Methods, devices and storage media for acquiring spot beams

Abstract

This disclosure relates to a method, apparatus, and storage medium for acquiring spot beams. The method includes: acquiring the position of a target terminal at a first moment to obtain a first position; determining first ephemeris data of a first satellite based on the first position; inputting the first position, the first ephemeris data, and the first moment into a first prediction model to obtain a first spot beam of the first satellite available at the first position at the first moment, output by the first prediction model; the first prediction model is trained based on a first sample, the first sample including a sample spot beam, a first sample moment, a first sample position of the sample terminal at the first sample moment, and the first sample ephemeris data corresponding to the first sample position, the sample spot beam including the spot beam available at the first sample position of the sample terminal at the first sample moment. The above scheme can quickly determine the available satellite spot beams at the current moment and position using the first prediction model.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a method, apparatus and storage medium for acquiring spot beams. Background Technology

[0002] In communication scenarios, UE (User Equipment) typically needs to perform downlink synchronization with satellites based on spot beams to complete initial satellite access. For example, the UE can scan its supported frequency bands and points to acquire spot beams and then perform downlink synchronization. However, this method of acquiring spot beams is time-consuming and may affect the user experience. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a method, apparatus and storage medium for acquiring spot beams.

[0004] According to a first aspect of the present disclosure, a method for acquiring a spot beam is provided, comprising:

[0005] Obtain the target terminal's position at the first moment, and get the first position;

[0006] Based on the first location, the first ephemeris data of the first satellite is determined, and the coverage area of ​​the first satellite at the first moment includes the first location;

[0007] The first position, the first ephemeris data, and the first time are input into the first prediction model to obtain the first spot beam of the first satellite available at the first position at the first time, as output by the first prediction model.

[0008] The first prediction model is trained based on the first sample, which includes a sample point beam, a first sample time, a first sample position of the sample terminal at the first sample time, and the first sample ephemeris data corresponding to the first sample position. The sample point beam includes the point beam available to the sample terminal at the first sample position at the first sample time.

[0009] Optionally, determining the first ephemeris data of the first satellite based on the first position includes:

[0010] The first position and the first time are input into the second prediction model to obtain the first ephemeris data output by the second prediction model;

[0011] The second prediction model is trained based on the second sample, which includes the second sample ephemeris data, the second sample time, and the second sample position of the sample terminal at the second sample time. The second sample ephemeris data includes the ephemeris data that the sample terminal can obtain at the second sample position at the second sample time.

[0012] Optionally, it includes:

[0013] Obtain the second sample position of the sample terminal at the second sample time;

[0014] The ephemeris data obtained by the sample terminal at the second sample position at the second sample time is obtained to obtain the second sample ephemeris data;

[0015] The second sample is generated based on the ephemeris data of the second sample, the time of the second sample, and the location of the second sample.

[0016] The second prediction model is trained by taking the second sample as input and the ephemeris data obtained by the predicted sample terminal at the second sample position at the second sample time as output.

[0017] Optionally, determining the first ephemeris data of the first satellite based on the first position includes:

[0018] The first position is input into the third prediction model to obtain the first ephemeris data output by the third prediction model;

[0019] The third prediction model is trained based on a third sample, which includes the third sample location of the sample terminal and the third sample ephemeris data that the sample terminal can obtain at the third sample location.

[0020] Optionally, it includes:

[0021] Obtain the third sample location of the sample terminal;

[0022] The ephemeris data obtained by the sample terminal at the third sample location is obtained to obtain the third sample ephemeris data;

[0023] A third sample is generated based on the ephemeris data of the third sample and the location of the third sample.

[0024] The third prediction model is trained by taking the third sample as input and the ephemeris data obtained by the predicted sample terminal at the location of the third sample as output.

[0025] Optionally, it includes:

[0026] Obtain the location information of the target terminal;

[0027] The movement direction of the target terminal is determined based on the location information;

[0028] Based on the first position and the direction of movement, the next position of the target terminal is determined, and the second position is obtained;

[0029] Calculate the time when the target terminal arrives at the second position to obtain the second time.

[0030] Based on the second location, the second ephemeris data of the second satellite is determined, and the coverage area of ​​the second satellite at the second time includes the second location;

[0031] The second position, the second ephemeris data, and the second time are input into the first prediction model to obtain the second position of the second satellite available at the second time, as output by the first prediction model.

[0032] Optionally, it includes:

[0033] Obtain the first sample position of the sample terminal at the first sample time;

[0034] The sample point beam is obtained by acquiring the point beam that the sample terminal can search for at the first sample position at the first sample time.

[0035] The first sample is generated based on the sample point beam, the first sample time, and the first sample position.

[0036] The first prediction model is trained by taking the first sample as input and the predicted point beam that the sample terminal can search at the first sample position at the first sample time as output.

[0037] According to a second aspect of the present disclosure, a spot beam acquisition device is provided, comprising:

[0038] The first acquisition module is configured to acquire the position of the target terminal at the first moment and obtain the first position.

[0039] The first determining module is configured to determine the first ephemeris data of the first satellite based on the first location, wherein the coverage area of ​​the first satellite at the first moment includes the first location;

[0040] The first input module is configured to input the first position, the first ephemeris data, and the first time into the first prediction model to obtain the first spot beam of the first satellite available at the first position at the first time, as output by the first prediction model.

[0041] The first prediction model is trained based on the first sample, which includes a sample point beam, a first sample time, a first sample position of the sample terminal at the first sample time, and the first sample ephemeris data corresponding to the first sample position. The sample point beam includes the point beam available to the sample terminal at the first sample position at the first sample time.

[0042] Optionally, the first determining module includes:

[0043] The first input submodule is configured to input the first position and the first time into the second prediction model to obtain the first ephemeris data output by the second prediction model;

[0044] The second prediction model is trained based on the second sample, which includes the second sample ephemeris data, the second sample time, and the second sample position of the sample terminal at the second sample time. The second sample ephemeris data includes the ephemeris data that the sample terminal can obtain at the second sample position at the second sample time.

[0045] Optionally, it includes:

[0046] The second acquisition module is configured to acquire the second sample position of the sample terminal at the second sample time.

[0047] The third acquisition module is configured to acquire the ephemeris data obtained by the sample terminal at the second sample position at the second sample time, and obtain the second sample ephemeris data.

[0048] The first generation module is configured to generate the second sample based on the second sample ephemeris data, the second sample time, and the second sample position.

[0049] The first training module is configured to take the second sample as input and the ephemeris data obtained by the predicted sample terminal at the second sample position at the second sample time as output to train the second prediction model.

[0050] Optionally, the first determining module includes:

[0051] The second input submodule is configured to input the first position into the third prediction model to obtain the first ephemeris data output by the third prediction model;

[0052] The third prediction model is trained based on a third sample, which includes the third sample location of the sample terminal and the third sample ephemeris data that the sample terminal can obtain at the third sample location.

[0053] Optionally, it includes:

[0054] The fourth acquisition module is configured to acquire the third sample location of the sample terminal;

[0055] The fifth acquisition module is configured to acquire the ephemeris data obtained by the sample terminal at the third sample location, thereby obtaining the third sample ephemeris data;

[0056] The second generation module is configured to generate a third sample based on the third sample ephemeris data and the position of the third sample.

[0057] The second training module is configured to take the third sample as input and the ephemeris data obtained by the predicted sample terminal at the location of the third sample as output to train the third prediction model.

[0058] Optionally, it includes:

[0059] The sixth acquisition module is configured to acquire the location information of the target terminal;

[0060] The direction determination module is configured to determine the movement direction of the target terminal based on the location information;

[0061] The location determination module is configured to determine the next location of the target terminal based on the first location and the direction of movement, thereby obtaining a second location;

[0062] The time calculation module is configured to calculate the time when the target terminal arrives at the second location, and obtain the second time.

[0063] The ephemeris determination module is configured to determine the second ephemeris data of the second satellite based on the second position, wherein the coverage area of ​​the second satellite at the second time includes the second position;

[0064] The second input module is configured to input the second position, the second ephemeris data, and the second time into the first prediction model to obtain the second spot beam of the second satellite available at the second time, as output by the first prediction model.

[0065] Optionally, it includes:

[0066] The seventh acquisition module is configured to acquire the first sample position of the sample terminal at the first sample time.

[0067] The eighth acquisition module is configured to acquire the point beam that the sample terminal can search for at the first sample position at the first sample time, and obtain the sample point beam.

[0068] The third generation module is configured to generate the first sample based on the sample point beam, the first sample time, and the first sample position.

[0069] The third training module is configured to take the first sample as input and the predicted point beam that the sample terminal can search for at the first sample position at the first sample time as output to train the first prediction model.

[0070] According to a third aspect of the present disclosure, a spot beam acquisition device is provided, comprising:

[0071] processor;

[0072] Memory used to store processor-executable instructions;

[0073] The processor is configured to perform the steps of the method described in any of the first aspects above.

[0074] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the method described in any of the first aspects above.

[0075] In the above technical solution, a first prediction model can be trained based on the first sample. This allows the terminal to obtain its first location at a first moment, determine the first ephemeris data of the first satellite based on the first location, and input the first location, the first ephemeris data, and the first moment into the first prediction model to obtain the first spot beam of the first satellite available at the first location at the first moment, as output by the first prediction model. In other words, the available satellite spot beams at the current moment and location can be quickly predicted using the first prediction model. Compared to scanning various frequency bands and frequencies of the terminal device to obtain spot beams, the above solution has a faster spot beam acquisition speed, which helps to shorten the terminal device's network access time.

[0076] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0077] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0078] Figure 1 This is a flowchart illustrating a method for acquiring a spot beam according to an exemplary embodiment.

[0079] Figure 2 This is a flowchart illustrating the training process of a second prediction model according to an exemplary embodiment.

[0080] Figure 3This is a flowchart illustrating the training process of a third prediction model according to an exemplary embodiment.

[0081] Figure 4 This is a flowchart illustrating the training process of a first prediction model according to an exemplary embodiment.

[0082] Figure 5 This is a flowchart illustrating a method for acquiring a spot beam according to an exemplary embodiment.

[0083] Figure 6 This is a flowchart illustrating a second point beam acquisition process according to an exemplary embodiment.

[0084] Figure 7 This is a block diagram illustrating a spot beam acquisition device according to an exemplary embodiment.

[0085] Figure 8 This is a block diagram illustrating an apparatus 800 for acquiring a spot beam according to an exemplary embodiment. Detailed Implementation

[0086] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0087] Before introducing the spot beam acquisition method, apparatus and storage medium of this disclosure, the application scenarios of this disclosure will be introduced first.

[0088] In some communication scenarios, user terminals can scan the frequency bands they support to identify satellites that match their frequency bands. For example, a user terminal can open a window for each supported frequency band and identify the higher-energy frequency band window and its corresponding satellite. The terminal device can then attempt to obtain relevant information about the satellite, such as ephemeris data and supported spot beams, and subsequently perform downlink synchronization and obtain cell information based on the spot beams, ultimately completing initial satellite access.

[0089] However, the process of a user terminal scanning its own supported frequency bands and frequencies takes a long time, which means that in some scenarios, the user terminal needs a long time to acquire the spot beam, thus affecting the user experience.

[0090] Therefore, this disclosure provides a method for acquiring a spot beam. The method can be applied to user terminals, such as mobile phones, tablets, wearable devices, etc. The method can also be applied to other computing devices, such as computing servers, desktop computers, laptops, etc. Figure 1 This is a flowchart illustrating a method for acquiring a spot beam, as shown in this disclosure. (Refer to...) Figure 1 The method includes:

[0091] In step S11, the position of the target terminal at the first moment is obtained, thus obtaining the first position.

[0092] For example, the location of a target terminal at a given moment can be obtained through various positioning technologies. These positioning technologies can be base station positioning, GPS (Global Positioning System) positioning, WiFi (Wireless Fidelity) positioning, Bluetooth positioning, or combinations thereof.

[0093] In step S12, the first ephemeris data of the first satellite is determined based on the first position, and the coverage area of ​​the first satellite at the first moment includes the first position.

[0094] For example, in some implementation scenarios, a second prediction model can be trained to predict the first ephemeris data. Figure 2 This is a flowchart illustrating the training process of a second prediction model as shown in this disclosure, with reference to... Figure 2 The training process for the second prediction model may include:

[0095] In step S21, the second sample position of the sample terminal at the second sample time is obtained.

[0096] As an example, historical data of the sample terminal can be obtained, a certain moment in the historical data can be used as the second sample moment, and the position of the sample terminal in the historical data at the second sample moment can be used as the second sample position.

[0097] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0098] In step S22, the ephemeris data obtained by the sample terminal at the second sample position at the second sample time is obtained to obtain the second sample ephemeris data.

[0099] For example, the sample terminal can obtain the second sample ephemeris data via satellite broadcast. That is, the sample terminal can communicate with a satellite to download the complete ephemeris data and obtain the second sample ephemeris data. However, due to the limited bandwidth of satellite links, this method of obtaining ephemeris data takes a long time.

[0100] In some scenarios, the sample terminal can also acquire second sample ephemeris data via internet broadcast. For example, the sample terminal can request SUPL data from the network using the SUPL (Secure User Plane Location) protocol. The sample terminal can then receive the ephemeris data transmitted back from the network as the second sample ephemeris data. However, this method is dependent on the network environment and therefore has limitations.

[0101] Furthermore, it is worth noting that multiple satellites may exist at the same location at the same time; that is, the coverage area of ​​multiple satellites may include the location at that time. Therefore, the ephemeris data obtained by the sample terminal at the second sample location at the second sample time may include ephemeris data from multiple satellites; that is, the second sample ephemeris data may include ephemeris data from multiple satellites.

[0102] In step S23, a second sample is generated based on the second sample ephemeris data, the second sample time, and the second sample position.

[0103] For example, the second sample ephemeris data can be used as the sample label, and the second sample time and second sample location can be used as features to generate the second sample.

[0104] In step S24, the second prediction model is trained by taking the second sample as input and the ephemeris data obtained by the predicted sample terminal at the second sample position at the second sample time as output.

[0105] In various embodiments of this disclosure, the second prediction model or other models involved in the embodiments of this disclosure can be trained based on various network models, such as recurrent neural network models, convolutional neural network models, etc., and this disclosure does not limit them.

[0106] By inputting the second sample into the model to be trained, the ephemeris data of the sample terminal at the second sample position at the second sample time can be obtained, which is predicted by the model to be trained. This predicted ephemeris data can then be compared with the second sample ephemeris data, i.e., compared with the actual ephemeris data, to calculate the loss value. Based on the loss value, the parameters of the model to be trained can be adjusted, thereby completing the training and obtaining the second prediction model.

[0107] After obtaining the second prediction model, the first ephemeris data can be obtained based on the second prediction model. In this case, determining the first ephemeris data of the first satellite based on the first position, i.e., step S12, includes:

[0108] The first position and the first time are input into the second prediction model to obtain the first ephemeris data output by the second prediction model.

[0109] The above scheme trains a second prediction model, which takes the first location and the first time as inputs and outputs the first ephemeris data. In other words, the ephemeris data of a satellite at a specific location at a specific time can be quickly obtained using the second prediction model. This method has advantages such as fast ephemeris data acquisition and independence from network conditions.

[0110] It should be noted that in some implementation scenarios, ephemeris data can also be obtained from high-orbit satellites, such as geostationary satellites. However, the relative positions of these satellites to Earth do not change over time. Therefore, referring to... Figure 3 The flowchart shown illustrates the training process for a third prediction model. The third prediction model can be trained through the following steps:

[0111] In step S31, the third sample position of the sample terminal is obtained.

[0112] As an example, historical data of the sample terminal can be obtained, and the location of the sample terminal in the historical data can be used as the third sample location.

[0113] In step S32, the ephemeris data obtained by the sample terminal at the third sample position is acquired to obtain the third sample ephemeris data.

[0114] For instructions on how to obtain ephemeris data using the sample terminal, please refer to the information provided. Figure 2 The embodiments described herein will not be elaborated upon further.

[0115] In step S33, a third sample is generated based on the third sample ephemeris data and the position of the third sample.

[0116] In step S34, the third prediction model is trained by taking the third sample as input and the ephemeris data obtained by the predicted sample terminal at the location of the third sample as output.

[0117] By inputting a third sample into the model to be trained, we can obtain the ephemeris data of the sample terminal at the location of the third sample, which is predicted by the model. This allows us to compare the predicted ephemeris data with the third sample ephemeris data, i.e., with the actual ephemeris data, thereby calculating the loss value. Based on the loss value, the parameters of the model to be trained can be adjusted, thus completing the training and obtaining the third prediction model.

[0118] After obtaining the third prediction model, the first ephemeris data can be obtained based on the third prediction model. In this case, determining the first ephemeris data of the first satellite based on the first position, i.e., step S12, includes:

[0119] The first position is input into the third prediction model to obtain the first ephemeris data output by the third prediction model.

[0120] The above scheme trains a third prediction model, which inputs the first position into the third prediction model to obtain the first ephemeris data output by the third prediction model. In other words, the ephemeris data of a satellite at a specific location at a specific time can be quickly obtained using the third prediction model. This method has advantages such as fast ephemeris data acquisition and independence from network conditions.

[0121] Still refer to Figure 1 In step S13, the first position, the first ephemeris data and the first time are input into the first prediction model to obtain the first position at the first time and the first spot beam of the first satellite available at the first time output by the first prediction model.

[0122] The first prediction model is trained based on the first sample, which includes a sample point beam, a first sample time, a first sample position of the sample terminal at the first sample time, and the first sample ephemeris data corresponding to the first sample position. The sample point beam includes the point beam available to the sample terminal at the first sample position at the first sample time.

[0123] The training method of the first prediction model is illustrated below. (Refer to...) Figure 4 The flowchart shown illustrates the training process for a first prediction model. The first prediction model can be trained in the following manner:

[0124] In step S41, the first sample position of the sample terminal at the first sample time is obtained.

[0125] In step S42, the point beam that the sample terminal can search for at the first sample position at the first sample time is obtained, and the sample point beam is obtained.

[0126] As an example, historical data of the sample terminal can be acquired, with a specific moment in the historical data taken as the first sample moment, the position of the sample terminal at the first sample moment in the historical data taken as the second sample position, and the spot beam that the sample terminal can search for at the first sample position at the first sample moment taken as the sample spot beam. The spot beam that the sample terminal can search for at the first sample position at the first sample moment can refer to the spot beam actually used by the sample terminal at the first sample position at the first sample moment (or within a preset time range starting from the first sample moment) for initial satellite access.

[0127] In step S43, the first sample is generated based on the sample point beam, the first sample time, and the first sample position.

[0128] In step S44, the first prediction model is trained by taking the first sample as input and the predicted point beam that the sample terminal can search for at the first sample position at the first sample time as output.

[0129] By inputting the first sample into the model to be trained, the predicted point beams that the model can search for at the first sample position at the first sample time can be obtained. The predicted point beams can then be compared with the sample point beams, i.e., compared with the actually available point beams, to calculate the loss value. Based on the loss value, the parameters of the model to be trained can be adjusted, thereby completing the training and obtaining the first prediction model.

[0130] In this way, the first position, the first ephemeris data, and the first time can be input into the first prediction model to obtain the first position at the first time and the first spot beam of the first satellite available at the first time output by the first prediction model.

[0131] In the above technical solution, a first prediction model can be trained based on the first sample. This allows the terminal to obtain its first position at a first moment, determine the first ephemeris data of the first satellite based on the first position, and input the first position, the first ephemeris data, and the first moment into the first prediction model to obtain the first spot beam of the first satellite available at the first position at the first moment, as output by the first prediction model. In other words, the available satellite spot beams at the current moment and position can be quickly predicted using the first prediction model. Compared to scanning various frequency bands and frequencies of the terminal device to obtain spot beams, the above solution has a faster spot beam acquisition speed.

[0132] Figure 5 This is a flowchart illustrating a method for acquiring a spot beam, as shown in this disclosure. (Refer to...) Figure 5 The method is in Figure 1 In addition to:

[0133] In step S14, the location information of the target terminal is obtained.

[0134] In step S15, the movement direction of the target terminal is determined based on the location information.

[0135] For example, in some implementation scenarios, a time-series-based location sequence of the target terminal can be obtained, and the direction of the target terminal's position change can be determined by fitting each location point in the location sequence.

[0136] In step S16, the next position of the target terminal is determined based on the first position and the direction of movement, thus obtaining the second position.

[0137] In step S17, the time when the target terminal reaches the second position is calculated to obtain the second time.

[0138] For example, the second moment can be determined by combining the first moment, the speed information of the target terminal, and the distance between the first and second positions.

[0139] In step S18, the second ephemeris data of the second satellite is determined based on the second position, and the coverage area of ​​the second satellite at the second time includes the second position.

[0140] Reference Figure 6 The diagram illustrates a flowchart for acquiring a second point beam. The second position can be represented by base station positioning, GPS positioning, Wi-Fi positioning, and Bluetooth positioning. By inputting the second position into a third prediction model, the second ephemeris data predicted by the third prediction model can be obtained. Of course, in some implementation scenarios, the second time and the second position can also be input into the second prediction model to obtain the second ephemeris data.

[0141] In step S19, the second position, the second ephemeris data, and the second time are input into the first prediction model to obtain the second position at the second time and the second spot beam of the available second satellite, as output by the first prediction model.

[0142] It should be noted that, typically, a UE obtains a spot beam for downlink synchronization by scanning frequency bands and frequencies. Therefore, the UE needs to reach a specific location before it can acquire the spot beam. In the above solution, a second prediction model / third prediction model can be used to predict the second ephemeris data, and the second spot beam can be predicted using the second ephemeris data, the second time, the second location, and the first prediction model. In other words, this solution can predict the second spot beam available to the UE at the second location at the second time before the UE reaches the second location. This approach can further improve the UE's network connection speed and the speed of spot beam switching.

[0143] Based on the same inventive concept, this disclosure also provides a spot beam acquisition device. Figure 7 This is a block diagram of a spot beam acquisition device shown in this disclosure, with reference to... Figure 7 The spot beam acquisition device includes:

[0144] The first acquisition module 701 is configured to acquire the position of the target terminal at a first moment and obtain the first position.

[0145] The first determining module 702 is configured to determine the first ephemeris data of the first satellite based on the first location, wherein the coverage area of ​​the first satellite at the first moment includes the first location;

[0146] The first input module 703 is configured to input the first position, the first ephemeris data and the first time into the first prediction model to obtain the first spot beam of the first satellite available at the first position at the first time, output by the first prediction model.

[0147] The first prediction model is trained based on the first sample, which includes a sample point beam, a first sample time, a first sample position of the sample terminal at the first sample time, and the first sample ephemeris data corresponding to the first sample position. The sample point beam includes the point beam available to the sample terminal at the first sample position at the first sample time.

[0148] In the above technical solution, a first prediction model can be trained based on the first sample. This allows the terminal to obtain its first position at a first moment, determine the first ephemeris data of the first satellite based on the first position, and input the first position, the first ephemeris data, and the first moment into the first prediction model to obtain the first spot beam of the first satellite available at the first position at the first moment, as output by the first prediction model. In other words, the available satellite spot beams at the current moment and position can be quickly predicted using the first prediction model. Compared to scanning various frequency bands and frequencies of the terminal device to obtain spot beams, the above solution has a faster spot beam acquisition speed.

[0149] Optionally, the first determining module includes:

[0150] The first input submodule is configured to input the first position and the first time into the second prediction model to obtain the first ephemeris data output by the second prediction model;

[0151] The second prediction model is trained based on the second sample, which includes the second sample ephemeris data, the second sample time, and the second sample position of the sample terminal at the second sample time. The second sample ephemeris data includes the ephemeris data that the sample terminal can obtain at the second sample position at the second sample time.

[0152] Optionally, it includes:

[0153] The second acquisition module is configured to acquire the second sample position of the sample terminal at the second sample time.

[0154] The third acquisition module is configured to acquire the ephemeris data obtained by the sample terminal at the second sample position at the second sample time, and obtain the second sample ephemeris data.

[0155] The first generation module is configured to generate the second sample based on the second sample ephemeris data, the second sample time, and the second sample position.

[0156] The first training module is configured to take the second sample as input and the ephemeris data obtained by the predicted sample terminal at the second sample position at the second sample time as output to train the second prediction model.

[0157] Optionally, the first determining module includes:

[0158] The second input submodule is configured to input the first position into the third prediction model to obtain the first ephemeris data output by the third prediction model;

[0159] The third prediction model is trained based on a third sample, which includes the third sample location of the sample terminal and the third sample ephemeris data that the sample terminal can obtain at the third sample location.

[0160] Optionally, it includes:

[0161] The fourth acquisition module is configured to acquire the third sample location of the sample terminal;

[0162] The fifth acquisition module is configured to acquire the ephemeris data obtained by the sample terminal at the third sample location, thereby obtaining the third sample ephemeris data;

[0163] The second generation module is configured to generate a third sample based on the third sample ephemeris data and the position of the third sample.

[0164] The second training module is configured to take the third sample as input and the ephemeris data obtained by the predicted sample terminal at the location of the third sample as output to train the third prediction model.

[0165] Optionally, it includes:

[0166] The sixth acquisition module is configured to acquire the location information of the target terminal;

[0167] The direction determination module is configured to determine the movement direction of the target terminal based on the location information;

[0168] The location determination module is configured to determine the next location of the target terminal based on the first location and the direction of movement, thereby obtaining a second location;

[0169] The time calculation module is configured to calculate the time when the target terminal arrives at the second location, and obtain the second time.

[0170] The ephemeris determination module is configured to determine the second ephemeris data of the second satellite based on the second position, wherein the coverage area of ​​the second satellite at the second time includes the second position;

[0171] The second input module is configured to input the second position, the second ephemeris data, and the second time into the first prediction model to obtain the second spot beam of the second satellite available at the second time, as output by the first prediction model.

[0172] Optionally, it includes:

[0173] The seventh acquisition module is configured to acquire the first sample position of the sample terminal at the first sample time.

[0174] The eighth acquisition module is configured to acquire the point beam that the sample terminal can search for at the first sample position at the first sample time, and obtain the sample point beam.

[0175] The third generation module is configured to generate the first sample based on the sample point beam, the first sample time, and the first sample position.

[0176] The third training module is configured to take the first sample as input and the predicted point beam that the sample terminal can search for at the first sample position at the first sample time as output to train the first prediction model.

[0177] This disclosure also provides a spot beam acquisition device, comprising:

[0178] processor;

[0179] Memory used to store processor-executable instructions;

[0180] The processor is configured to perform the steps of the point beam acquisition method provided in this disclosure.

[0181] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the spot beam acquisition method provided in this disclosure.

[0182] Regarding the spot beam acquisition device in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments of the relevant spot beam acquisition method, and will not be elaborated here.

[0183] Figure 8 This is a block diagram illustrating an apparatus 800 for acquiring a spot beam according to an exemplary embodiment. For example, apparatus 800 may be a mobile phone, tablet device, etc.

[0184] Reference Figure 8 The device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output interface 812, a sensor component 814, and a communication component 816.

[0185] Processing component 802 typically controls the overall operation of device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the aforementioned spot beam acquisition method. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0186] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0187] Power supply component 806 provides power to various components of device 800. Power supply component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 800.

[0188] Multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0189] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0190] Input / output interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0191] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0192] Communication component 816 is configured to facilitate wired or wireless communication between device 800 and other devices. Device 800 can access wireless networks based on communication standards, such as WiFi, 4G, or 5G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0193] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described spot beam acquisition method.

[0194] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of the device 800 to complete the above-described spot beam acquisition method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0195] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described spot beam acquisition method when executed by the programmable device.

[0196] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of this disclosure. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0197] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method of acquiring a spot beam, characterized by, The method comprises: obtaining a first position of a target terminal at a first time; determining first ephemeris data of a first satellite according to the first position, wherein a coverage range of the first satellite at the first time comprises the first position; inputting the first position, the first ephemeris data and the first time into a first prediction model to obtain a first spot beam of a first satellite available at the first position at the first time output by the first prediction model; wherein the first prediction model is trained based on first samples, and the first samples comprise a sample spot beam, a first sample time, a first sample position of a sample terminal at the first sample time and first sample ephemeris data corresponding to the first sample position; obtaining position information of the target terminal; determining a moving direction of the target terminal according to the position information; determining a next position of the target terminal according to the first position and the moving direction to obtain a second position; calculating a time when the target terminal reaches the second position to obtain a second time; determining second ephemeris data of a second satellite according to the second position, wherein a coverage range of the second satellite at the second time comprises the second position; inputting the second position, the second ephemeris data and the second time into the first prediction model to obtain a second spot beam of a second satellite available at the second position at the second time output by the first prediction model.

2. The method of claim 1, wherein, The method further comprises: inputting the first position and the first time into a second prediction model to obtain the first ephemeris data output by the second prediction model; wherein the second prediction model is trained based on second samples, and the second samples comprise second sample ephemeris data, a second sample time and a second sample position of a sample terminal at the second sample time, and the second sample ephemeris data comprises ephemeris data that can be obtained by the sample terminal at the second sample position at the second sample time.

3. The method of claim 2, wherein, The method comprises: obtaining a second sample position of the sample terminal at a second sample time; obtaining ephemeris data obtained by the sample terminal at the second sample position at the second sample time to obtain second sample ephemeris data; generating a second sample according to the second sample ephemeris data, a second sample time and a second sample position; training the second prediction model by taking the second sample as input and taking predicted ephemeris data obtained by the sample terminal at the second sample position at the second sample time as output.

4. The method of claim 1, wherein, The method further comprises: inputting the first position into a third prediction model to obtain the first ephemeris data output by the third prediction model; wherein the third prediction model is trained based on third samples, and the third samples comprise a third sample position of a sample terminal and third sample ephemeris data that can be obtained by the sample terminal at the third sample position.

5. The method of claim 4, wherein, The method comprises: obtaining a third sample position of the sample terminal; obtaining ephemeris data obtained by the sample terminal at the third sample position to obtain third sample ephemeris data; generating a third sample according to the third sample ephemeris data and the third sample position; training the third prediction model by taking the third sample as input and taking predicted ephemeris data obtained by the sample terminal at the third sample position as output.

6. The method according to any one of claims 1 to 5, characterized in that, comprising: obtaining a first sample position of the sample terminal at a first sample time; obtaining a sample spot beam that can be searched by the sample terminal at the first sample position at the first sample time to obtain a sample spot beam; generating a first sample according to the sample spot beam, the first sample time and the first sample position; training the first prediction model by taking the first sample as input and taking a predicted spot beam that can be searched by the sample terminal at the first sample position at the first sample time as output.

7. An apparatus for acquiring a spot beam, the apparatus comprising: comprising: a first obtaining module configured to obtain a position of a target terminal at a first time to obtain a first position; a first determining module configured to determine first ephemeris data of a first satellite according to the first position, the coverage range of the first satellite at the first time including the first position; a first input module configured to input the first position, the first ephemeris data and the first time to a first prediction model to obtain a first spot beam of a first satellite available at the first position at the first time output by the first prediction model; wherein the first prediction model is trained based on a first sample, the first sample including a sample spot beam, a first sample time, a first sample position where a sample terminal is located at the first sample time and first sample ephemeris data corresponding to the first sample position, the sample spot beam including a spot beam available at the first sample position of the sample terminal at the first sample time; a sixth obtaining module configured to obtain position information of the target terminal; a direction determining module configured to determine a moving direction of the target terminal according to the position information; a position determining module configured to determine a next position of the target terminal according to the first position and the moving direction to obtain a second position; a time calculating module configured to calculate a time when the target terminal reaches the second position to obtain a second time; an ephemeris determining module configured to determine second ephemeris data of a second satellite according to the second position, the coverage range of the second satellite at the second time including the second position; a second input module configured to input the second position, the second ephemeris data and the second time to the first prediction model to obtain a second spot beam of a second satellite available at the second position at the second time output by the first prediction model.

8. A spot beam acquisition device, characterized in that, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the steps of the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon computer program instructions, wherein, The program instructions, when executed by the processor, implement the steps of the method of any one of claims 1 to 6.

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