first UE at a current prediction time based on the historical positioning data and the historical travel data of the first UE at a historical reference time;

[0148] Determine, according to the movement trend data, an area to which the first UE is about to travel and a duration of time that the first UE is in the area to which it is about to travel;

[0149] When the area to be traveled is a communication abnormal area, based on the duration of the area, a communication status message is sent to the second UE;

[0150] The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

[0151] In one embodiment, the processing logic in the computer program also implements the following steps when executed by the processor: when the area to be traveled is a communication abnormality area, determining the cause of the communication abnormality in the communication abnormality area; and sending a communication status message to the second UE based on the cause of the communication abnormality and the duration of the area.

[0152] In one embodiment, when the processing logic in the computer program is executed by the processor, the following steps are also implemented: determining a communication anomaly score based on the cause of the communication anomaly and / or the duration of the area; when the communication anomaly score is greater than the communication warning threshold, generating a communication status message including the cause of the communication anomaly and the duration of the area, and sending the communication status message to the second UE.

[0153] In one embodiment, the processing logic in the computer program also implements the following steps when executed by the processor: obtaining historical mobile trend data and corresponding real mobile trend data predicted at different historical prediction moments within a historical verification period; the historical verification period is a neighborhood period before the current prediction moment; determining the prediction accuracy based on the consistency between the historical mobile trend data and the corresponding real mobile trend data at each historical prediction moment within the historical verification period; when the prediction accuracy is greater than a preset accuracy threshold, determining the area where the first UE is about to travel and the duration of time that the first UE is in the area where it is about to travel based on the mobile trend data.

[0154] In one embodiment, the processing logic in the computer program also implements the following steps when executed by the processor: based on the trained mobile trend prediction model, predict the mobile trend data of the first UE at the current prediction moment according to the historical positioning data and historical travel data of the first UE at the historical reference moment; when the prediction accuracy is not greater than the preset accuracy threshold, adjust the network parameters of the trained mobile trend prediction model according to the historical positioning data and corresponding historical travel data at the historical reference moment corresponding to each historical prediction moment, and the actual mobile trend data at the corresponding historical prediction moment; based on the adjusted mobile trend prediction model, re-predict the mobile trend data of the first UE at the current prediction moment according to the historical positioning data and historical travel data of the first UE at the historical reference moment.

[0155] In one embodiment, the processing logic in the computer program also implements the following steps when executed by the processor: when the first UE moves at a traveling speed along the traveling direction at the current position, the moving area that the first UE is about to reach is regarded as the upcoming traveling area; based on the traveling speed and the area length of the upcoming traveling area in the traveling direction, determine the duration of the first UE being in the upcoming traveling area.

[0156] 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.

[0157] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments 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 this application can include at least one of non-volatile 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), magnetoresistive 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, etc., but are not limited to this.

[0158] 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 specification.

[0159] The above 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 method for prompting a user equipment UE communication status, characterized in that: include: In the case of network-based communication between the first UE and the second UE, predicting the movement trend data of the first UE at a current prediction time according to the historical positioning data and the historical travel data of the first UE at a historical reference time; Determine, according to the movement trend data, an area to which the first UE is about to travel and a duration for which the first UE is in the area to which it is about to travel; In the case where the area to be traveled is a communication abnormality area, sending a communication status message to the second UE based on the duration of the area; The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

2. The method according to claim 1, characterized in that When the area to be traveled is a communication abnormal area, sending a communication status message to the second UE based on the duration of the area includes: In the case that the area to be traveled is a communication abnormality area, determining a communication abnormality cause of the communication abnormality area; Send a communication status message to the second UE according to the cause of the communication abnormality and the duration of the area.

3. The method according to claim 2, characterized in that The sending a communication status message to the second UE according to the communication abnormality cause and the duration of the area includes: Determine a communication anomaly score according to the cause of the communication anomaly and / or the duration of the area; In a case where the communication anomaly score is greater than a communication warning threshold, a communication status message including the communication anomaly cause and the duration of the area is generated, and the communication status message is sent to the second UE.

4. The method according to claim 1, characterized in that The determining, according to the movement trend data, an area to which the first UE is about to travel and a duration for which the first UE is in the area to which it is about to travel comprises: Obtaining historical movement trend data predicted at different historical prediction moments within a historical verification period and corresponding real movement trend data; the historical verification period is a neighborhood period before the current prediction moment; Determining the prediction accuracy according to the consistency between the historical movement trend data and the corresponding real movement trend data at each historical prediction moment in the historical verification period; In a case where the prediction accuracy is greater than a preset accuracy threshold, the area to which the first UE is about to travel and the duration of time that the first UE is in the area to which it is about to travel are determined according to the movement trend data.

5. The method according to claim 4, characterized in that The predicting, according to the historical positioning data and the historical travel data of the first UE, the movement trend data of the first UE includes: Based on the trained mobility trend prediction model, according to the historical positioning data and historical travel data of the first UE at the historical reference time, predicting the mobility trend data of the first UE at the current prediction time; Accordingly, the method further includes: When the prediction accuracy is not greater than the preset accuracy threshold, adjusting the network parameters of the trained movement trend prediction model according to the historical positioning data and the corresponding historical travel data at the historical reference moments corresponding to each historical prediction moment, and the real movement trend data at the corresponding historical prediction moment; Based on the adjusted mobility trend prediction model, the mobility trend data of the first UE at the current prediction moment is re-predicted based on the historical positioning data and historical travel data of the first UE at the historical reference moment.

6. The method according to any one of claims 1 to 5, characterized in that: The movement trend data includes a travel direction and a travel speed; The determining, according to the movement trend data, an area to which the first UE is about to travel and a duration for which the first UE is in the area to which it is about to travel comprises: When the first UE moves along the traveling direction at the traveling speed at the current position, taking the moving area that the first UE is about to reach as the upcoming traveling area; The duration of time that the first UE is in the area to be traveled is determined according to the travel speed and the area length of the area to be traveled in the travel direction.

7. A UE communication status prompting device, characterized in that: include: A prediction module, configured to predict movement trend data of the first UE at a current prediction time based on historical positioning data and historical travel data of the first UE at a historical reference time when the first UE and the second UE are in communication based on a network; A determination module, configured to determine, according to the movement trend data, an area to which the first UE is about to travel and a duration for which the first UE is in the area to which it is about to travel; A sending module, configured to send a communication status message to the second UE based on the duration of the area when the area to be traveled is a communication abnormal area; The communication status message is used to prompt that the communication status of the first UE is about to become abnormal.

8. A communication device, comprising a memory, a transceiver and a processor, wherein the memory stores a computer program, characterized in that: The transceiver is used to receive data or send data under the control of the processor, and the processor implements the steps of the method described in any one of claims 1 to 6 when executing the computer program.

9. 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 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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