Ephemeris data prediction method, device, electronic device and storage medium

Abstract

The present application provides a method, device, electronic device, and storage medium for ephemeris data prediction, relating to the field of communication technology. The method filters historical ephemeris data that meets set conditions to obtain filtered ephemeris data, and then predicts ephemeris data for future moments based on the filtered ephemeris data. For the base station side and the receiving device, filtering the historical ephemeris data can effectively remove noise and errors in the ephemeris data, thereby correcting measurement errors in ephemeris digitization and improving the accuracy of the ephemeris data. In this way, the accuracy of the ephemeris data sent by the base station side will also be higher, and the accuracy of the ephemeris data predicted by the receiving device will also be higher. In this way, the receiving device can obtain more accurate ephemeris data, thereby improving the accuracy of subsequent data processing.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device, electronic device and storage medium for ephemeris data prediction. Background Art

[0002] In satellite communications, due to the long distance between satellites and the ground, air interface latency is significant. Therefore, receiving devices must obtain satellite positions in advance to accurately calculate and compensate for timing advance. In some communication systems, ephemeris data carrying satellite position and velocity information can be distributed via relevant system information. For example, in the current Third Generation Partnership Project (3GPP) standard, this is distributed by base stations via System Information Blocks (SIBs). Because the base station's SIB message delivery period is inconsistent with the base station's ephemeris data update period, when the ephemeris update period is significantly longer than the SIB period, the ephemeris data carried in the SIB message may experience a time lag. Due to the high speed of satellite motion, if the time a receiving device receives ephemeris data is significantly different from the actual time corresponding to the ephemeris data, the actual satellite position / velocity at the time of reception will differ significantly from the position / velocity in the ephemeris data, affecting the accuracy of subsequent data processing by the receiving device, such as the inability to accurately synchronize time and frequency offset. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, electronic device and storage medium for predicting ephemeris data, so as to improve the problem in the prior art that the ephemeris time received by the receiving device is significantly different from the actual ephemeris data, affecting the accuracy of the receiving device when performing subsequent related data processing.

[0004] In a first aspect, an embodiment of the present application provides a method for predicting ephemeris data, the method comprising:

[0005] Filtering historical ephemeris data that meets set conditions to obtain filtered ephemeris data, wherein the set conditions include that the amount of historical ephemeris data reaches a set number, the filtering order of the filtering is N, N is an integer greater than or equal to 1, and the set number is N;

[0006] Based on the filtered ephemeris data, ephemeris data at a future time is predicted.

[0007] In the above implementation, historical ephemeris data that meets the specified conditions is filtered to obtain filtered ephemeris data, which is then used to predict ephemeris data for future times. For both the base station and the receiving device, filtering the historical ephemeris data effectively removes noise and errors from the data, correcting for measurement errors during ephemeris digitization and improving the accuracy of the data. This results in higher accuracy for the ephemeris data sent from the base station and higher accuracy for the ephemeris data predicted by the receiving device. This allows the receiving device to obtain more accurate ephemeris data, thereby improving the accuracy of subsequent data processing.

[0008] Optionally, filtering the historical ephemeris data that meets the set conditions to obtain filtered ephemeris data includes:

[0009] Acquire historical ephemeris data from an ephemeris data storage area, wherein the ephemeris data storage area stores the most recently obtained N ephemeris data;

[0010] The historical ephemeris data is filtered to obtain filtered ephemeris data.

[0011] In the above implementation process, since the ephemeris data storage area stores the latest N ephemeris data, all ephemeris data can be directly read out from the ephemeris data storage area for filtering without filtering the historical ephemeris data, which can simplify the operation and improve the efficiency of filtering.

[0012] Optionally, filtering the historical ephemeris data that meets the set conditions to obtain filtered ephemeris data includes:

[0013] The Kalman filter algorithm is used to filter the historical ephemeris data that meets the set conditions to obtain the filtered ephemeris data.

[0014] In the above implementation process, the filtering function in the Kalman filter algorithm can effectively filter out the noise and interference in the historical ephemeris data, so the accuracy of subsequent ephemeris data prediction can be improved.

[0015] Optionally, the ephemeris data prediction method is performed by a receiving device, and the ephemeris data at a future time is predicted based on the filtered ephemeris data, including:

[0016] Based on the filtered ephemeris data, ephemeris data for the receiving device at each future time-frequency offset synchronization moment is predicted, where each time-frequency offset synchronization moment is before the next ephemeris data reception moment. This enables ephemeris data to be predicted at each time-frequency offset synchronization moment, thereby improving the accuracy of time-frequency offset synchronization.

[0017] Optionally, the execution subject of the ephemeris data prediction method is a network device, and after predicting the ephemeris data at a future time based on the filtered ephemeris data, the method further includes:

[0018] Acquiring ephemeris data from a corresponding source according to a data selection strategy, wherein the sources of the ephemeris data include those obtained at the most recent ephemeris update time and those obtained by prediction;

[0019] The ephemeris data is carried in the system information and sent.

[0020] In the above implementation process, the network device side can flexibly select the corresponding source of ephemeris data for distribution according to the data selection strategy, so as to avoid the problem that the ephemeris data received by the receiving device differs greatly from the actual ephemeris data due to a time lag in the ephemeris data when the system information distribution cycle is inconsistent with the ephemeris data update cycle. In this way, the receiving device can obtain more accurate ephemeris data, thereby improving the accuracy of subsequent data processing, such as accurate time-frequency offset synchronization.

[0021] Optionally, the data selection strategy includes a time difference between the most recent ephemeris update time and the time when the system information is issued;

[0022] Alternatively, the data selection strategy includes the amount of ephemeris data predicted based on the filtered ephemeris data;

[0023] Alternatively, the data selection strategy includes the accuracy of a prediction algorithm used by the receiving device to predict relevant information using ephemeris data;

[0024] Alternatively, the data selection strategy includes indication information sent by a receiving device, wherein the indication information is used to indicate a source of the ephemeris data;

[0025] Alternatively, the data selection strategy includes a source of ephemeris data predicted by a machine learning algorithm.

[0026] In the above implementation process, multiple data selection strategies are set, which allows the network device to flexibly select an appropriate data selection strategy according to needs.

[0027] In a second aspect, an embodiment of the present application provides an ephemeris data prediction device, the device comprising:

[0028] a filtering module, configured to filter historical ephemeris data that meets set conditions to obtain filtered ephemeris data, wherein the set conditions include that the amount of historical ephemeris data reaches a set number, the filtering order of the filtering is N, where N is an integer greater than or equal to 1, and the set number is N;

[0029] The prediction module is used to predict the ephemeris data at a future time based on the filtered ephemeris data.

[0030] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect above are executed.

[0031] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method provided in the first aspect are executed.

[0032] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising computer program instructions, which, when read and executed by a processor, execute the steps in the method provided in the first aspect above.

[0033] Other features and advantages of the present application will be described in the following description and, in part, will become apparent from the description or be understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A schematic diagram of the structure of a communication system provided in an embodiment of the present application;

[0036] Figure 2 A flowchart of a method for predicting ephemeris data provided in an embodiment of the present application;

[0037] Figure 3 A detailed flow chart of filtering and predicting ephemeris data provided in an embodiment of the present application;

[0038] Figure 4 A schematic diagram of an ephemeris update period and a system information delivery period provided in an embodiment of the present application;

[0039] Figure 5 A structural block diagram of an ephemeris data prediction device provided in an embodiment of the present application;

[0040] Figure 6A schematic structural diagram of an electronic device for executing an ephemeris data prediction method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present application.

[0042] It should be noted that the terms "system" and "network" in the embodiments of the present invention are used interchangeably. "Multiple" refers to two or more. In view of this, in the embodiments of the present invention, "multiple" can also be understood as "at least two." "And / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / ", unless otherwise specified, generally indicates that the related objects are in an "or" relationship.

[0043] Please refer to Figure 1 , Figure 1 This is a structural diagram of a communication system 10 provided in an embodiment of the present application. The communication system 10 includes a network device 11 and a receiving device 12. The network device 11 is connected to the receiving device 12 in a wireless manner.

[0044] The network device 11 can be any device with wireless transceiver functions, including but not limited to an evolved base station (NodeB or eNB or e-NodeB, evolutionary NodeB) in LTE, a base station (gNodeB or gNB) or a transmission receiving point (TRP) in NR, a base station of subsequent evolution of 3GPP, etc. The base station can be: a macro base station, a micro base station, a pico base station, a small station, a relay station, or a balloon station, a satellite station, etc. The network device can also be a server, a wearable device, or a vehicle-mounted device, etc. The following description takes the network device as a base station as an example.

[0045] The receiving device 12 is a device with wireless transceiver function, which can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water surface (such as ships, etc.); it can also be deployed in the air (such as airplanes, balloons and satellites, etc.). The receiving device can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a vehicle-mounted terminal device, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a wearable terminal device, etc. The embodiments of the present application do not limit the application scenarios. The receiving device may also be sometimes referred to as user equipment (UE), access terminal equipment, vehicle-mounted terminal, industrial control terminal, UE unit, UE station, mobile station, mobile station, remote station, remote terminal equipment, mobile device, wireless communication device, UE agent, or UE device. The receiving device may be fixed or mobile.

[0046] In satellite communications, base stations obtain ephemeris data from the ephemeris platform and transmit it to receiving devices. Since this ephemeris data is calculated by the ephemeris platform using relevant algorithms, it contains certain errors. The receiving device calculates timing advance and performs time-frequency offset synchronization based on the ephemeris data transmitted by the base station. If the error in the original ephemeris data is large, the error in the subsequent processing performed by the receiving device will be further amplified. Furthermore, when the base station transmits ephemeris data, it transmits it via relevant system information. The time period for transmitting system information is inconsistent with the update period of ephemeris data. When the ephemeris update period is significantly longer than the system information transmission period, the transmitted ephemeris data may experience a time lag, which in turn affects the accuracy of the subsequent data processing performed by the receiving device.

[0047] To address the aforementioned issues, embodiments of the present application provide an ephemeris data prediction method. This method filters historical ephemeris data that meets set conditions to obtain filtered ephemeris data, and then predicts ephemeris data for future moments based on the filtered ephemeris data. For both the base station and the receiving device, filtering the historical ephemeris data effectively removes noise and errors from the ephemeris data, correcting measurement errors during ephemeris digitization and improving the accuracy of the ephemeris data. On the base station side, predictions can be made based on the filtered ephemeris data. This allows the predicted ephemeris data to be selected for delivery when system information is transmitted, thereby avoiding the issue of older historical ephemeris data being delivered, which could result in significant differences from the actual ephemeris data. This allows the receiving device to ensure the accuracy of subsequent data processing. On the receiving device side, ephemeris data for each time-frequency offset synchronization moment can be predicted based on the filtered ephemeris data, avoiding the issue of large errors resulting from using the ephemeris data delivered by the base station for time-frequency offset synchronization, thereby improving the accuracy of time-frequency offset synchronization.

[0048] That is to say, the execution subject of the ephemeris data prediction method of the present application can be a base station or a receiving device, and the subsequent embodiments will introduce different execution subjects respectively.

[0049] Please refer to Figure 2 , Figure 2 A flowchart of a method for predicting ephemeris data provided in an embodiment of the present application, the method comprising the following steps:

[0050] Step S110: Filtering the historical ephemeris data that meets the set conditions to obtain filtered ephemeris data.

[0051] Step S120: Predicting ephemeris data at a future time based on the filtered ephemeris data.

[0052] If the base station is executing this method, it will send the ephemeris data along with the system information at the time of sending the system information. To select the ephemeris data that is most beneficial to the receiving device, the ephemeris data to be sent can be either predicted based on the filtered ephemeris data or the ephemeris data obtained at the most recent ephemeris update time.

[0053] Regardless of the type of ephemeris data to be sent, the base station can predict the ephemeris data and make a selection based on the corresponding strategy at the time of sending. For example, after obtaining the latest ephemeris data from the ephemeris platform, the base station predicts the ephemeris data for the next time the system information is sent.

[0054] Regarding the implementation method of the base station side when predicting ephemeris data, the historical ephemeris data may include the ephemeris data obtained by the base station from the ephemeris platform at the ephemeris update time before the current time, including the updated ephemeris data currently obtained. For example, if the ephemeris update period is 1 second, that is, the base station updates the ephemeris data once every 1 second, if the system information delivery period is 0.64 seconds, starting from the 0th second, the updated ephemeris data is obtained at the 0th second. At this time, the historical ephemeris data includes the updated ephemeris data obtained at the 0th second. After obtaining the updated ephemeris data at the 0th second, the base station side can filter the ephemeris data at the 0th second, and then predict the ephemeris data at the 0.64th second based on the filtered ephemeris data. When sending the system information at the 0.64th second, it can choose to send the updated ephemeris data obtained at the 0th second or the predicted ephemeris data.

[0055] The base station side continues to obtain the latest ephemeris data from the ephemeris platform at the 1st second. At this time, the historical ephemeris data may include the updated ephemeris data at the 0th second and the updated ephemeris data at the 1st second. These two ephemeris data are then filtered, and then the ephemeris data at the 1.28th second is predicted based on the filtered data. When sending the system information at the 1.28th second, you can choose to send the updated ephemeris data obtained at the 1st second or the predicted ephemeris data.

[0056] Alternatively, the historical ephemeris data may include the ephemeris data obtained by the base station at the ephemeris update time before the current moment and the predicted ephemeris data. Continuing with the above example, for example, if the current moment is 0.64 seconds, the historical ephemeris data includes the ephemeris data updated at 0 seconds and the ephemeris data predicted based on the ephemeris data updated at 0 seconds. If the current moment is 1.28 seconds, the historical ephemeris data may include the ephemeris data updated at 0 seconds and 1 seconds, the ephemeris data predicted based on the ephemeris data updated at 0 seconds, and the ephemeris data predicted based on the ephemeris data updated at 1 second, and / or, the ephemeris data predicted based on the ephemeris data updated at 0 seconds and 1 second.

[0057] That is, the historical ephemeris data may refer to all ephemeris data obtained before the current moment, or may refer to partial ephemeris data, such as ephemeris data obtained at the ephemeris update time, or ephemeris data obtained at the ephemeris update time and predicted ephemeris data.

[0058] If the receiving device is executing this method, it can receive ephemeris data sent by the base station via system information. This ephemeris data can be actual or predicted by the base station. Because the system information delivery period is inconsistent with the time-frequency offset synchronization period, the receiving device can achieve more accurate time-frequency offset synchronization by predicting the ephemeris data at each time-frequency offset synchronization moment.

[0059] For example, after receiving the ephemeris data carried in the system information, the receiving device can store it as historical ephemeris data. When it is necessary to predict the ephemeris data of each future time-frequency offset synchronization moment, the historical ephemeris data can be obtained for filtering, and then the ephemeris data of each time-frequency offset synchronization moment can be predicted based on the filtered ephemeris data.

[0060] For a receiving device, historical ephemeris data may include all ephemeris data received from system information, or may include ephemeris data received from system information in the most recent period, or may include all or part of the ephemeris data received from system information and previously predicted ephemeris data.

[0061] In some embodiments, the ephemeris data may include the position and velocity of the satellite, and may also include orbital parameter information, etc.

[0062] Whether it is a base station or a receiving device, since the original ephemeris data may have certain errors, when performing ephemeris data prediction, the historical ephemeris data that meets the set conditions are first filtered to obtain filtered ephemeris data. The set conditions here may include that the number of historical ephemeris data reaches a set number. The reason for setting this set condition is that if the amount of historical ephemeris data is small, it may affect the prediction accuracy. If the prediction accuracy is not high, it is better to directly use the original ephemeris data. Therefore, after the number of historical ephemeris data reaches the set number, the historical ephemeris data is filtered, and prediction is performed based on the filtered ephemeris data.

[0063] On the base station side, continuing with the above example, it is assumed that the set number is set to 2, and the updated ephemeris data is obtained at the 0th second. At this time, the number of historical ephemeris data is 1, which has not reached the set number. Then, when the ephemeris data needs to be sent at 0.64 seconds, the updated ephemeris data obtained at the 0th second can be directly selected for sending. The base station obtains the updated ephemeris data at the 1st second. At this time, the number of historical ephemeris data is 2, which reaches the set number. At this time, the historical ephemeris data can be filtered and then predicted. Of course, when the base station sends the ephemeris data at 1.28 seconds, it can choose whether to send the updated ephemeris data at the 1st second or the predicted ephemeris data according to the corresponding strategy. In the subsequent process, since the number of historical ephemeris data has reached the set number, the subsequent process can filter the historical ephemeris data and then predict it, which will not be elaborated here.

[0064] If the executor of the ephemeris data prediction method is a receiving device, then on the receiving device side, in order to achieve accurate time-frequency offset synchronization, the ephemeris data of the receiving device at each future time-frequency offset synchronization moment can be predicted based on the filtered ephemeris data, where each time-frequency offset synchronization moment is before the next ephemeris data reception moment.

[0065] Assume that the system information transmission period is 0.64 seconds, that is, the receiving device receives ephemeris data every 0.64 seconds, performs time-frequency offset synchronization every 100 ms (i.e., the receiving device needs to predict ephemeris data every 100 ms), and the set number is set to 2. The receiving device receives ephemeris data sent by the base station at 0 seconds. At this time, the number of historical ephemeris data is 1, which does not reach the set number. The receiving device then predicts ephemeris data for the next six time-frequency offset synchronization moments based on the ephemeris data received at 0 seconds. The receiving device receives ephemeris data sent by the base station at 0.64 seconds. At this time, the number of historical ephemeris data is 2, which reaches the set number. The receiving device then filters the two historical ephemeris data and predicts the ephemeris data for the next six time-frequency offset synchronization moments. Since the number of historical ephemeris data reaches the set number, the subsequent process can filter and predict the historical ephemeris data. This is not detailed here.

[0066] In addition, in the process of filtering the historical ephemeris data, a multi-order filter may be used for filtering, and the filtering order is N, where N is an integer greater than or equal to 1, and the set number is also N.

[0067] A multi-order filter is composed of multiple filter cascades, and each cascaded filter can be called a first-order filter. A multi-order filter can provide a steeper transition band, thereby more accurately controlling the frequency response to better suppress unwanted frequency components.

[0068] If the filter order is N, it means that an N-order filter is used to filter the historical ephemeris data. If the number is set to N, it means that N historical ephemeris data are selected for filtering. The N historical ephemeris data here can be understood as the most recent N historical ephemeris data.

[0069] In a specific implementation, each historical ephemeris data can be input into a filter for filtering. For example, assuming N is 2, the first historical ephemeris data can be used as the input of the first filter, and the second historical ephemeris data can be used as the input of the second filter. Of course, since the first filter and the second filter are cascaded, the output of the first filter will continue to be input into the second filter for further filtering. Alternatively, each historical ephemeris data can be input into two filters for filtering. For example, both historical ephemeris data can be input into the first filter, and the output of the first filter will continue to be input into the second filter. The output of the second filter is the filtered ephemeris data.

[0070] In some implementations, the multi-order filter may be implemented using a recursive filter or a non-recursive filter, and the specific filter type is not specifically limited in this solution.

[0071] By using N-order filtering to filter N historical ephemeris data, the measurement error in the ephemeris data can be effectively reduced, and the accuracy of subsequent ephemeris data prediction can be improved through the filtered ephemeris data.

[0072] In the above implementation, historical ephemeris data that meets the specified conditions is filtered to obtain filtered ephemeris data, which is then used to predict ephemeris data for future times. For both the base station and the receiving device, filtering the historical ephemeris data effectively removes noise and errors from the data, correcting for measurement errors during ephemeris digitization and improving the accuracy of the data. This results in higher accuracy for the ephemeris data sent from the base station and higher accuracy for the ephemeris data predicted by the receiving device. This allows the receiving device to obtain more accurate ephemeris data, thereby improving the accuracy of subsequent data processing.

[0073] Based on the above embodiment, historical ephemeris data can be stored in the ephemeris data storage area. When filtering is performed, historical ephemeris data can be obtained from the ephemeris data storage area, wherein the ephemeris data storage area stores the most recent N ephemeris data, and then the historical ephemeris data is filtered to obtain filtered ephemeris data.

[0074] On the base station side, the latest N ephemeris data may refer to the ephemeris data obtained at the latest N ephemeris update times, or may refer to the N ephemeris data obtained through updating and prediction. On the receiving device side, the latest N ephemeris data may refer to the N ephemeris data received most recently.

[0075] The storage space of the ephemeris data storage area is effective. Assume that N is 2, that is, the ephemeris data storage area can only store 2 ephemeris data. If the base station or the receiving device obtains the third ephemeris data to be stored in the ephemeris data storage area, the first ephemeris data stored first can be deleted. At this time, the ephemeris data storage area stores the second and third obtained ephemeris data, that is, the ephemeris data storage area stores the latest two ephemeris data. In this way, the historical ephemeris data obtained from the ephemeris data storage area meets the set conditions, and there is no need to filter the historical ephemeris data, which is more efficient.

[0076] In the above implementation process, since the ephemeris data storage area stores the latest N ephemeris data, all ephemeris data can be directly read out from the ephemeris data storage area for filtering without filtering the historical ephemeris data, which can simplify the operation and improve the efficiency of filtering.

[0077] On the basis of the above embodiment, in order to improve the accuracy of filtering, a Kalman filter algorithm may be used to filter the historical ephemeris data that meets the set conditions to obtain filtered ephemeris data.

[0078] It can be understood that in the present application scheme, the Kalman filter algorithm is not used to directly predict the ephemeris data, but is used to filter the ephemeris data. This is because the Kalman filter algorithm must have corresponding observation data at the corresponding time position (that is, there is ephemeris data at each time-frequency offset synchronization position) to perform filtering prediction. However, in the scenario of satellite communication, since the time-frequency offset synchronization period is very short, it is impossible to obtain ephemeris data at each time-frequency offset synchronization moment, and if the filtering period is too short, the operation delay may not meet the requirements. Therefore, in this scheme, the historical ephemeris data can be filtered by the Kalman filter algorithm.

[0079] Taking the receiving device for filtering prediction as an example, it is assumed that the receiving device updates the ephemeris data every 1 second, that is, it receives the ephemeris data sent by the base station every 1 second, and then performs time-frequency offset synchronization every 10ms (the ephemeris data every 10ms needs to be calculated through prediction). The filtering algorithm is the Kalman filter algorithm, where the order of the Kalman filter is k=2, the number is set to 2, and the position / velocity error variance calculated based on the measurement error statistics is σ p =σ v =0.1.

[0080] At the 0th second, n=1, the receiving device receives the ephemeris data sent by the base station for the first time and stores it in the ephemeris data storage area. At this time, the number of historical ephemeris data is 1, which has not reached the set data. The receiving device then directly predicts the ephemeris data for the time-frequency offset synchronization time t=(0.01, 0.02, ..., 0.09, 1) between the 0th second and the 1st second based on the received ephemeris data.

[0081] At the first second, n=2, the receiving device receives the ephemeris data sent again by the base station and stores it in the ephemeris data storage area. At this time, the number of historical ephemeris data is 2, which reaches the set number. The Kalman filter algorithm is then used to filter the two ephemeris data in the ephemeris data storage area (the above-mentioned variance can be used in the filtering to filter out the variance. The specific implementation method can refer to the implementation process of the relevant Kalman filter algorithm and is not described in detail here). The filtered ephemeris data is output, and then the ephemeris data at the time-frequency offset synchronization time t=(0.01, 0.02, ..., 0.09, 1) within the next second is predicted based on the filtered ephemeris data.

[0082] Each time the next ephemeris data receiving cycle is entered, n=n+1, the above steps are repeated to predict the ephemeris data at each time-frequency offset synchronization moment. The specific implementation process can be as follows: Figure 3 As shown, Figure 3 The set quantity is expressed as N.

[0083] In the above implementation process, the filtering function in the Kalman filter algorithm can effectively filter out the noise and interference in the historical ephemeris data, so the accuracy of subsequent ephemeris data prediction can be improved.

[0084] Based on the above embodiment, there are several ways to implement the above-mentioned prediction of the ephemeris data at the future time based on the filtered ephemeris data:

[0085] (1) Linear prediction:

[0086] A linear model can be used to predict the ephemeris data of future moments based on the filtered ephemeris data. For example, the ephemeris data includes position and speed. The distance traveled by the satellite can be calculated based on the speed and time difference in the filtered ephemeris data. The position of the satellite at the current moment can then be obtained based on the position in the filtered ephemeris data. The satellite's operating speed may not change much, so the speed in the filtered ephemeris data can be used as the speed at the current moment.

[0087] (2) Using the neural network model to predict the ephemeris data at the future time based on the filtered ephemeris data:

[0088] Neural network models, such as recurrent neural network models and long short-term memory network models, are used to learn the satellite's motion pattern from filtered ephemeris data and predict its future position and speed. Since deep learning models have strong learning capabilities, they can predict more accurate ephemeris data.

[0089] It is understandable that in practical applications, there may be other ways to predict ephemeris data, such as sampling-based methods (eg, particle filtering), which are not listed here one by one.

[0090] Based on the above embodiment, the base station side sends the ephemeris data according to the sending period of the system information. Generally, the ephemeris update period is longer than the system information sending period. Therefore, when the system information sending time arrives, the ephemeris data may be updated at a distant time. In this case, if the ephemeris data obtained at the ephemeris update time is directly sent, it may result in a large deviation from the actual ephemeris data at the current moment. Therefore, in order to enable the receiving device to obtain more accurate ephemeris data, the latest ephemeris data can be predicted based on the filtered ephemeris data and then selected for sending.

[0091] However, in some cases, the receiving device may use ephemeris data to predict the ephemeris data for subsequent time-frequency offset synchronization moments. If the ephemeris data in the system information has large errors, using this data to perform satellite orbit prediction calculations on the receiving device may cause error amplification, thereby affecting time-frequency offset synchronization performance and preventing stable access. Therefore, if the base station sends predicted ephemeris data, there may be a certain deviation between the predicted ephemeris data and the actual ephemeris data. This may cause the receiving device to use the base station's predicted ephemeris data instead of the original measured ephemeris data for prediction, which in turn causes error amplification.

[0092] Therefore, based on the above considerations, a data selection strategy is set on the base station side. According to the data selection strategy, it is possible to choose whether to send the ephemeris data obtained at the most recent ephemeris update time or the ephemeris data predicted based on the filtered ephemeris data. Even if the predicted ephemeris data is sent, the accuracy of the predicted ephemeris data will be higher because it is obtained by filtering the historical ephemeris data first and then predicting it.

[0093] If the executor of the ephemeris data prediction method is a network device, such as a base station, the ephemeris data of the corresponding source can be obtained according to the data selection strategy, where the sources of the ephemeris data include those obtained at the most recent ephemeris update time and those obtained by prediction, and then the ephemeris data is carried in the system information and sent.

[0094] The data selection strategy may be pre-configured in the base station, or may be configured based on relevant feedback from the receiving device.

[0095] The satellite sends ephemeris data in the following two ways:

[0096] (1) The satellite's ephemeris data is calculated by the ground base station and sent to the receiving equipment on the ground via the satellite.

[0097] (2) The satellite's ephemeris data is calculated by the base station on the satellite and sent to the receiving equipment on the ground.

[0098] To conserve resources when distributing ephemeris data, it can be sent to the receiving device via system information. For example, in satellite communications, ephemeris data can be sent to the receiving device via other system information (OSI). OSI refers to one or more SIBs other than the master information block (MIB) and system information block 1 (SIB1). OSI can be carried in system information (SI) messages. Each SI message contains one or more SIBs other than SIB1 that share the same scheduling requirements (e.g., the same transmission period).

[0099] Alternatively, the ephemeris data may also be sent via the SIB, that is, the ephemeris data is carried in the SIB and sent to the receiving device.

[0100] In the above implementation process, the network device side can flexibly select the corresponding source of ephemeris data for distribution according to the data selection strategy, so as to avoid the problem that the ephemeris data received by the receiving device differs greatly from the actual ephemeris data due to a time lag in the ephemeris data when the system information distribution cycle is inconsistent with the ephemeris data update cycle. In this way, the receiving device can obtain more accurate ephemeris data, thereby improving the accuracy of subsequent data processing, such as accurate time-frequency offset synchronization.

[0101] In the embodiments of the present application, the following methods for implementing data selection strategies are provided. Based on these data selection strategies, the base station can flexibly select ephemeris data from corresponding sources for distribution.

[0102] (a) The data selection strategy includes the time difference between the last ephemeris update time and the time when the system information is sent.

[0103] Since the ephemeris update cycle and the system information issuance cycle are inconsistent, in order to issue the ephemeris data closest to the current time as much as possible, the data selection strategy may include the time difference between the most recent ephemeris update time and the system information issuance time. When obtaining ephemeris data from the corresponding source, if the time difference is greater than or equal to the set time difference, the ephemeris data predicted based on the filtered ephemeris data can be obtained. If the time difference is less than the set time difference, the ephemeris data obtained at the most recent ephemeris update time is obtained.

[0104] Among them, the time of issuing the system information can be understood as the current moment. In order to facilitate the rapid selection of the ephemeris data of the corresponding source when the system information is issued, the base station can filter the historical ephemeris data after obtaining the ephemeris data at each ephemeris update time (of course, if it is the first time to obtain the updated ephemeris data, if the number of historical ephemeris data does not reach the set number, then filtering is not required; if it reaches the set number, then filtering is required), and then predict the ephemeris data at the time of issuing the next system information based on the filtered ephemeris data.

[0105] like Figure 4 As shown in the figure, the system information is issued at a period of 0.64 seconds, the time difference is set to 0.64 seconds, and the ephemeris data update period is 1 second. The time of the first update of the ephemeris data coincides with the time of the first issuance of the system information, which is recorded as the 0th second.

[0106] The base station obtains updated ephemeris data at the 0th second, and then sends the updated ephemeris data through the system information at the 0th second. The base station predicts the ephemeris data at the 0.64th second based on the filtered ephemeris data updated at the 0th second.

[0107] At 0.64 seconds, the time difference between the most recent ephemeris update time (0 seconds) and the time when the system information is sent (0.64 seconds) is 0.64 seconds, which is equal to the set time difference. At this time, the predicted ephemeris data is sent through the system information at 0.64 seconds.

[0108] At the first second, the base station enters the next ephemeris update cycle and can obtain updated ephemeris data. The most recent ephemeris update time is the first second. The base station can predict the ephemeris data for the 1.28th second based on the filtered ephemeris data updated at the first second, or based on the filtered ephemeris data updated at the first second and the ephemeris data updated at the 0th second. The current prediction timing can be to predict the ephemeris data for the next system information delivery time after the ephemeris data is updated, or to predict the ephemeris data at the next system information delivery time or within a period before the delivery time of the next system information.

[0109] At 1.28 seconds, the time difference with the most recent ephemeris update time (1 second) is 0.28 seconds, which is less than the set time difference. At this time, the ephemeris data updated at the 1st second is sent through the system information.

[0110] The base station predicts the ephemeris data at 1.92 seconds based on the ephemeris data updated at 1 second, or based on the filtered ephemeris data updated at 1 second and the ephemeris data updated at 0 second. At 1.92 seconds, the time difference between the ephemeris data and the most recent ephemeris update time (1 second) is 0.92 seconds, which is greater than the set time difference. At this time, the predicted ephemeris data can be sent through system information.

[0111] The subsequent delivery of ephemeris data is similar to the above process, and for the sake of brevity, it will not be repeated here.

[0112] Understandably, the time difference can, in principle, be flexibly set based on actual needs. However, in practice, to ensure that the receiving device receives the latest actual ephemeris data, setting the time difference requires certain constraints, such as setting it to be less than or equal to the system information delivery period (0.64 seconds). The principle for setting this is to avoid delivering ephemeris data that is too old. If the most recently updated ephemeris data is too old, predicted ephemeris data will be delivered.

[0113] In the above implementation process, the source of the ephemeris data is determined by the time difference. If the updated ephemeris data is far away from the current time, the predicted ephemeris data is selected for distribution. If the updated ephemeris data is close to the current time, the most recently updated ephemeris data is selected for distribution, thereby reducing the time lag of the ephemeris data.

[0114] (b) The data selection strategy includes the amount of ephemeris data predicted based on the filtered ephemeris data.

[0115] To improve the efficiency of information transmission, base stations generally consider predicting ephemeris data in advance. For example, after each update of ephemeris data, the ephemeris data for multiple subsequent system information transmission time points can be predicted. This may result in a large amount of predicted ephemeris data. If the predicted ephemeris data is selected for transmission, data transmission efficiency may be affected. In view of this situation, the data selection strategy in the embodiment of the present application may include the amount of ephemeris data predicted based on the filtered ephemeris data. If the data amount is greater than or equal to the set data amount, the ephemeris data obtained at the most recent ephemeris update time is obtained. If the data amount is less than the set data amount, the ephemeris data predicted based on the filtered ephemeris data is obtained.

[0116] With the above Figure 4 Taking the example in as an example, the data volume is set to M, assuming that M is equal to the data volume of the ephemeris data obtained at the most recent ephemeris update time.

[0117] The base station obtains updated ephemeris data at the 0th second, and then sends the updated ephemeris data through the system information at the 0th second. The base station predicts the ephemeris data at the 0.64th second based on the ephemeris data updated at the 0th second.

[0118] At 0.64 seconds, if the data volume of the predicted ephemeris data is less than the data volume of the ephemeris data updated at 0 seconds, the predicted ephemeris data is selected for delivery.

[0119] At the first second, the base station obtains the newly updated ephemeris data and predicts the ephemeris data at 1.28 seconds and 1.92 seconds based on the ephemeris data updated at the first second.

[0120] At 1.28 seconds, if the data volume of the predicted ephemeris data is greater than the data volume of the ephemeris data updated at the first second, the data volume of the ephemeris data updated at the first second is selected for transmission.

[0121] The subsequent process of sending ephemeris data is similar to the above process and will not be repeated here for the sake of brevity. The data volume of the predicted ephemeris data is compared with the set data volume. If the data volume is larger, the updated ephemeris data is sent; if the data volume is smaller, the predicted ephemeris data is sent.

[0122] It is understandable that the set data volume in the above example refers to the data volume of the ephemeris data obtained at the most recent ephemeris update time. In actual situations, the set data volume can be flexibly set according to the data transmission efficiency requirements.

[0123] In the above implementation process, the source of the ephemeris data is determined by the data volume. When the data volume is large, the updated ephemeris data is selected for distribution. When the data volume is small, the predicted ephemeris data is selected for distribution, which can save transmission resources.

[0124] (c) The data selection strategy includes the accuracy of the prediction algorithm used by the receiving device to predict relevant information using the ephemeris data.

[0125] Since the receiving device may use the received ephemeris data to predict relevant information, such as predicting the ephemeris data of each subsequent time-frequency offset synchronization moment, and the predicted ephemeris data itself may contain errors, if the prediction algorithm of the receiving device is not accurate, and the receiving device makes predictions based on the ephemeris data predicted by the base station, it may lead to the problem of further amplification of the prediction results. Therefore, taking this situation into consideration, the data selection strategy in this application may include the accuracy of the prediction algorithm used by the receiving device to predict relevant information using ephemeris data. If it is greater than or equal to the set accuracy, the ephemeris data obtained by predicting the filtered ephemeris data is obtained. If the accuracy is less than the set accuracy, the ephemeris data obtained at the most recent ephemeris update time is obtained.

[0126] The accuracy of the prediction algorithm of the receiving device can be fed back to the base station in real time or periodically. For example, before each system information is delivered, the base station can send an accuracy acquisition request to the receiving device, so that the receiving device can provide feedback on the accuracy of the current prediction algorithm to the base station. Alternatively, before each system information is delivered, the receiving device can proactively provide feedback on the accuracy of the current prediction algorithm to the base station.

[0127] The set accuracy can be flexibly set based on the actual prediction accuracy of the receiving device. For example, at the time each system information is delivered, if the base station determines that the current most recent accuracy is greater than or equal to the set accuracy, this indicates that the receiving device's current prediction algorithm has high accuracy. In this case, the predicted ephemeris data can be delivered. If the base station determines that the current most recent accuracy is less than the set accuracy, this indicates that the receiving device's current prediction algorithm has low accuracy. In this case, to improve the accuracy of the receiving device's prediction results, the ephemeris data obtained at the most recent ephemeris update time can be delivered. Because the updated ephemeris data is actual ephemeris data, even if the receiving device's prediction algorithm has low accuracy, the actual ephemeris data can be used to correct the prediction accuracy, reducing the problem of further error amplification.

[0128] In the above implementation process, the source of the ephemeris data is determined by its accuracy. When the accuracy is high, the predicted ephemeris data is selected for delivery. When the accuracy is low, the updated ephemeris data is selected for delivery. This can improve the prediction accuracy of the receiving device.

[0129] In some embodiments, the above lists multiple implementation methods for predicting ephemeris data. Different implementation methods may correspond to different prediction accuracies. Therefore, after obtaining the accuracy information of the prediction algorithm sent by the receiving device, the base station can choose to use the corresponding method to predict the ephemeris data according to the accuracy.

[0130] For example, if the accuracy is greater than or equal to the set accuracy, it indicates that the accuracy of the prediction algorithm of the receiving device is high. In this case, method (1) can be selected to achieve fast prediction. If the accuracy is less than the set accuracy, it indicates that the accuracy of the prediction algorithm of the receiving device is low. In this case, method (2) can be selected to perform prediction. This can improve the accuracy of the prediction. When the subsequent receiving device uses the predicted ephemeris data for prediction, its prediction accuracy can also be further improved.

[0131] In this way, the base station can flexibly select the corresponding prediction method according to the needs of the receiving device to realize the prediction of ephemeris data, which can take into account both the efficiency and accuracy of the prediction.

[0132] (d) The data selection strategy includes the indication information sent by the receiving device.

[0133] The data selection strategy may include indication information sent by the receiving device. For example, the receiving device may provide feedback to the base station regarding whether to send predicted ephemeris data or the ephemeris data obtained at the most recent ephemeris update time, based on its current prediction needs. Specifically, the base station may send a prompt to the receiving device before the system information is sent, prompting the receiving device to provide feedback regarding whether to send predicted ephemeris data or updated ephemeris data. Alternatively, the receiving device may proactively provide feedback to the base station regarding whether to send predicted ephemeris data or updated ephemeris data before the system information is sent.

[0134] If the indication information indicates that the source of the ephemeris data is obtained based on the prediction of historical ephemeris data, then at the time when the system information is sent, the ephemeris data obtained based on the prediction of the filtered ephemeris data is obtained and sent; if the indication information indicates that the source of the ephemeris data is obtained at the most recent ephemeris update time, then the ephemeris data obtained at the most recent ephemeris update time is obtained and sent.

[0135] In some embodiments, the indication information can be represented by a related identifier, such as identifier 1, which represents the sending of predicted ephemeris data, and identifier 0, which represents the sending of updated ephemeris data. Of course, the specific identification information can be set flexibly, so that the indication information sent by the receiving device can be identification information, which can reduce the occupancy of transmission resources.

[0136] In the above implementation process, the source of the ephemeris data is determined by the indication information, so that the ephemeris data to be sent can be flexibly selected according to the needs of the receiving device.

[0137] (e) The data selection strategy includes the source of ephemeris data predicted by the machine learning algorithm.

[0138] Among them, machine learning algorithms include decision trees, random forests, support vector machines, neural network models, etc. When selecting ephemeris data for delivery, if the source of the ephemeris data predicted by the machine learning algorithm is obtained at the most recent ephemeris update time, the ephemeris data obtained at the most recent ephemeris update time is obtained and delivered; if the source of the ephemeris data predicted by the machine learning algorithm is obtained based on filtered ephemeris data, the ephemeris data predicted based on the filtered ephemeris data is obtained and delivered.

[0139] When using a machine learning algorithm for prediction, historical data can be collected and input into the algorithm, including historical ephemeris data, issuance records, changes in ephemeris data, network delays, satellite-related attribute data (for example, different satellites may have different issuance patterns for ephemeris data), predicted ephemeris data, the accuracy of predicted ephemeris data, and other information. The machine learning algorithm can use these historical data to achieve intelligent prediction to predict whether to select the most recently updated ephemeris data or the predicted ephemeris data for this issuance.

[0140] Of course, it is also possible to first collect all data related to the ephemeris data delivery, such as the interaction data between the base station and the receiving device, and then use statistical tests or model evaluation techniques (such as random forest importance scoring) to determine the data related to the ephemeris data delivery decision. These data can be used as input data for predicting the source.

[0141] It is understandable that the machine learning algorithm can be trained in advance by collecting a large amount of data so that the training algorithm can learn the correlation between these data and the decision of sending ephemeris data, which is conducive to finding the best ephemeris data during actual prediction.

[0142] For the prediction results output by the machine learning algorithm, the confidence levels of two sources can be output. If the confidence level of a certain source is higher, the ephemeris data from that source is selected for delivery. For example, if the confidence level of the ephemeris data obtained at the most recent ephemeris update time is higher, the ephemeris data obtained at the most recent ephemeris update time is selected for delivery. Otherwise, the ephemeris data obtained by prediction is selected for delivery.

[0143] In some cases, if the machine learning algorithm cannot make a decision, such as outputting equal confidence levels from two sources, several data selection strategies provided in the above embodiments can be selected to re-determine the source of the ephemeris data.

[0144] In the above implementation process, the source of the ephemeris data is intelligently predicted through a machine learning algorithm, so that the best ephemeris data can be automatically selected for delivery, thereby improving the accuracy of the ephemeris data delivery.

[0145] In some implementations, the base station can be configured with multiple data selection strategies as described above. Each data selection strategy can be prioritized, with higher-priority strategies activated first. The priority of each data selection strategy can be adjusted based on feedback from the receiving device or determined by the base station using a machine learning algorithm. The base station can also independently decide which data selection strategy to activate, or negotiate with the receiving device to activate the strategy. In practice, the specific data selection strategy chosen can be flexibly selected based on actual needs.

[0146] It should be noted that, for the base station side, it predicts the ephemeris data at the time when the system information is sent, and for the receiving device side, it can predict the ephemeris data at each time-frequency offset synchronization moment. Regardless of whether the base station sends the most recently updated ephemeris data or the predicted ephemeris data, the receiving device side can filter the received ephemeris data before making the prediction in order to further improve the data accuracy. Therefore, if the execution subjects of the method provided by this application are different, when executing the above steps S110 and S120, the historical ephemeris data obtained may be different (for example, the historical ephemeris data on the base station side includes the updated ephemeris data, while the historical ephemeris data on the receiving device side includes the ephemeris data sent by the base station, which may be the updated ephemeris data or the ephemeris data predicted by the base station), and the predicted time may be different.

[0147] Based on the above embodiment, in order to facilitate the receiving device to distinguish whether the received ephemeris data is predicted ephemeris data or updated ephemeris data, the system information may further carry an identifier for representing the source of the ephemeris data.

[0148] For example, if it is predicted ephemeris data, the identifier is 0, and if it is updated ephemeris data, the identifier is 1. In this way, the receiving device can quickly know the source of the currently received ephemeris data based on the identifier, and can choose whether to use it according to needs. For example, in some cases, the receiving device wants to use updated ephemeris data, but the received data is predicted ephemeris data. In this case, the receiving device can choose to discard the received ephemeris data and choose to use the updated ephemeris data obtained previously, or wait for the next ephemeris data reception.

[0149] It is understandable that the specific form of the identifier representing the source of the ephemeris data can also be flexibly set according to actual needs.

[0150] In the above implementation process, the receiving device can quickly identify the source of the ephemeris data received this time.

[0151] Based on the above embodiment, in some cases, the receiving device may make relevant predictions based on the time of the ephemeris data, so the system information may also carry a time field. If the source of the ephemeris data is obtained from the most recent ephemeris update time, the time field is the most recent ephemeris update time. If the source of the ephemeris data is obtained by prediction based on filtered ephemeris data, the time field is the time when the system information was sent.

[0152] Continue with the above Figure 4 For example, when the base station sends the system information at the 0th second, the updated ephemeris data is carried in the system information, and the time field is the 0th second, and the identifier representing the source of the ephemeris data is 1;

[0153] When the base station sends the system information at the 0.64th second, it carries the predicted ephemeris data in the system information, and the time field is the 0.64th second, and the identifier representing the source of the ephemeris data is 0;

[0154] When the base station sends the system information at 1.28 seconds, it carries the ephemeris data updated at the 1st second in the system information, and the time field is the 1st second, and the identifier representing the source of the ephemeris data is 1;

[0155] When the base station sends the system information at 1.92 seconds, it carries the predicted ephemeris data in the system information, and the time field is 1.92 seconds, and the identifier representing the source of the ephemeris data is 0.

[0156] The subsequent process of sending system information is similar to the above process. For the sake of brevity, it will not be explained in detail here.

[0157] In the above implementation process, the receiving device can quickly obtain the time of the ephemeris data, and then use the time to perform subsequent related data processing.

[0158] Please refer to Figure 5 , Figure 5 The present invention provides a structural block diagram of an ephemeris data prediction device 200. The device 200 may be a module, program segment or code on an electronic device. Figure 2 The method embodiment corresponds to the embodiment that can be executed Figure 2 The various steps involved in the method embodiment and the specific functions of the device 200 can be found in the description above. To avoid repetition, detailed description is appropriately omitted here.

[0159] Optionally, the apparatus 200 includes:

[0160] A filtering module 210 is configured to filter the historical ephemeris data that meets a set condition to obtain filtered ephemeris data, wherein the set condition includes that the amount of historical ephemeris data reaches a set number, the filtering order is N, where N is an integer greater than or equal to 1, and the set number is N;

[0161] The prediction module 220 is configured to predict the ephemeris data at a future time based on the filtered ephemeris data.

[0162] Optionally, the filtering module 210 is configured to obtain historical ephemeris data from an ephemeris data storage area, wherein the ephemeris data storage area stores the most recently acquired N ephemeris data; and filter the historical ephemeris data to obtain filtered ephemeris data.

[0163] Optionally, the filtering module 210 is configured to filter the historical ephemeris data that meets set conditions using a Kalman filtering algorithm to obtain filtered ephemeris data.

[0164] Optionally, the ephemeris data prediction device 200 runs on a receiving device, and the prediction module 220 is used to predict the ephemeris data of the receiving device at each future time-frequency offset synchronization moment based on the filtered ephemeris data, wherein each time-frequency offset synchronization moment is before the next ephemeris data reception moment.

[0165] Optionally, the ephemeris data prediction device 200 runs on a network device, and the device 200 further includes:

[0166] The data sending module is used to obtain the ephemeris data of the corresponding source according to the data selection strategy, wherein the sources of the ephemeris data include those obtained at the most recent ephemeris update time and those obtained by prediction; and the ephemeris data is sent carrying it in the system information.

[0167] Optionally, the data selection strategy includes a time difference between the most recent ephemeris update time and the time when the system information is issued;

[0168] Alternatively, the data selection strategy includes the amount of ephemeris data predicted based on the filtered ephemeris data;

[0169] Alternatively, the data selection strategy includes the accuracy of a prediction algorithm used by the receiving device to predict relevant information using ephemeris data;

[0170] Alternatively, the data selection strategy includes indication information sent by a receiving device, wherein the indication information is used to indicate a source of the ephemeris data;

[0171] Alternatively, the data selection strategy includes a source of ephemeris data predicted by a machine learning algorithm.

[0172] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0173] Please refer to Figure 6 , Figure 6 A structural diagram of an electronic device for executing an ephemeris data prediction method provided in an embodiment of the present application, the electronic device may include: at least one processor 310, such as a CPU, at least one communication interface 320, at least one memory 330 and at least one communication bus 340. Among them, the communication bus 340 is used to realize the connection and communication between these components. Among them, the communication interface 320 of the device in the embodiment of the present application is used to communicate signaling or data with other node devices. The memory 330 can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 330 can optionally also be at least one storage device located away from the aforementioned processor. Computer-readable instructions are stored in the memory 330. When the computer-readable instructions are executed by the processor 310, the electronic device executes the above-mentioned Figure 2 The method process shown.

[0174] I understand. Figure 6 The structure shown is only for illustration, and the electronic device may also include Figure 6 More or fewer components than shown, or with Figure 6 Different configurations shown. Figure 6 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0175] The embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program performs the following operations: Figure 2 The method process in the illustrated method embodiment is performed by the electronic device.

[0176] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the methods provided in the above method embodiments, for example, including:

[0177] Filtering historical ephemeris data that meets set conditions to obtain filtered ephemeris data, wherein the set conditions include that the amount of historical ephemeris data reaches a set number, the filtering order of the filtering is N, N is an integer greater than or equal to 1, and the set number is N;

[0178] Based on the filtered ephemeris data, ephemeris data at a future time is predicted.

[0179] In summary, the embodiments of the present application provide an ephemeris data prediction method, apparatus, electronic device, and storage medium. These methods filter historical ephemeris data that meets set conditions to obtain filtered ephemeris data, and then predict ephemeris data for future moments based on the filtered ephemeris data. For the base station and receiving device, filtering historical ephemeris data effectively removes noise and errors from the ephemeris data, correcting measurement errors in the ephemeris digitization process and improving the accuracy of the ephemeris data. This results in higher accuracy for the ephemeris data sent by the base station, and higher accuracy for the ephemeris data predicted by the receiving device. This allows the receiving device to obtain more accurate ephemeris data, thereby improving the accuracy of subsequent data processing.

[0180] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0181] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0182] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0183] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0184] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for predicting ephemeris data, characterized in that: The method comprises: Filtering historical ephemeris data that meets set conditions to obtain filtered ephemeris data, wherein the set conditions include that the amount of historical ephemeris data reaches a set number, the filtering order of the filtering is N, N is an integer greater than or equal to 1, and the set number is N; Predicting ephemeris data at a future time based on the filtered ephemeris data; Wherein, if the execution subject of the ephemeris data prediction method is a network device, after predicting the ephemeris data at a future time based on the filtered ephemeris data, the method further includes: Selecting ephemeris data from a corresponding source according to a data selection strategy, wherein the sources of the ephemeris data include those obtained at the most recent ephemeris update time and those obtained by prediction; Sending the selected ephemeris data of the corresponding source in the system information; The data selection strategy includes selecting an ephemeris source based on a time difference between the most recent ephemeris update time and the time when the system information is issued; Alternatively, the data selection strategy includes selecting an ephemeris source based on the amount of ephemeris data predicted from the filtered ephemeris data; Alternatively, the data selection strategy includes selecting an ephemeris source based on the accuracy of a prediction algorithm used by the receiving device to predict relevant information using the ephemeris data; Alternatively, the data selection strategy includes selecting an ephemeris source according to indication information sent by the receiving device, wherein the indication information is used to indicate a source of the ephemeris data; Alternatively, the data selection strategy includes selecting an ephemeris source based on a source of ephemeris data predicted based on a machine learning algorithm.

2. The method according to claim 1, characterized in that The filtering of the historical ephemeris data that meets the set conditions to obtain filtered ephemeris data includes: Acquire historical ephemeris data from an ephemeris data storage area, wherein the ephemeris data storage area stores the most recently obtained N ephemeris data; The historical ephemeris data is filtered to obtain filtered ephemeris data.

3. The method according to claim 1, characterized in that The filtering of the historical ephemeris data that meets the set conditions to obtain filtered ephemeris data includes: The Kalman filter algorithm is used to filter the historical ephemeris data that meets the set conditions to obtain the filtered ephemeris data.

4. The method according to claim 1, wherein If the execution subject of the ephemeris data prediction method is a receiving device, the ephemeris data at a future time is predicted based on the filtered ephemeris data, including: Based on the filtered ephemeris data, ephemeris data of the receiving device at each future time-frequency offset synchronization moment is predicted, wherein each time-frequency offset synchronization moment is before a next ephemeris data reception moment.

5. An ephemeris data prediction device, characterized in that: The device comprises: a filtering module, configured to filter historical ephemeris data that meets a set condition to obtain filtered ephemeris data, wherein the set condition includes that the amount of historical ephemeris data reaches a set amount; A prediction module, configured to predict ephemeris data at a future time based on the filtered ephemeris data; Wherein, if the ephemeris data prediction device is run on a network device, the device further includes: A data sending module is used to select ephemeris data from a corresponding source according to a data selection strategy, wherein the source of the ephemeris data includes the ephemeris data obtained at the most recent ephemeris update time and the ephemeris data obtained by prediction; and send the selected ephemeris data from the corresponding source in the system information; The data selection strategy includes selecting an ephemeris source based on a time difference between the most recent ephemeris update time and the time when the system information is issued; Alternatively, the data selection strategy includes selecting an ephemeris source based on the amount of ephemeris data predicted from the filtered ephemeris data; Alternatively, the data selection strategy includes selecting an ephemeris source based on the accuracy of a prediction algorithm used by the receiving device to predict relevant information using the ephemeris data; Alternatively, the data selection strategy includes selecting an ephemeris source according to indication information sent by the receiving device, wherein the indication information is used to indicate a source of the ephemeris data; Alternatively, the data selection strategy includes selecting an ephemeris source based on a source of ephemeris data predicted based on a machine learning algorithm.

6. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 4 is executed.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is executed.

8. A computer program product, characterized in that The method comprises computer program instructions, and when the computer program instructions are read and executed by a processor, the method according to any one of claims 1 to 4 is executed.

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