Application network optimization methods, apparatus, devices, and storage media

Abstract

This application discloses a network optimization method, apparatus, device, and storage medium for an application, belonging to the field of network technology. The method includes: acquiring geomagnetic information and signal quality information of a target terminal; determining the target preset line where the target terminal is located and the target reference point corresponding to the target terminal based on the geomagnetic information of the target terminal and the reference geomagnetic information of reference points included in multiple preset lines; obtaining the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and a trained signal strength prediction model; and optimizing the network of the target application running in the foreground on the target terminal based on the signal strength information and a preset optimization strategy. Using this application can avoid situations where signal strength rapidly decreases or even disappears, thereby improving the user's experience of using the terminal network.

Description

Technical Field

[0001] This application relates to the field of network technology, and in particular to a method, apparatus, device, and storage medium for optimizing network applications. Background Technology

[0002] Mobile phones, tablets and other terminals have become indispensable devices in people's lives, and people's demands for the signal quality of wireless communication on these terminals are also increasing.

[0003] Current wireless communication methods involve establishing multiple base stations, each corresponding to a specific cell range. Within this cell range, a terminal can establish a wireless communication connection with the corresponding base station, thereby enabling wireless communication for the terminal.

[0004] However, this wireless communication method also has certain limitations. When users travel along a certain route, there may be situations such as a sudden drop in signal strength. For example, when users commute by public transportation and travel quickly from a location close to the base station to a location far away from the base station, there may be a rapid drop in signal strength or even a loss of signal, which brings great inconvenience to people's lives. Summary of the Invention

[0005] This application provides a network optimization method for an application, which can solve the technical problems existing in the prior art.

[0006] In a first aspect, a network optimization method for an application is provided, the method comprising:

[0007] Acquire geomagnetic information and signal quality information of the target terminal;

[0008] Based on the geomagnetic information of the target terminal and the reference geomagnetic information of the reference points included in multiple preset lines, the target preset line where the target terminal is located and the target reference point corresponding to the target terminal are determined.

[0009] Based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0010] Based on the signal strength information of the target terminal at the next reference point and the preset optimization strategy, network optimization is performed on the target application running in the foreground state in the target terminal.

[0011] In one possible implementation, the preset route includes at least one of road routes, bus routes, subway lines, and train lines.

[0012] In one possible implementation, the reference points include multiple coordinate points evenly distributed along the preset route at preset route distances.

[0013] In one possible implementation, acquiring the geomagnetic information and signal quality information of the target terminal includes:

[0014] The geomagnetic intensity, magnetic heading angle, and signal quality parameters of the target terminal's location are periodically acquired.

[0015] Based on the obtained geomagnetic intensity and magnetic heading angle at a preset number of locations, the geomagnetic information is generated, wherein the geomagnetic information includes a geomagnetic gradient sequence and a magnetic heading angle sequence. The geomagnetic gradient sequence is a sequence composed of the difference between the geomagnetic intensity of every two adjacent locations in the preset number of locations, and the magnetic heading angle sequence is a sequence composed of the magnetic heading angles at the preset number of locations.

[0016] Based on the obtained signal quality parameters of a preset number of locations, the signal quality information is generated, wherein the signal quality information includes a sequence of signal quality parameters of a preset number of locations.

[0017] In one possible implementation, the reference geomagnetic information of the reference points included in the preset route includes a reference geomagnetic gradient sequence and a reference magnetic heading angle sequence corresponding to multiple reference points in the preset route;

[0018] The step of determining the target preset line where the target terminal is located and the target reference point corresponding to the target terminal based on the geomagnetic information of the target terminal and the reference geomagnetic information of reference points included in multiple preset lines includes:

[0019] For each preset number of adjacent reference points in the reference geomagnetic gradient sequence corresponding to each preset line, determine the first degree of difference between the geomagnetic gradient sequence corresponding to the preset number of positions and the reference geomagnetic gradient subsequence.

[0020] For each preset route, in the reference magnetic heading angle sequence corresponding to a preset number of adjacent reference points, a second degree of difference is determined between the magnetic heading angle sequence corresponding to the preset number of positions and the reference magnetic heading angle subsequence.

[0021] For each preset number of adjacent reference points, the total difference between the preset number of locations and the preset number of adjacent reference points is determined based on the first difference degree, the second difference degree, the geomagnetic weight value, and the magnetic heading angle weight value corresponding to the preset number of reference points.

[0022] Based on the total difference between the preset number of locations and the preset number of adjacent reference points in each preset line, the target preset line where the preset number of locations are located and the preset number of adjacent target reference points corresponding to the preset number of locations are determined.

[0023] In one possible implementation, determining the target preset line where the preset number of locations are located and the preset number of adjacent target reference points corresponding to the preset number of locations, based on the total difference value between the preset number of locations and the preset number of adjacent reference points in each preset line, includes:

[0024] The minimum value is selected from multiple total difference values. If the minimum value is less than a preset difference threshold, the preset route corresponding to the minimum value is determined as the target preset route, and the preset number of adjacent reference points corresponding to the minimum value are determined as target reference points.

[0025] In one possible implementation, the signal quality parameters include at least one of signal received power, reference signal received quality, channel quality indication, signal-to-interference-plus-noise ratio, and physical cell identifier.

[0026] In one possible implementation, obtaining the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model includes:

[0027] Based on the reference geomagnetic information and location coordinates of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0028] In one possible implementation, the signal strength information is a signal strength value or a signal strength level.

[0029] In one possible implementation, the trained signal strength prediction model includes a trained early peak prediction model, a trained late peak prediction model, and a trained off-peak prediction model.

[0030] The method of obtaining the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model includes:

[0031] When the current time point is within the morning peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained morning peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained;

[0032] When the current time point is within the evening peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained evening peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained;

[0033] When the current time point is within the off-peak time period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained off-peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0034] In one possible implementation, the method further includes:

[0035] Acquire sample input data and reference output data, wherein the sample input data includes reference geomagnetic information of sample reference points included in the preset line and signal quality information corresponding to the sample reference points, and the reference output data includes actual signal strength information of the next reference point of the sample reference point;

[0036] Based on the reference geomagnetic information of the sample reference points, the signal quality information corresponding to the sample reference points, and the signal strength prediction model to be trained, the prediction output data is obtained.

[0037] Based on the baseline output data and the predicted output data, the parameters of the signal strength prediction model to be trained are adjusted to obtain the trained signal strength prediction model.

[0038] Secondly, a network optimization device for an application is provided, the device comprising:

[0039] The acquisition module is used to acquire the geomagnetic information and signal quality information of the target terminal;

[0040] The first determining module is used to determine the target preset line where the target terminal is located and the target reference point corresponding to the target terminal based on the geomagnetic information of the target terminal and the reference geomagnetic information of the reference points included in multiple preset lines;

[0041] The second determining module is used to obtain the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model.

[0042] The optimization module is used to perform network optimization on the target application running in the foreground state of the target terminal based on the signal strength information of the target terminal at the next reference point and the preset optimization strategy.

[0043] In one possible implementation, the preset route includes at least one of road routes, bus routes, subway lines, and train lines.

[0044] In one possible implementation, the reference points include multiple coordinate points evenly distributed along the preset route at preset route distances.

[0045] In one possible implementation, the acquisition module is configured to:

[0046] The geomagnetic intensity, magnetic heading angle, and signal quality parameters of the target terminal's location are periodically acquired.

[0047] Based on the obtained geomagnetic intensity and magnetic heading angle at a preset number of locations, the geomagnetic information is generated, wherein the geomagnetic information includes a geomagnetic gradient sequence and a magnetic heading angle sequence. The geomagnetic gradient sequence is a sequence composed of the difference between the geomagnetic intensity of every two adjacent locations in the preset number of locations, and the magnetic heading angle sequence is a sequence composed of the magnetic heading angles at the preset number of locations.

[0048] Based on the obtained signal quality parameters of a preset number of locations, the signal quality information is generated, wherein the signal quality information includes a sequence of signal quality parameters of a preset number of locations.

[0049] In one possible implementation, the reference geomagnetic information of the reference points included in the preset route includes a reference geomagnetic gradient sequence and a reference magnetic heading angle sequence corresponding to multiple reference points in the preset route;

[0050] The first determining module is used for:

[0051] For each preset number of adjacent reference points in the reference geomagnetic gradient sequence corresponding to each preset line, determine the first degree of difference between the geomagnetic gradient sequence corresponding to the preset number of positions and the reference geomagnetic gradient subsequence.

[0052] For each preset route, in the reference magnetic heading angle sequence corresponding to a preset number of adjacent reference points, a second degree of difference is determined between the magnetic heading angle sequence corresponding to the preset number of positions and the reference magnetic heading angle subsequence.

[0053] For each preset number of adjacent reference points, the total difference between the preset number of locations and the preset number of adjacent reference points is determined based on the first difference degree, the second difference degree, the geomagnetic weight value, and the magnetic heading angle weight value corresponding to the preset number of reference points.

[0054] Based on the total difference between the preset number of locations and the preset number of adjacent reference points in each preset line, the target preset line where the preset number of locations are located and the preset number of adjacent target reference points corresponding to the preset number of locations are determined.

[0055] In one possible implementation, the first determining module is configured to:

[0056] The minimum value is selected from multiple total difference values. If the minimum value is less than a preset difference threshold, the preset route corresponding to the minimum value is determined as the target preset route, and the preset number of adjacent reference points corresponding to the minimum value are determined as target reference points.

[0057] In one possible implementation, the signal quality parameters include at least one of signal received power, reference signal received quality, channel quality indication, signal-to-interference-plus-noise ratio, and physical cell identifier.

[0058] In one possible implementation, the second determining module is configured to:

[0059] Based on the reference geomagnetic information and location coordinates of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0060] In one possible implementation, the signal strength information is a signal strength value or a signal strength level.

[0061] In one possible implementation, the trained signal strength prediction model includes a trained early peak prediction model, a trained late peak prediction model, and a trained off-peak prediction model.

[0062] The second determining module is used for:

[0063] When the current time point is within the morning peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained morning peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained;

[0064] When the current time point is within the evening peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained evening peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained;

[0065] When the current time point is within the off-peak time period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained off-peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0066] In one possible implementation, the device further includes a training module for:

[0067] Acquire sample input data and reference output data, wherein the sample input data includes reference geomagnetic information of sample reference points included in the preset line and signal quality information corresponding to the sample reference points, and the reference output data includes actual signal strength information of the next reference point of the sample reference point;

[0068] Based on the reference geomagnetic information of the sample reference points, the signal quality information corresponding to the sample reference points, and the signal strength prediction model to be trained, the prediction output data is obtained.

[0069] Based on the baseline output data and the predicted output data, the parameters of the signal strength prediction model to be trained are adjusted to obtain the trained signal strength prediction model.

[0070] Thirdly, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, the instruction being loaded and executed by the processor to implement the operation performed by the network optimization method of the application.

[0071] Fourthly, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the instruction being loaded and executed by a processor to implement the operations performed by the network optimization method of an application.

[0072] Fifthly, a computer program product is provided, the computer program product including at least one instruction, the at least one instruction being loaded and executed by a processor to implement the operations performed by the network optimization method of the application.

[0073] The beneficial effects of the technical solution provided in this application are as follows: The solution mentioned in this application can determine the target preset line where the target terminal is currently located and the target reference point corresponding to the target terminal based on the obtained geomagnetic information of the target terminal and the reference geomagnetic information of multiple preset lines including reference points. Then, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the quality information of the target terminal, and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained. Finally, based on the signal strength information of the target terminal at the next reference point and the preset optimization strategy, network optimization is performed on the target application running in the foreground state in the target terminal. By using this application, the signal strength at the next reference point that the target terminal will reach can be predicted. The target application can be optimized by using the predicted signal strength information to avoid the situation of rapid signal strength reduction or even loss, thereby improving the user's experience of using the terminal network. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a flowchart of a network optimization method for an application provided in an embodiment of this application;

[0076] Figure 2 This is a schematic diagram of a preset route and reference point provided in an embodiment of this application;

[0077] Figure 3 This is a schematic diagram illustrating the location of a preset route, reference point, and target terminal provided in an embodiment of this application;

[0078] Figure 4 This is a schematic diagram of a method for determining a target preset route and a target reference point provided in an embodiment of this application;

[0079] Figure 5 This is a schematic diagram of a training method for a signal strength prediction model provided in an embodiment of this application;

[0080] Figure 6This is a schematic diagram of the structure of a network optimization device for an application provided in an embodiment of this application;

[0081] Figure 7 This is a structural block diagram of a terminal provided in an embodiment of this application;

[0082] Figure 8 This is a structural block diagram of a server provided in an embodiment of this application. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0084] This application provides a network optimization method for an application, which can be implemented by a computer device. The computer device can be a terminal or server, and the terminal can be a desktop computer, laptop computer, tablet computer, mobile phone, etc.

[0085] Computer equipment may include processors, memory, input components, output components, communication components, etc.

[0086] The processor can be a central processing unit (CPU). The processor can be used to read instructions and process data. For example, it can acquire the geomagnetic information and signal quality information of the target terminal, determine the target preset line where the target terminal is located and the target reference point corresponding to the target terminal, determine the signal strength information of the target terminal at the next reference point on the target preset line, and perform network optimization on the target application running in the foreground state in the target terminal based on the signal strength information and preset optimization strategies, etc.

[0087] The memory can be various types of volatile or non-volatile memory, such as solid-state drives (SSDs) and dynamic random access memory (DRAM). The memory can be used for data storage, for example, storing the acquired geomagnetic information and signal quality information of the target terminal; storing the target preset line where the target terminal is located and the target reference point corresponding to the target terminal; storing the signal strength information of the target terminal at the next reference point on the target preset line; storing the relevant data of the trained signal strength prediction model; storing the relevant data of the preset optimization strategy, and so on.

[0088] Communication components can be wired network connectors, wireless fidelity (WiFi) modules, Bluetooth modules, cellular network communication modules, etc.

[0089] This application provides a network optimization method for an application, applicable to scenarios where a user is walking or riding in a vehicle along a preset route. When a user is traveling along a preset route, the terminal's signal strength may suddenly decrease or even be lost. This application's network optimization method can predict the signal strength at the next reference point the user (or terminal) is about to reach. Based on the predicted signal strength information, the network of the target application currently running on the terminal is optimized, making the network of the target application more optimized and avoiding lag or signal loss, thus improving the user experience.

[0090] Figure 1 This is a flowchart of a network optimization method for an application provided in an embodiment of this application. See also... Figure 1 This embodiment includes:

[0091] Before performing step 101, multiple preset routes need to be set. The preset routes may include at least one of road routes, bus routes, subway routes and train routes. Of course, other transportation routes may also be included, but this embodiment does not limit this.

[0092] For each preset route, multiple reference points can be determined on the preset route. These reference points can include multiple coordinate points evenly distributed along the preset route according to the preset route distance. That is, the distance along the preset route between any two adjacent reference points is the preset route distance. Figure 2 As shown, 11 reference points, M1 to M11, are set on the preset line M. It can be understood that... Figure 2 The other preset lines shown are not labeled.

[0093] The reference geomagnetic information at each reference point along the preset lines is acquired. This reference geomagnetic information is used to match the location of the target terminal in step 102, or to predict signal strength information in step 103. Therefore, after acquiring the reference geomagnetic information of the reference points included in multiple preset lines, it is stored accordingly. This allows for the corresponding storage of the position coordinate sequence of all reference points and their geomagnetic information for all reference points included in multiple preset lines.

[0094] 101. Obtain the geomagnetic information and signal quality information of the target terminal.

[0095] In practice, when a user travels along a preset route (this travel includes walking or riding in a vehicle), the system can obtain geomagnetic information and signal quality information corresponding to the target terminal's current location. The geomagnetic information characterizes the target terminal's position and movement trend, while the signal quality information characterizes the target terminal's current signal quality.

[0096] In this embodiment of the application, the geomagnetic information and signal quality information corresponding to the current location of the target terminal can be of various kinds.

[0097] The first approach could be: the geomagnetic information and signal quality information corresponding to the target terminal's current location only include the geomagnetic information and signal quality information acquired at the coordinate point corresponding to the target terminal's current location. For example, such as... Figure 3 As shown, the user rides a bus along the preset route M, and the target terminal obtains the corresponding data at position m6, which is the geomagnetic information and signal quality information corresponding to the current position of the target terminal.

[0098] The second type could be: geomagnetic information and signal quality information corresponding to the current location of the target terminal, including geomagnetic information and signal quality information composed of data acquired by the target terminal at multiple locations at the latest determined preset number of locations. For example, such as... Figure 3 As shown, the preset number is set to 4. The user rides a bus along a preset route M. The target terminal periodically acquires data corresponding to its current location. When data for location m5 is acquired, the geomagnetic information and signal quality information for the target terminal at location m5 (i.e., the target terminal's current location) can be generated based on the data for locations m2, m3, m4, and m5. Similarly, when data for location m6 is acquired, the geomagnetic information and signal quality information for the target terminal at location m6 (i.e., the target terminal's current location) can be generated based on the data for locations m3, m4, m5, and m6.

[0099] 102. Based on the geomagnetic information of the target terminal and the reference geomagnetic information of the reference points included in multiple preset lines, determine the target preset line where the target terminal is located and the target reference point corresponding to the target terminal.

[0100] In practice, after obtaining the geomagnetic information of the target terminal, the geomagnetic information corresponding to the current location of the target terminal can be matched with the reference geomagnetic information of reference points in multiple preset lines to determine which preset line the target terminal is currently located on, and this preset line is designated as the target preset line. Furthermore, the reference point corresponding to the target terminal on the target preset line is determined and designated as the target reference point.

[0101] In one possible implementation, the target reference point can be one reference point or multiple reference points.

[0102] When the target reference point is a single reference point, it represents the reference point on the target preset line that is closest to the current location of the target terminal. For example, ... Figure 3 As shown, the current position of the target terminal is m6, so the target reference point corresponding to the target terminal can be M6.

[0103] When there are multiple target reference points, these target reference points represent the preset number of reference points corresponding to the latest determined positions of the target terminal on the target preset line. This can also be understood as the multiple reference points on the target preset line corresponding to the latest segment of the target terminal's execution trajectory. For example, if the preset number is 4 and the current position of the target terminal is m6, then the target reference points corresponding to the target terminal can be M3, M4, M5, and M6.

[0104] 103. Based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0105] In implementation, after determining the reference geomagnetic information of the target reference point corresponding to the target terminal, the reference geomagnetic information of the target reference point and the signal quality information of the target terminal can be input into the trained signal strength prediction model to predict the signal strength information of the target terminal at the next reference point on the target preset line. For example, the current position of the target terminal is... Figure 3 Based on steps 101-103, m4 can be used to predict the signal strength information when the target terminal arrives at m5.

[0106] In one possible implementation, in addition to considering reference geomagnetic information and signal quality information, other information can also be considered to improve the accuracy of prediction. In this embodiment, step 103 can also be: based on the reference geomagnetic information and position coordinates of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model, to obtain the signal strength information of the target terminal at the next reference point on the target preset line.

[0107] In practice, the reference geomagnetic information and location coordinates of the target reference point, as well as the signal quality information of the target terminal, can be input into the trained signal strength prediction model, thereby making the predicted signal strength information at the next reference point more accurate.

[0108] The signal strength information can be a signal strength value, or a signal strength level, etc., and this application embodiment does not limit this. For the signal strength level, multiple levels can be set as needed; for example, the signal strength level can include four levels: unqualified, qualified, good, and excellent.

[0109] 104. Based on the signal strength information of the target terminal at the next reference point and the preset optimization strategy, perform network optimization on the target application running in the foreground state in the target terminal.

[0110] In implementation, multiple preset optimization strategies can be set. When the signal strength information meets the optimization conditions, the corresponding preset optimization strategy can be executed.

[0111] The optimization conditions can be varied. For example, when the signal strength information is a signal strength value, the optimization condition can be that the signal strength value is less than a preset signal strength threshold; when the signal strength information is a signal strength level, the optimization condition can be that the signal strength information is unqualified. Of course, the optimization conditions can also be other settings, and this application embodiment does not limit them.

[0112] In this embodiment of the application, the preset optimization strategy can be any method that can improve the network quality of the target application. The preset optimization strategy can be: when the signal strength information meets the optimization conditions, dynamically adjust the network bandwidth to allocate more bandwidth to the target application that is running in the foreground on the target terminal, so as to improve the network quality of the target application.

[0113] In this embodiment, corresponding preset optimization strategies can be set for different target applications. For example, when the target application is an audio / video application, a browser, a reader, or other similar application, the preset optimization condition could be to preload data in the target application. Alternatively, when the target application is an audio / video application, the preset optimization condition could be to increase its video resolution or its audio bitrate, etc.

[0114] By employing this application, the signal strength at the next reference point that the target terminal will reach can be predicted. The target application can be optimized based on the predicted signal strength information to avoid situations where the signal strength drops rapidly or is even lost, thereby improving the user's experience of using the terminal network.

[0115] Furthermore, in related technologies, the satellite signals used in navigation and positioning technology are easily affected by obstacles, leading to instability. Therefore, in this embodiment, navigation and positioning technology is not used to obtain the location of the target terminal. Instead, geomagnetic information is used to determine the target preset route and the corresponding target reference point. The geomagnetic field is a natural resource, avoiding positioning failures caused by using satellite signals. It is evident that by adopting this application, the reliability of determining the target preset route and target reference point is improved.

[0116] In this embodiment of the application, based on the above-mentioned geomagnetic information and signal quality information including relevant data of a preset number of locations, step 101 can be implemented as follows:

[0117] First, the geomagnetic intensity, magnetic heading angle, and signal quality parameters of the target terminal's location are periodically acquired.

[0118] Then, for geomagnetic information, geomagnetic information can be generated based on the geomagnetic intensity and magnetic heading angle of a preset number of locations. The geomagnetic information includes a geomagnetic gradient sequence and a magnetic heading angle sequence. The geomagnetic gradient sequence is a sequence composed of the difference between the geomagnetic intensity of every two adjacent locations in the preset number of locations, and the magnetic heading angle sequence is a sequence composed of the magnetic heading angles of the preset number of locations.

[0119] For signal quality information, signal quality information can be generated based on the acquired signal quality parameters at a preset number of locations. The signal quality information includes a sequence of signal quality parameters at a preset number of locations.

[0120] For example, when the preset number is 5, the geomagnetic gradient sequence includes four parameter values, and the magnetic heading angle sequence and signal quality information each include 5 parameter values.

[0121] In contrast to the aforementioned geomagnetic information, the reference geomagnetic information of the reference points included in the preset route includes the reference geomagnetic gradient sequence and the reference magnetic heading angle sequence corresponding to multiple reference points in the preset route. For example, if a preset route includes 30 reference points, then the reference geomagnetic gradient sequence corresponding to the preset route includes 29 parameter values, and the reference magnetic heading angle sequence includes 30 parameter values.

[0122] In one possible implementation, the signal quality parameters include at least one of the following: Reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (RSRQ), Channel Quality Indicator (CQI), Signal to Interference plus Noise Ratio (SINR), and Physical Cell Identifier (PCI).

[0123] like Figure 4 As shown, based on the above information settings, the implementation method of step 102 may include:

[0124] 401. For each preset line, in the reference geomagnetic gradient sequence corresponding to a preset number of adjacent reference points, determine the first difference between the geomagnetic gradient sequence corresponding to a preset number of positions and the reference geomagnetic gradient subsequence.

[0125] In practice, for each preset route, the following processing can be performed: obtain the reference geomagnetic gradient subsequence corresponding to each preset number of adjacent reference points among the multiple reference points included in the preset route.

[0126] Then, for each preset number of adjacent reference points in each preset line, the following processing is performed: calculate the first difference between the geomagnetic gradient sequence at the preset number of locations and the reference geomagnetic gradient subsequence corresponding to each preset number of adjacent reference points.

[0127] Understandably, the greater the first degree of difference, the less correlated the preset number of positions are with the preset number of reference points.

[0128] In one possible implementation, there are multiple calculation methods. For example, the geomagnetic gradient sequence of a preset number of locations and any set of reference geomagnetic gradient subsequences can be input into the contour matching algorithm to obtain the first difference between the geomagnetic gradient sequence and the set of reference geomagnetic gradient subsequences.

[0129] The contour matching algorithm can include any one of the following: cross-correlation algorithm, mean absolute difference algorithm, and mean square error algorithm.

[0130] 402. For each preset route, in the reference magnetic heading angle sequence, for every preset number of adjacent reference points, determine the second difference between the magnetic heading angle sequence corresponding to the preset number of positions and the reference magnetic heading angle subsequence.

[0131] In practice, similar to step 401, the following processing can be performed for each preset route: obtain the reference magnetic heading sub-sequence corresponding to each preset number of adjacent reference points among the multiple reference points included in the preset route.

[0132] Then, for each preset number of adjacent reference points in each preset route, the following processing is performed: calculate the second difference between the magnetic heading angle sequence of the preset number of positions and the reference magnetic heading angle subsequence corresponding to each preset number of adjacent reference points.

[0133] Understandably, the greater the second degree of difference, the less correlated the preset number of positions are with the preset number of reference points.

[0134] In one possible implementation, the calculation method is similar to step 401. It can also be that the magnetic heading angle sequence of a preset number of positions and any set of reference magnetic heading angle sub-sequences are input into the contour line matching algorithm to obtain the second difference degree between the magnetic heading angle sequence and the set of reference magnetic heading angle sub-sequences.

[0135] The contour matching algorithm can include any one of the following: cross-correlation algorithm, mean absolute difference algorithm, and mean square error algorithm.

[0136] It is understandable that the execution of steps 401 and 402 is not sequential; step 401 can be executed first, step 402 can be executed first, or they can be executed together.

[0137] 403. For each preset number of adjacent reference points, based on the first difference degree, second difference degree, geomagnetic weight value and magnetic heading angle weight value corresponding to the preset number of reference points, determine the total difference degree between the preset number of locations and the preset number of adjacent reference points.

[0138] In practice, the first degree of difference can be multiplied by the geomagnetic weight value to obtain the weighted first degree of difference, and the second degree of difference can be multiplied by the magnetic heading angle weight value to obtain the weighted second degree of difference. Then, the weighted first degree of difference and the weighted second degree of difference are added together to obtain the total degree of difference between a preset number of locations and a preset number of adjacent reference points.

[0139] 404. Based on the total difference between a preset number of locations and a preset number of adjacent reference points in each preset line, determine the target preset line where the preset number of locations are located and the preset number of adjacent target reference points corresponding to the preset number of locations.

[0140] In practice, the smaller the total difference of a reference point, the greater its correlation with a preset number of locations. Based on this rule, the target preset route and target reference points can be determined.

[0141] In one possible implementation, the method for determining the target preset route and the target reference point can be as follows:

[0142] The minimum value is selected from multiple total difference values. If the minimum value is less than the preset difference threshold, the preset route corresponding to the minimum value is determined as the target preset route, and the preset number of adjacent reference points corresponding to the minimum value are determined as target reference points.

[0143] In implementation, the minimum total difference is selected from multiple sets of preset number of reference points. The minimum total difference indicates that the correlation between this set of reference points and the preset number of locations of the target terminal is the greatest. Then, it is determined whether the minimum value is less than a preset difference threshold. If it is less than the threshold, it means that the preset number of locations of the target terminal are located on the same preset line. If it is greater than or equal to the threshold, it means that the preset number of locations of the target terminal are located on different preset lines.

[0144] When the minimum value is less than the preset difference threshold, the preset line corresponding to the minimum value can be determined as the target preset line, and the preset number of adjacent reference points corresponding to the minimum value can be determined as the target reference points.

[0145] When the minimum value is greater than or equal to the preset difference threshold, it means that the user (or target terminal) has just entered the current preset line and has not yet obtained enough locations. At this time, the target preset line and target reference point cannot be determined for the current location of the target terminal. The target preset line and target reference point can be determined when enough locations are obtained (i.e., the minimum value among the calculated total difference is less than the preset difference threshold).

[0146] In this embodiment, since the flow of people varies at different times, this flow affects the network quality of the target terminal. Generally, the more people there are, the worse the network quality of the target terminal, and the less people there are, the better the network quality. To address this, different signal strength prediction models can be trained for different time periods to predict the signal strength information of the next reference point within that time period.

[0147] In one possible implementation, the trained signal strength prediction model includes a trained early peak prediction model, a trained late peak prediction model, and a trained off-peak prediction model. The method for predicting signal strength information can then be:

[0148] First, the day can be divided into morning peak hours, evening peak hours, and off-peak hours in advance. Then, corresponding models can be trained for each of these three time periods to obtain the trained morning peak prediction model, the trained evening peak prediction model, and the trained off-peak prediction model.

[0149] When it is necessary to predict the signal strength information of the next reference point of the target terminal, the following processing can be performed: determine which of the above three time periods the current time point is located in.

[0150] When the current time point is within the morning rush hour, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained morning rush hour prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0151] When the current time point is within the evening peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained evening peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0152] When the current time point is within the off-peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained off-peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0153] This setting allows for the prediction of signal strength at the next reference point based on different time periods and varying pedestrian traffic, resulting in more accurate and realistic predictions.

[0154] In this embodiment of the application, before performing step 101, it is necessary to train the signal strength prediction model to obtain a trained signal strength prediction model, such as... Figure 5 As shown, its training method can be as follows:

[0155] 501. Obtain sample input data and benchmark output data.

[0156] The sample input data includes reference geomagnetic information of the sample reference points included in the preset line and signal quality information corresponding to the sample reference points. The reference output data includes the actual signal strength information of the next reference point of the sample reference point.

[0157] In practice, multiple sets of sample input data and corresponding benchmark output data can be obtained. The benchmark output data is the actual signal strength information of the next reference point of the sample reference point that is actually detected or actually calculated.

[0158] When the signal strength information is a signal strength value, the detection equipment can be used to detect the actual signal strength value of the next reference point of the sample reference point.

[0159] When the signal strength information for the dried bean curd is a signal strength level, the following method can be used to determine the actual signal strength information of the next reference point:

[0160] Obtain the signal received power, reference signal received quality, signal and interference plus noise at the next reference point of the sample reference point, and then determine the actual signal strength information of the next reference point based on Table 1 below.

[0161] Table 1

[0162]

[0163] For example, when the signal received power at the reference point is -70, the reference signal received quality is -5, and the signal plus interference noise is 20, the actual signal strength information at the next reference point is excellent.

[0164] Of course, other reasonable methods can also be used to determine the actual signal strength information, and this application embodiment does not limit this.

[0165] 502. Based on the reference geomagnetic information of the sample reference point, the signal quality information corresponding to the sample reference point, and the signal strength prediction model to be trained, the prediction output data is obtained.

[0166] In practice, the reference geomagnetic information of the sample reference point and the signal quality information corresponding to the sample reference point can be input into the signal strength prediction model to be trained, so as to obtain the prediction output data output by the signal strength prediction model to be trained.

[0167] Understandably, when using the location coordinates of a sample reference point to predict signal strength information, the signal strength prediction model needs to be trained by inputting the reference geomagnetic information of the sample reference point, the signal quality information corresponding to the sample reference point, and the location coordinates of the sample reference point into the signal strength prediction model to obtain the predicted output data of the signal strength prediction model.

[0168] 503. Based on the baseline output data and the predicted output data, the parameters of the signal strength prediction model to be trained are adjusted to obtain the trained signal strength prediction model.

[0169] In practice, the loss value can be calculated based on the baseline output data, the predicted output data, and the loss function. It can then be determined whether the loss value meets the training completion condition. If it does not meet the condition, the parameters of the signal strength prediction model to be trained can be tuned based on the loss value. If the condition is met, training can be stopped, and the signal strength prediction model at this point can be identified as the signal strength prediction model that has been trained.

[0170] There can be multiple training completion conditions. For example, the training completion condition can be: the loss value is less than a preset loss value threshold, where the preset loss value threshold can be 0.8 or other reasonable values.

[0171] Alternatively, the training completion condition can also be: the number of training sessions exceeds a preset threshold, where the preset threshold can be 300 or other reasonable values.

[0172] The embodiments of this application do not impose specific limitations on the conditions for training completion.

[0173] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0174] The scheme mentioned in this application embodiment can determine the target preset line where the target terminal is currently located and the target reference point corresponding to the target terminal based on the obtained geomagnetic information of the target terminal and the reference geomagnetic information of multiple preset lines including reference points. Then, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the quality information of the target terminal, and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained. Finally, based on the signal strength information of the target terminal at the next reference point and the preset optimization strategy, network optimization is performed on the target application running in the foreground state of the target terminal. By using this application, the signal strength at the next reference point that the target terminal will reach can be predicted. The target application can be optimized by using the predicted signal strength information to avoid the situation of rapid signal strength reduction or even loss, thereby improving the user's experience of terminal network.

[0175] This application provides a network optimization device for an application. This device can be the computer device described in the above embodiments, such as... Figure 6 As shown, the device includes:

[0176] The acquisition module 610 is used to acquire the geomagnetic information and signal quality information of the target terminal;

[0177] The first determining module 620 is used to determine the target preset line where the target terminal is located and the target reference point corresponding to the target terminal based on the geomagnetic information of the target terminal and the reference geomagnetic information of the reference points included in multiple preset lines;

[0178] The second determining module 630 is used to obtain the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model.

[0179] The optimization module 640 is used to perform network optimization on the target application running in the foreground state of the target terminal based on the signal strength information of the target terminal at the next reference point and a preset optimization strategy.

[0180] In one possible implementation, the preset route includes at least one of road routes, bus routes, subway lines, and train lines.

[0181] In one possible implementation, the reference points include multiple coordinate points evenly distributed along the preset route at preset route distances.

[0182] In one possible implementation, the acquisition module 610 is used for:

[0183] The geomagnetic intensity, magnetic heading angle, and signal quality parameters of the target terminal's location are periodically acquired.

[0184] Based on the obtained geomagnetic intensity and magnetic heading angle at a preset number of locations, the geomagnetic information is generated, wherein the geomagnetic information includes a geomagnetic gradient sequence and a magnetic heading angle sequence. The geomagnetic gradient sequence is a sequence composed of the difference between the geomagnetic intensity of every two adjacent locations in the preset number of locations, and the magnetic heading angle sequence is a sequence composed of the magnetic heading angles at the preset number of locations.

[0185] Based on the obtained signal quality parameters of a preset number of locations, the signal quality information is generated, wherein the signal quality information includes a sequence of signal quality parameters of a preset number of locations.

[0186] In one possible implementation, the reference geomagnetic information of the reference points included in the preset route includes a reference geomagnetic gradient sequence and a reference magnetic heading angle sequence corresponding to multiple reference points in the preset route;

[0187] The first determining module 620 is used for:

[0188] For each preset number of adjacent reference points in the reference geomagnetic gradient sequence corresponding to each preset line, determine the first degree of difference between the geomagnetic gradient sequence corresponding to the preset number of positions and the reference geomagnetic gradient subsequence.

[0189] For each preset route, in the reference magnetic heading angle sequence corresponding to a preset number of adjacent reference points, a second degree of difference is determined between the magnetic heading angle sequence corresponding to the preset number of positions and the reference magnetic heading angle subsequence.

[0190] For each preset number of adjacent reference points, the total difference between the preset number of locations and the preset number of adjacent reference points is determined based on the first difference degree, the second difference degree, the geomagnetic weight value, and the magnetic heading angle weight value corresponding to the preset number of reference points.

[0191] Based on the total difference between the preset number of locations and the preset number of adjacent reference points in each preset line, the target preset line where the preset number of locations are located and the preset number of adjacent target reference points corresponding to the preset number of locations are determined.

[0192] In one possible implementation, the first determining module 620 is configured to:

[0193] The minimum value is selected from multiple total difference values. If the minimum value is less than a preset difference threshold, the preset route corresponding to the minimum value is determined as the target preset route, and the preset number of adjacent reference points corresponding to the minimum value are determined as target reference points.

[0194] In one possible implementation, the signal quality parameters include at least one of signal received power, reference signal received quality, channel quality indication, signal-to-interference-plus-noise ratio, and physical cell identifier.

[0195] In one possible implementation, the second determining module 630 is configured to:

[0196] Based on the reference geomagnetic information and location coordinates of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0197] In one possible implementation, the signal strength information is a signal strength value or a signal strength level.

[0198] In one possible implementation, the trained signal strength prediction model includes a trained early peak prediction model, a trained late peak prediction model, and a trained off-peak prediction model.

[0199] The second determining module 630 is used for:

[0200] When the current time point is within the morning peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained morning peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained;

[0201] When the current time point is within the evening peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained evening peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained;

[0202] When the current time point is within the off-peak time period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained off-peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

[0203] In one possible implementation, the device further includes a training module for:

[0204] Acquire sample input data and reference output data, wherein the sample input data includes reference geomagnetic information of sample reference points included in the preset line and signal quality information corresponding to the sample reference points, and the reference output data includes actual signal strength information of the next reference point of the sample reference point;

[0205] Based on the reference geomagnetic information of the sample reference points, the signal quality information corresponding to the sample reference points, and the signal strength prediction model to be trained, the prediction output data is obtained.

[0206] Based on the baseline output data and the predicted output data, the parameters of the signal strength prediction model to be trained are adjusted to obtain the trained signal strength prediction model.

[0207] It should be noted that the network optimization device for the application provided in the above embodiments is only illustrated by the division of the above functional modules when performing network optimization for the application. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the network optimization device for the application provided in the above embodiments and the network optimization method embodiments for the application belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0208] Figure 7 A structural block diagram of a terminal 700 provided in an exemplary embodiment of this application is shown. The terminal can be a computer device as described in the above embodiments. The terminal 700 can be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 700 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0209] Typically, terminal 700 includes a processor 701 and a memory 702.

[0210] Processor 701 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 701 may be implemented using at least one hardware form selected from DSP (digital signal processing), FPGA (field-programmable gate array), and PLA (programmable logic array). Processor 701 may also include a main processor and a coprocessor. The main processor, also known as a CPU (central processing unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 701 may integrate a GPU (graphics processing unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 701 may also include an AI (artificial intelligence) processor, which is used to handle computational operations related to machine learning.

[0211] Memory 702 may include one or more computer-readable storage media, which may be non-transitory. Memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in memory 702 is used to store at least one instruction, which is executed by processor 701 to implement the network optimization method of the application provided in the method embodiments of this application.

[0212] In some embodiments, the terminal 700 may also optionally include a peripheral device interface 703 and at least one peripheral device. The processor 701, memory 702, and peripheral device interface 703 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 704, a display screen 705, a camera 706, an audio circuit 707, a positioning component 708, and a power supply 709.

[0213] Peripheral device interface 703 can be used to connect at least one I / O (input / output) related peripheral device to processor 701 and memory 702. In some embodiments, processor 701, memory 702 and peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 701, memory 702 and peripheral device interface 703 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0214] The radio frequency (RF) circuit 704 is used to receive and transmit RF (radio frequency) signals, also known as electromagnetic signals. The RF circuit 704 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 704 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (wireless fidelity) networks. In some embodiments, the RF circuit 704 may also include circuitry related to NFC (near field communication), which is not limited in this application.

[0215] Display screen 705 is used to display a user interface (UI). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 705 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 701 for processing. In this case, display screen 705 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 705, which serves as the front panel of terminal 700; in other embodiments, there may be at least two display screens 705, respectively disposed on different surfaces of terminal 700 or in a folded design; in still other embodiments, display screen 705 may be a flexible display screen, disposed on a curved or folded surface of terminal 700. Furthermore, display screen 705 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 705 may be made of materials such as LCD (liquid crystal display) or OLED (organic light-emitting diode).

[0216] The camera assembly 706 is used to acquire images or videos. Optionally, the camera assembly 706 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (virtual reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0217] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 701 for processing, or input to the radio frequency circuit 704 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal 700. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert the electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0218] The positioning component 708 is used to determine the current geographic location of the positioning terminal 700 in order to enable navigation or LBS (location-based service). The positioning component 708 can be a positioning component based on GPS (global positioning system), BeiDou system, Grenadin system, or Galileo system.

[0219] The power supply 709 is used to power the various components in the terminal 700. The power supply 709 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When the power supply 709 includes a rechargeable battery, the rechargeable battery can support wired or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0220] In some embodiments, the terminal 700 further includes one or more sensors 710. The one or more sensors 710 include, but are not limited to: an accelerometer 711, a gyroscope 712, a pressure sensor 713, a fingerprint sensor 714, an optical sensor 715, and a proximity sensor 716.

[0221] Accelerometer 711 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by terminal 700. For example, accelerometer 711 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 701 can control display screen 705 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 711. Accelerometer 711 can also be used for games or for acquiring user motion data.

[0222] The gyroscope sensor 712 can detect the orientation and rotation angle of the terminal 700. The gyroscope sensor 712, in conjunction with the accelerometer sensor 711, can collect 3D motion data from the user on the terminal 700. Based on the data collected by the gyroscope sensor 712, the processor 701 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0223] The pressure sensor 713 can be disposed on the side bezel of the terminal 700 and / or the lower layer of the display screen 705. When the pressure sensor 713 is disposed on the side bezel of the terminal 700, it can detect the user's grip signal on the terminal 700, and the processor 701 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 713. When the pressure sensor 713 is disposed on the lower layer of the display screen 705, the processor 701 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 705. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0224] The fingerprint sensor 714 is used to collect a user's fingerprint. The processor 701 identifies the user based on the fingerprint collected by the fingerprint sensor 714, or vice versa. When the user's identity is identified as trusted, the processor 701 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 714 can be located on the front, back, or side of the terminal 700. When the terminal 700 has a physical button or manufacturer logo, the fingerprint sensor 714 can be integrated with the physical button or manufacturer logo.

[0225] An optical sensor 715 is used to collect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 based on the ambient light intensity collected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is decreased. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 based on the ambient light intensity collected by the optical sensor 715.

[0226] The proximity sensor 716, also known as a distance sensor, is typically located on the front panel of the terminal 700. The proximity sensor 716 is used to detect the distance between the user and the front of the terminal 700. In one embodiment, when the proximity sensor 716 detects that the distance between the user and the front of the terminal 700 is gradually decreasing, the processor 701 controls the display screen 705 to switch from a screen-on state to a screen-off state; when the proximity sensor 716 detects that the distance between the user and the front of the terminal 700 is gradually increasing, the processor 701 controls the display screen 705 to switch from a screen-off state to a screen-on state.

[0227] Those skilled in the art will understand that Figure 7 The structure shown does not constitute a limitation on terminal 700, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0228] Figure 8 This is a schematic diagram of a server structure provided in an embodiment of this application. The server 800 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 801 and one or more memories 802. The memory 802 stores at least one instruction, which is loaded and executed by the processor 801 to implement the methods provided in the various method embodiments described above. Of course, the server may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server may also include other components for implementing device functions, which will not be elaborated upon here.

[0229] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions that can be executed by a processor in a terminal to perform the network optimization method of the application described in the above embodiments. This computer-readable storage medium may be non-transitory. For example, the computer-readable storage medium may be ROM (read-only memory), RAM (random access memory), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0230] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0231] It should be noted that all information (including but not limited to user equipment information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals (including but not limited to signals transmitted between user terminals and other devices) involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the "geomagnetic information and signal quality information" involved in this application were obtained under full authorization.

[0232] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A network optimization method for an application, characterized in that, The method includes: The geomagnetic information and signal quality information of the target terminal are acquired, wherein the signal quality information is used to characterize the current signal quality of the target terminal; Based on the geomagnetic information of the target terminal and the reference geomagnetic information of multiple reference points included in multiple preset lines, the target preset line where the target terminal is located is determined among the multiple preset lines, and the target reference point corresponding to the target terminal is determined among the multiple reference points included in the target preset line. Based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained. Based on the signal strength information of the target terminal at the next reference point and the preset optimization strategy, network optimization is performed on the target application running in the foreground state in the target terminal.

2. The method according to claim 1, characterized in that, The reference points include multiple coordinate points evenly distributed along the preset route according to the preset route distance.

3. The method according to claim 1, characterized in that, The acquisition of the geomagnetic information and signal quality information of the target terminal includes: The geomagnetic intensity, magnetic heading angle, and signal quality parameters of the target terminal's location are periodically acquired. Based on the obtained geomagnetic intensity and magnetic heading angle at a preset number of locations, the geomagnetic information is generated, wherein the geomagnetic information includes a geomagnetic gradient sequence and a magnetic heading angle sequence. The geomagnetic gradient sequence is a sequence composed of the difference between the geomagnetic intensity of every two adjacent locations in the preset number of locations, and the magnetic heading angle sequence is a sequence composed of the magnetic heading angles at the preset number of locations. Based on the obtained signal quality parameters of a preset number of locations, the signal quality information is generated, wherein the signal quality information includes a sequence of signal quality parameters of a preset number of locations.

4. The method according to claim 3, characterized in that, The reference geomagnetic information of the reference points included in the preset route includes the reference geomagnetic gradient sequence and the reference magnetic heading angle sequence corresponding to multiple reference points in the preset route; The step of determining the target preset line where the target terminal is located from among the multiple preset lines and the reference geomagnetic information of multiple reference points included in the multiple preset lines based on the geomagnetic information of the target terminal and the target reference point corresponding to the target terminal from among the multiple reference points included in the target preset line includes: For each preset number of adjacent reference points in the reference geomagnetic gradient sequence corresponding to each preset line, determine the first degree of difference between the geomagnetic gradient sequence corresponding to the preset number of positions and the reference geomagnetic gradient subsequence. For each preset route, in the reference magnetic heading angle sequence corresponding to a preset number of adjacent reference points, a second degree of difference is determined between the magnetic heading angle sequence corresponding to the preset number of positions and the reference magnetic heading angle subsequence. For each preset number of adjacent reference points, the total difference between the preset number of locations and the preset number of adjacent reference points is determined based on the first difference degree, the second difference degree, the geomagnetic weight value, and the magnetic heading angle weight value corresponding to the preset number of reference points. Based on the total difference between the preset number of locations and each preset number of adjacent reference points, the target preset route where the preset number of locations are located and the preset number of adjacent target reference points corresponding to the preset number of locations are determined.

5. The method according to claim 4, characterized in that, The step of determining the target preset line where the preset number of positions are located and the preset number of adjacent target reference points corresponding to the preset number of positions, based on the total difference value between the preset number of positions and the preset number of adjacent reference points in each preset line, includes: The minimum value is selected from multiple total difference values. If the minimum value is less than a preset difference threshold, the preset route corresponding to the minimum value is determined as the target preset route, and the preset number of adjacent reference points corresponding to the minimum value are determined as target reference points.

6. The method according to claim 1, characterized in that, The method of obtaining the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model includes: Based on the reference geomagnetic information and location coordinates of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

7. The method according to claim 1, characterized in that, The trained signal strength prediction model includes a trained early peak prediction model, a trained late peak prediction model, and a trained off-peak prediction model. The method of obtaining the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model includes: When the current time point is within the morning peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained morning peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained; When the current time point is within the evening peak period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained evening peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained; When the current time point is within the off-peak time period, based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained off-peak prediction model, the signal strength information of the target terminal at the next reference point on the target preset line is obtained.

8. The method according to claim 1, characterized in that, The method further includes: Acquire sample input data and reference output data, wherein the sample input data includes reference geomagnetic information of sample reference points included in the preset line and signal quality information corresponding to the sample reference points, and the reference output data includes actual signal strength information of the next reference point of the sample reference point; Based on the reference geomagnetic information of the sample reference points, the signal quality information corresponding to the sample reference points, and the signal strength prediction model to be trained, the prediction output data is obtained. Based on the baseline output data and the predicted output data, the parameters of the signal strength prediction model to be trained are adjusted to obtain the trained signal strength prediction model.

9. A network optimization device for an application, characterized in that, The device includes: The acquisition module is used to acquire geomagnetic information and signal quality information of the target terminal, wherein the signal quality information is used to characterize the current signal quality of the target terminal; The first determining module is used to determine the target preset line where the target terminal is located among the multiple preset lines based on the geomagnetic information of the target terminal and the reference geomagnetic information of multiple reference points included in the multiple preset lines, and to determine the target reference point corresponding to the target terminal among the multiple reference points included in the target preset line. The second determining module is used to obtain the signal strength information of the target terminal at the next reference point on the target preset line based on the reference geomagnetic information of the target reference point corresponding to the target terminal, the signal quality information of the target terminal, and the trained signal strength prediction model. The optimization module is used to perform network optimization on the target application running in the foreground state of the target terminal based on the signal strength information of the target terminal at the next reference point and the preset optimization strategy.

10. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to perform the operations performed by the network optimization method of the application as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, which is loaded and executed by a processor to perform the operations performed by the network optimization method of the application as described in any one of claims 1 to 8.

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