Short message fallback method and device based on artificial intelligence, medium and program product

Abstract

The invention provides a short message fallback method and device based on artificial intelligence, a medium and a program product, and relates to the technical field of satellite communication. The method comprises the following steps: acquiring at least one of satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, shielding object type quantitative data and satellite top window passing period remaining time data as multi-dimensional satellite communication feature data; performing short message fallback prediction processing on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model to obtain at least one of a fallback triggering probability, a fallback opportunity and a fallback mode as a message triggering scheme; and triggering short message fallback based on the message triggering scheme. By adopting the method and the device, the problem of falling opportunity pre-judgment lag existing in a traditional short message falling trigger mechanism can be solved.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, and in particular to a short message fallback method, device, medium and program product based on artificial intelligence. Background Technology

[0002] In the field of satellite communication technology, a "voice → short message" fallback method is commonly used, switching to short message mode to maintain basic communication when the voice communication link is abnormal. However, the triggering mechanism for "voice → short message" fallback in related technologies mainly relies on two methods: a fixed signal threshold or a single ephemeris trajectory prediction. When using a fixed signal threshold, the fallback is only initiated after the signal-to-noise ratio of the voice link drops below the threshold. When using a single ephemeris trajectory prediction, it relies on a simple prediction based on the satellite's orbital trajectory. Both of these triggering methods suffer from a lag in the prediction of the fallback timing, resulting in an excessively long switching gap between voice and short message communication, thus making it difficult to effectively guarantee the continuity of satellite communication. Summary of the Invention

[0003] The main objective of this application is to propose an artificial intelligence-based short message fallback method, electronic device, storage medium, and computer program product, which aims to solve the problem of delayed fallback timing prediction in traditional short message fallback triggering mechanisms, avoid excessively long switching gaps between voice communication and short message communication, and thus fully ensure the continuity of satellite communication.

[0004] To achieve the above objectives, a first aspect of this application proposes a short message fallback method based on artificial intelligence, the method comprising: The data to be acquired includes at least one of the following: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, quantification data of obstruction types, and data on the remaining time of the satellite overpass window; these are multi-dimensional satellite communication characteristic data. Based on a preset artificial intelligence model, short message fallback prediction processing is performed on the multi-dimensional satellite communication feature data to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme. The short message fallback is triggered based on the aforementioned message triggering scheme.

[0005] In some embodiments, the artificial intelligence model includes a lightweight hybrid prediction model, which includes a convolutional neural network layer and a deep learning layer; The process of performing short message fallback prediction on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model yields at least one of the following fallback triggering schemes: fallback trigger probability, fallback timing, and fallback mode: Spatial feature data is extracted from the multi-dimensional satellite communication feature data based on the convolutional neural network layer; Based on the deep learning layer, temporal feature data is extracted from the multi-dimensional satellite communication feature data; The spatial feature data and the temporal feature data are subjected to the short message fallback prediction processing to obtain at least one of the following: fallback trigger probability, fallback timing, and fallback mode, which is a message triggering scheme.

[0006] In some embodiments, the multi-dimensional satellite communication feature data includes terminal power consumption data and satellite overpass window remaining time data, and the message triggering scheme includes fallback trigger probability; The triggering of short message fallback based on the message triggering scheme includes: When the fallback trigger probability in the message triggering scheme is greater than or equal to a preset probability threshold, the trigger threshold for short message fallback is adaptively adjusted based on the terminal power consumption data and the remaining time data of the satellite overpass window, to obtain the adjusted trigger threshold. The short message fallback is triggered based on the adjusted trigger threshold.

[0007] In some embodiments, the message triggering scheme further includes a fallback timing, the adjusted triggering threshold includes an adjusted signal-to-noise ratio triggering threshold, and triggering short message fallback based on the adjusted triggering threshold includes: In response to the arrival of the fallback timing, and when the real-time signal-to-noise ratio is less than or equal to the adjusted signal-to-noise ratio trigger threshold, short message fallback is triggered.

[0008] In some embodiments, the multi-dimensional satellite communication feature data includes terminal power consumption data and link real-time quality data, and the message triggering scheme includes a fallback mode; The process of performing short message fallback prediction on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model yields at least one of the following fallback triggering schemes: fallback trigger probability, fallback timing, and fallback mode: Based on a preset artificial intelligence model, short message fallback prediction processing is performed on the bandwidth and / or terminal power consumption data corresponding to the real-time quality data of the link to obtain the fallback mode.

[0009] In some embodiments, triggering short message fallback based on the message triggering scheme includes: The satellite communication data is processed according to the described fallback mode to obtain short message data packets.

[0010] In some embodiments, the satellite communication data includes voice data, and the fallback mode includes one of a voice segment mode, a speech-to-text mode, and an identifier data mode; the voice segment mode is used to instruct the voice data to be split into at least two voice segments, and the short message data packet is generated based on the at least two voice segments; the speech-to-text mode is used to instruct the voice data to be converted into text summary data, and the short message data packet is generated based on the text summary data; the identifier data mode is used to instruct the generation of a short message data packet based on terminal status data, wherein the terminal status data is the status data of the satellite communication transmitter.

[0011] In some embodiments, triggering short message fallback based on the message triggering scheme includes: The satellite communication data is processed based on the aforementioned message triggering scheme to obtain the processed target data; The target data is subjected to quantum encryption to obtain quantum encrypted data of the target data; A short message data packet is generated based on the target data and the quantum encrypted data.

[0012] In some embodiments, the method is applied to a satellite communication transmitter, and the triggering of short message fallback based on the message triggering scheme includes: The satellite communication data is processed based on the aforementioned message triggering scheme to obtain short message data packets; After triggering short message fallback based on the message triggering scheme, the method further includes: The data volume estimation information of the short message data packet is sent to the edge gateway, so that the edge gateway reserves time slot resources for the short message data packet based on the data volume estimation information; the time slot resources are used to transmit the short message data packet to the satellite communication receiver.

[0013] To achieve the above objectives, a second aspect of this application proposes another short message fallback method based on artificial intelligence, the method being applied to a satellite communication receiver, the method comprising: Receive short message data packets sent by a satellite communication transmitter; the short message data packets include target data and quantum-encrypted data of the target data, the target data being obtained by the satellite communication transmitter processing satellite communication data; The target data is subjected to security verification based on the quantum encrypted data, and if the target data passes the security verification, the target data is reassembled to obtain the recovered satellite communication data.

[0014] To achieve the above objectives, a third aspect of this application proposes yet another short message fallback method based on artificial intelligence, the method being applied to an edge gateway, the method comprising: The system receives data volume estimation information for short message data packets sent by a satellite communication transmitter; the short message data packets are obtained by the satellite communication transmitter processing satellite communication data. Based on the data volume estimation information, time slot resources for the short message data packets are reserved; In response to receiving the short message data packet sent by the satellite communication transmitter, the short message data packet is preferentially transmitted to the satellite communication receiver using the reserved time slot resources.

[0015] To achieve the above objectives, a fourth aspect of this application proposes a short message fallback device based on artificial intelligence, the device comprising: The acquisition module is used to acquire at least one of the following as multi-dimensional satellite communication characteristic data: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and satellite overpass window remaining time data; The hybrid prediction module is used to perform short message fallback prediction processing on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model, and to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme. The fallback triggering module is used to trigger short message fallback based on the message triggering scheme.

[0016] To achieve the above objectives, a fifth aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the artificial intelligence-based short message fallback method described in the first, second, and / or third aspects.

[0017] To achieve the above objectives, a sixth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the artificial intelligence-based short message fallback method described in the first, second, and / or third aspects.

[0018] To achieve the above objectives, a seventh aspect of the present application provides a computer program product, which includes a computer program that, when executed by a processor, implements the artificial intelligence-based short message fallback method provided in the first, second, and / or third aspects above.

[0019] The artificial intelligence-based short message fallback method, apparatus, electronic device, computer-readable storage medium, and computer program product proposed in this application acquire at least one of the following as multi-dimensional satellite communication feature data: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and satellite overpass window remaining time data. Based on a preset artificial intelligence model, the multi-dimensional satellite communication feature data is processed to predict short message fallback, resulting in at least one of the following as a message triggering scheme: fallback trigger probability, fallback timing, and fallback mode. Short message fallback is triggered based on the message triggering scheme.

[0020] This application embodiment acquires at least one of the following multi-dimensional feature data from satellite communication: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and remaining time data of satellite overpass window. Based on an artificial intelligence model, it performs short message fallback prediction processing on the acquired multi-dimensional communication feature data to obtain at least one of the following fallback triggering schemes: fallback trigger probability, fallback timing, and fallback mode. Short message fallback is then triggered based on this scheme. Thus, compared to traditional methods that rely on fixed signal thresholds or single ephemeris trajectory prediction to trigger short message fallback, this application embodiment, based on an artificial intelligence model and integrating multi-dimensional satellite communication feature data for short message fallback prediction processing, can achieve a greater lead time for fallback timing prediction. This makes the short message fallback timing more accurate, significantly reducing the handover lag time. In other words, this application embodiment can effectively solve the problem of lag in fallback timing prediction in traditional short message fallback triggering mechanisms, avoiding excessively long handover intervals between voice communication and short message communication, thereby fully ensuring the continuity of satellite communication.

[0021] Furthermore, the embodiments of this application use multi-dimensional satellite communication feature data to predict short message fallback, which breaks through the limitation of single-dimensional prediction to trigger short message fallback. It can provide data support for whether to trigger short message fallback through more comprehensive scene perception, so as to make accurate decisions. Attached Figure Description

[0022] Figure 1 A flowchart illustrating the steps of the AI-based short message fallback method provided in some embodiments of this application; Figure 2 for Figure 1 A detailed flowchart of step S102; Figure 3 A schematic diagram of the structure of a lightweight hybrid prediction model involved in some embodiments of the short message fallback method based on artificial intelligence provided in this application. Figure 4 for Figure 1A schematic diagram of another detailed step in step S102; Figure 5 for Figure 1 A detailed flowchart of step S103; Figure 6 for Figure 1 A schematic diagram of another detailed step in step S103; Figure 7 The hardware module architecture diagram of the satellite communication transmitter involved in some embodiments of the short message fallback method based on artificial intelligence provided in the embodiments of this application; Figure 8 A flowchart illustrating the steps of the AI-based short message fallback method provided in this application embodiment in other embodiments; Figure 9 A flowchart illustrating the steps of the AI-based short message fallback method provided in this application in some other embodiments; Figure 10 A flowchart illustrating the steps of the AI-based short message fallback method provided in this application in some other embodiments; Figure 11 A flowchart illustrating the overall process of the AI-based short message fallback method provided in this application embodiment in a complete embodiment; Figure 12 A timing diagram illustrating the application of the AI-based short message fallback method provided in this application in a complete embodiment of a mountain emergency rescue scenario; Figure 13 A schematic diagram of the structure of the short message fallback device based on artificial intelligence provided in the embodiments of this application; Figure 14 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0024] It should be noted that although functional modules are divided in the device / system schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device / system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0026] First, the overall concept of the short message fallback method based on artificial intelligence provided in the embodiments of this application will be explained.

[0027] Currently, satellite communication, as a core communication method in scenarios without terrestrial network coverage, is widely used in emergency rescue, field operations, and communication in remote areas. Its communication continuity directly affects the security and effectiveness of information transmission in various scenarios. However, in scenarios with incomplete satellite coverage, such as mountainous areas, canyons, and urban high-rise buildings, satellite communication links are easily affected by obstructions, leading to problems such as low signal-to-noise ratio and frequent communication interruptions, severely restricting the reliability of satellite communication. To ensure communication continuity in such scenarios, satellite communication generally adopts a "voice → short message" fallback method. That is, when the voice communication link is abnormal, it switches to short message mode to maintain basic communication. This fallback method has become the mainstream solution for existing satellite phones to address coverage deficiencies.

[0028] In related technologies, the "voice → short message" fallback triggering mechanism in satellite communication has significant defects. This is because the traditional "voice → short message" fallback triggering mechanism mainly relies on a fixed signal threshold or a single ephemeris trajectory prediction. When using a fixed signal threshold triggering, the fallback is only initiated after the signal-to-noise ratio of the voice link drops below the threshold. When using a single ephemeris trajectory prediction triggering, it relies on a simple prediction based on the satellite's orbital trajectory. Both of these triggering methods suffer from a lag in the prediction of the fallback timing, resulting in an excessively long switching gap between voice communication and short message communication, making it difficult to effectively ensure the continuity of satellite communication.

[0029] In addition, fixed signal threshold design is prone to insufficient resource allocation and data transmission failure when the window period is short.

[0030] Furthermore, in traditional "voice → short message" fallback technology, short message data packets are easily tampered with during weak link transmission, which seriously affects the security of data transmission. In addition, voice segments cannot be accurately spliced ​​back together after being split, affecting communication quality.

[0031] To address the aforementioned issues, this application proposes an artificial intelligence-based short message fallback method, apparatus, electronic device, computer-readable storage medium, and computer program product. This involves acquiring at least one of the following as multi-dimensional satellite communication characteristic data: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and remaining time data of the satellite overpass window. Based on a preset artificial intelligence model, short message fallback prediction processing is performed on the multi-dimensional satellite communication characteristic data to obtain at least one of the following: fallback trigger probability, fallback timing, and fallback mode, which constitutes a message triggering scheme. Short message fallback is then triggered based on the message triggering scheme.

[0032] This application embodiment acquires at least one of the following multi-dimensional feature data from satellite communication: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and remaining time data of satellite overpass window. Based on an artificial intelligence model, it performs short message fallback prediction processing on the acquired multi-dimensional communication feature data to obtain at least one of the following fallback triggering schemes: fallback trigger probability, fallback timing, and fallback mode. Short message fallback is then triggered based on this scheme. Thus, compared to traditional methods that rely on fixed signal thresholds or single ephemeris trajectory prediction to trigger short message fallback, this application embodiment, based on an artificial intelligence model and integrating multi-dimensional satellite communication feature data for short message fallback prediction processing, can achieve a greater lead time for fallback timing prediction. This makes the short message fallback timing more accurate, significantly reducing the handover lag time. In other words, this application embodiment can effectively solve the problem of lag in fallback timing prediction in traditional short message fallback triggering mechanisms, avoiding excessively long handover intervals between voice communication and short message communication, thereby fully ensuring the continuity of satellite communication.

[0033] Furthermore, the embodiments of this application use multi-dimensional satellite communication feature data to predict short message fallback, which breaks through the limitation of single-dimensional prediction to trigger short message fallback. It can provide data support for whether to trigger short message fallback through more comprehensive scene perception, so as to make accurate decisions.

[0034] Furthermore, compared to traditional short message fallback methods, the embodiments of this application can also achieve dynamic threshold adjustment to adapt to complex scenario requirements. That is, the embodiments of this application can also dynamically adjust the threshold based on the remaining window time and terminal power consumption rules, avoiding the resource waste of traditional short message fallback triggering mechanisms that rely on fixed thresholds in scenarios with short window periods and low power consumption, and effectively improving the transmission success rate of short message data packets.

[0035] Furthermore, in this embodiment, the short message data is secure and controllable, and possesses both integrity and tamper-proof properties. Specifically, this embodiment can use a random number seed from Quantum Key Distribution (QKD) to generate a quantum signature, fully guaranteeing the accuracy of voice segment tampering identification and effectively reducing segment loss rate, thus solving the deficiency of traditional short message fallback data lacking security protection.

[0036] Furthermore, the embodiments of this application can also balance the communication quality and power consumption of satellite communication through flexible matching of multiple fallback modes. For example, by dynamically switching between three fallback modes (voice segment + quantum signature mode (sufficient bandwidth), speech-to-text digest mode (limited bandwidth), and location only + emergency identification mode (extremely low power consumption)) according to the link and power consumption status, it is possible to reduce terminal power consumption while ensuring core communication needs, thereby adapting to complex weak link scenarios such as outdoor exploration and emergency rescue.

[0037] Furthermore, in this embodiment, the satellite communication terminal (satellite communication transmitter) may include a data acquisition module, a preprocessing module, an artificial intelligence (AI) prediction and decision-making module, a dynamic threshold adjustment module, a quantum signature encapsulation module, a collaborative scheduling module, and a data recovery module. These modules can work collaboratively to execute an AI-based short message fallback method, adapting to weak link scenarios where the obstruction angle is >60° and the satellite overpass window is <5 minutes.

[0038] Next, the artificial intelligence-based short message fallback method, apparatus, electronic device, computer-readable storage medium, and computer program product provided in this application will be specifically described through the following embodiments, and the various detailed embodiments of the artificial intelligence-based short message fallback method provided in this application will be described in detail first.

[0039] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0040] It should be noted that the AI-based short message fallback method provided in this application relates to the field of satellite communication technology. The AI-based short message fallback method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be an in-vehicle terminal, a smartphone, tablet, laptop, desktop computer, or other electronic device associated with a vehicle and capable of communicating and interacting with the vehicle via a network. The server can be the terminal's backend server terminal device, which can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and AI platforms. The software can be an application implementing the AI-based short message fallback method, a computer program, and a storage medium carrying the computer program. It should be understood that, based on different design needs of practical applications, the terminals, servers, and software that apply the AI-based short message fallback method provided in this application may also be other forms not listed here, and the AI-based short message fallback method provided in this application does not specifically limit these.

[0041] Furthermore, this application can also be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: vehicle terminals, personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, personal computers (PCs), minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via communication networks. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0042] For ease of understanding and explanation, the following text will use relevant terminal devices in satellite communication scenarios, such as satellite communication transmitters, edge gateways, and satellite communication receivers, as examples to illustrate the application of the AI-based short message fallback method provided in this application. In some descriptions, the satellite communication transmitter may be simply referred to as the transmitter or satellite communication terminal, the edge gateway may be simply referred to as the gateway, and the satellite communication receiver may be simply referred to as the receiver. The implementation of the AI-based short message fallback method provided in this application for any of the above-mentioned subject matter can refer to the implementation process of the AI-based short message fallback method described below.

[0043] Please refer to Figure 1 , Figure 1 The flowchart illustrates the steps of the AI-based short message fallback method provided in some embodiments of this application. It should be understood that, although... Figure 1 The flowcharts illustrating subsequent steps show the execution order of some method steps. However, based on different design needs in practical applications, the AI-based short message fallback method provided in this application embodiment can, of course, employ an execution order different from that shown in the figures. That is, Figure 1 The order of the method steps shown does not constitute a limitation on the execution logic order of the AI-based short message fallback method provided in the embodiments of this application. Any other method based on... Figure 1 Reasonable changes to the sequence of steps shown should be included within the protection scope of the short message fallback method based on artificial intelligence provided in the embodiments of this application.

[0044] like Figure 1 As shown, in some embodiments, the short message fallback method based on artificial intelligence provided in this application may include steps S101 to S103 as shown below.

[0045] Step S101: Obtain at least one of the following as multi-dimensional satellite communication characteristic data: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and satellite overpass window remaining time data.

[0046] When communicating with the satellite communication receiver via an edge gateway, the satellite communication transmitter can collect satellite ephemeris trajectory data, real-time link quality data (such as signal-to-noise ratio SNR, bit error rate, etc.), terminal power consumption data, quantification data of obstruction types, and / or data on the remaining time of the satellite overpass window in real time, thereby obtaining multi-dimensional satellite communication characteristic data.

[0047] In some embodiments, the satellite communication transmitter can collect satellite ephemeris trajectory data, real-time link quality data (SNR, bit error rate), and terminal power consumption data through a built-in GPS module, link monitoring module, and power consumption sensor, respectively; and through a pre-stored terrain database, combined with its own terminal positioning information, it matches the type of obstruction and quantifies obstruction scenarios such as mountains, canyons, and urban high-rise buildings into obstruction coefficients in the range of 0-1; at the same time, it extracts the remaining time data of the satellite overpass window.

[0048] In some embodiments, the satellite communication transmitter can also perform filtering and noise reduction preprocessing on the multi-dimensional satellite communication feature data collected in real time, remove outliers, and form a standardized feature dataset.

[0049] Step S102: Based on a preset artificial intelligence model, perform short message fallback prediction processing on the multi-dimensional satellite communication feature data to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme.

[0050] It should be noted that the preset artificial intelligence model can be a lightweight neural network model that has been pre-trained and deployed at the satellite communication transmitter. This model can perform inference based on multi-dimensional satellite communication feature data to achieve short message fallback prediction, obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as the message triggering scheme, and output the message triggering scheme as the model prediction result.

[0051] It should be noted that the fallback trigger probability is a quantified probability value used to characterize the likelihood of triggering short message fallback. For example, if the fallback trigger probability is greater than or equal to 85%, it indicates a high probability that the satellite communication transmitter will trigger a fallback from the regular communication mode to the short message mode. Conversely, if the fallback trigger probability is less than 20%, it indicates a low probability that the satellite communication transmitter will trigger a short message fallback. Furthermore, the fallback timing refers to the point in time at which the satellite communication transmitter will predict and switch to short message mode using an artificial intelligence model. From the current moment until the time indicated by the fallback timing, the satellite communication transmitter may be unable to continue voice calls due to a continuous decline in the overall quality of the communication link, thus triggering short message fallback at that time to switch from voice calls to short message mode to maintain basic communication. Moreover, the fallback mode indicates the method of generating short message data packets when short message fallback is triggered (e.g., splitting voice into multiple segments and generating short message data packets based on these segments).

[0052] After acquiring multi-dimensional satellite communication feature data, the satellite communication transmitter can input the multi-dimensional satellite communication feature data into a preset artificial intelligence model. The artificial intelligence model can then use this data to infer short message fallback prediction and obtain at least one of the following as a message triggering scheme: fallback trigger probability, fallback timing, and fallback mode.

[0053] In some embodiments, the satellite communication transmitter can preprocess the acquired multi-dimensional satellite communication feature data, such as performing filtering and noise reduction preprocessing on the multi-dimensional satellite communication feature data, and quantifying the type of obstruction into an obstruction coefficient by matching GPS positioning with a terrain database. Then, the satellite communication transmitter inputs the preprocessed multi-dimensional satellite communication feature data into a preset artificial intelligence model for short message fallback prediction.

[0054] Step S103: Trigger short message fallback based on the message triggering scheme.

[0055] It should be noted that triggering short message fallback can include the process of triggering short message fallback to process satellite communication data to generate short message data packets, and then sending the short message data packets to downstream devices (such as edge gateways, satellite communication receivers, etc.).

[0056] After receiving the message triggering scheme output by the artificial intelligence model, the satellite communication transmitter can trigger short message fallback based on the scheme. For example, if the message triggering scheme includes fallback timing and fallback mode, the satellite communication data is processed according to the fallback mode to generate short message data packets. In response to the arrival of the fallback timing, voice → short message fallback is triggered, and the short message data packet is sent to the edge gateway, which then transmits the short message data to the satellite communication receiver.

[0057] In this embodiment, satellite communication transmitters collect satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and / or satellite overpass window remaining time data in real time during satellite communication to obtain multi-dimensional satellite communication feature data. This multi-dimensional satellite communication feature data is then input into a preset artificial intelligence model. The artificial intelligence model uses this multi-dimensional satellite communication feature data to infer short message fallback prediction, obtaining at least one of the following as a message triggering scheme: fallback trigger probability, fallback timing, and fallback mode. Finally, short message fallback is triggered based on this message triggering scheme.

[0058] Therefore, compared to the traditional method of triggering short message fallback by relying on fixed signal thresholds or single ephemeris trajectory prediction, the embodiments of this application are based on artificial intelligence models and integrate multi-dimensional satellite communication feature data for short message fallback prediction processing. This enables greater lead time prediction of fallback timing, making the timing of short message fallback more accurate and significantly reducing the handover lag time. In other words, the embodiments of this application can effectively solve the problem of lag in fallback timing prediction in traditional short message fallback triggering mechanisms, avoid excessively long handover intervals between voice communication and short message communication, and thus fully ensure the continuity of satellite communication.

[0059] Furthermore, the embodiments of this application use multi-dimensional satellite communication feature data to predict short message fallback, which breaks through the limitation of single-dimensional prediction to trigger short message fallback. It can provide data support for whether to trigger short message fallback through more comprehensive scene perception, so as to make accurate decisions.

[0060] In some embodiments, the artificial intelligence model includes a lightweight hybrid prediction model, which includes convolutional neural network layers and deep learning layers.

[0061] Please refer to Figure 2 , Figure 2 for Figure 1 A detailed flowchart of step S102.

[0062] like Figure 2 As shown, in some embodiments, step S102 above: performing short message fallback prediction processing on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme, may include steps S201 to S203 as shown below.

[0063] Step S201: Extract spatial feature data from the multi-dimensional satellite communication feature data based on the convolutional neural network layer; Step S202: Extract time-series feature data from the multi-dimensional satellite communication feature data based on the deep learning layer; Step S203: Perform short message fallback prediction processing on the spatial feature data and the temporal feature data to obtain at least one of the following as a message triggering scheme: fallback trigger probability, fallback timing, and fallback mode.

[0064] The satellite communication transmitter can pre-build and train a lightweight hybrid prediction model based on Convolutional Neural Networks (CNNs) and Deep Learning Networks (LSTMs), and deploy the trained model locally. After acquiring multi-dimensional satellite communication feature data, the transmitter can input this data into the lightweight hybrid prediction model. This model uses CNN layers to extract spatial feature data (such as obstruction type quantification data and real-time link quality data) and deep learning layers to extract temporal feature data (such as satellite ephemeris trajectory data and remaining time of satellite overpass windows). The extracted spatial and temporal feature data are then fused to perform short message fallback decision analysis, yielding the analysis results: fallback trigger probability, fallback timing, and / or fallback mode. These results are then output as the final model prediction result by the lightweight hybrid prediction model.

[0065] For example, a lightweight hybrid prediction model can be a lightweight CNN-LSTM hybrid prediction model that has been pre-built and trained based on a convolutional neural network (CNN) and a deep learning network (LSTM). Figure 3 As shown, the structure and working principle of the lightweight CNN-LSTM hybrid prediction model can be described as follows: In this lightweight CNN-LSTM hybrid prediction model, the lower layer is a CNN spatial feature extraction layer, which is used to process spatial feature data such as occlusion type quantization data and link real-time quality data; the upper layer is an LSTM temporal feature capture layer, which is used to analyze temporal feature data such as satellite ephemeris trajectory data and satellite overpass window remaining time data; the top layer is the output layer, which explicitly labels the three major decision results output by the model: fallback trigger probability, fallback timing, and fallback mode.

[0066] In this embodiment, the lightweight CNN-LSTM model, which integrates five types of features, can predict the fallback timing with an advance of 5-10 seconds. Compared to the traditional 1-2 second advance based solely on a single ephemeris prediction, this reduces the handover lag time for short message fallback by more than 70%, effectively addressing the core pain point of fallback timing lag in weak link scenarios. Furthermore, this embodiment enhances the advance of fallback timing prediction through the lightweight CNN-LSTM model, thereby reducing the handover lag time for short message fallback. This enables proactive and accurate triggering of short message fallback, significantly shortening the handover gap between voice and short message communication and improving the continuity of satellite communication.

[0067] Furthermore, in this embodiment, a lightweight artificial intelligence model is adapted to the computing power of the transmitting end, and a dynamic power consumption adjustment strategy is combined to ensure low-power operation and improve the practicality of the terminal, thereby effectively avoiding terminal performance problems caused by algorithm complexity.

[0068] In some embodiments, the multi-dimensional satellite communication feature data includes terminal power consumption data and link real-time quality data, and the message triggering scheme includes a fallback mode.

[0069] Please refer to Figure 4 , Figure 4 for Figure 1 A schematic diagram of another detailed step in step S102.

[0070] like Figure 4 As shown, in some embodiments, step S102 above: performing short message fallback prediction processing on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme, may include step S401 as shown below.

[0071] Step S401: Based on a preset artificial intelligence model, perform short message fallback prediction processing on the bandwidth and / or terminal power consumption data corresponding to the real-time quality data of the link to obtain the fallback mode.

[0072] When a satellite communication transmitter performs short message fallback prediction processing on multi-dimensional satellite communication feature data using a pre-set artificial intelligence model, after extracting real-time link quality data (such as SNR and bit error rate) from the multi-dimensional satellite communication feature data, the artificial intelligence model can infer the bandwidth of the current satellite communication scenario based on this real-time link quality data. Therefore, based on the bandwidth corresponding to the real-time link quality data, it can perform decision analysis on the fallback modes that can be used for short message fallback, obtaining the fallback mode predicted based on bandwidth. For example, when bandwidth is sufficient, the fallback mode is determined to be voice segment + quantum signature mode, while when bandwidth is limited, the fallback mode is determined to be speech-to-text digest mode. Alternatively, when performing short message fallback prediction processing on multi-dimensional satellite communication feature data using a pre-set artificial intelligence model, the satellite communication transmitter can also perform decision analysis on the fallback modes that can be used for short message fallback based on its own terminal power consumption data, obtaining the fallback mode predicted based on bandwidth. For example, when power consumption is extremely low, the fallback mode is determined to be location only + emergency identification mode. Alternatively, when the satellite communication transmitter performs short message fallback prediction processing on multi-dimensional satellite communication feature data using a pre-set artificial intelligence model, it can also simultaneously perform decision analysis on the fallback modes that can be adopted for short message fallback based on the bandwidth corresponding to the real-time link quality data and its own terminal power consumption data, thus obtaining the fallback mode obtained by bandwidth-based short message fallback prediction. For example, when bandwidth is sufficient and power consumption is high, the fallback mode is determined to be speech-to-text summarization mode, while when bandwidth is tight and power consumption is extremely low, the fallback mode is determined to be location only + emergency identification mode.

[0073] In some embodiments, when the message triggering scheme includes the above-described fallback mode, step S103: triggering short message fallback based on the message triggering scheme may include the following steps: The satellite communication data is processed according to the described fallback mode to obtain short message data packets.

[0074] It should be noted that satellite communication data may include at least one of the following: voice data, video data, text data, navigation and positioning data, sensor perception data, control command data, and emergency alarm data.

[0075] When a satellite communication transmitter triggers short message fallback based on a message triggering scheme, if the message triggering scheme includes a fallback mode, the satellite communication transmitter can first process the satellite communication data according to the fallback mode to obtain the short message data packet corresponding to the satellite communication data.

[0076] In some embodiments, the satellite communication data includes voice data, and the fallback mode includes one of a voice segment mode, a speech-to-text mode, and an identifier data mode; the voice segment mode is used to instruct the voice data to be split into at least two voice segments, and the short message data packet is generated based on the at least two voice segments; the speech-to-text mode is used to instruct the voice data to be converted into text summary data, and the short message data packet is generated based on the text summary data; the identifier data mode is used to instruct the generation of a short message data packet based on terminal status data, wherein the terminal status data is the status data of the satellite communication transmitter.

[0077] It should be noted that the speech segment mode can be the speech segment + quantum signature mode mentioned above, the speech-to-text mode can be the speech-to-text summary mode mentioned above, and the identification data mode can be the location only + emergency identification mode mentioned above.

[0078] When a satellite communication transmitter processes satellite communication data in fallback mode, if the data includes voice data and the fallback mode is voice segment mode, the transmitter can split the voice data into multiple voice segments and further generate short message data packets based on these segments. For example, when processing voice data in voice segment mode, the transmitter can split the voice data into fixed 100ms segments, generate a unique quantum signature for each segment using a random number seed generated by QKD, and then encapsulate the split voice segments, quantum signatures, timestamps, and segment indexes into short messages to generate short message data packets.

[0079] When a satellite communication transmitter processes satellite communication data in fallback mode, if the data includes voice data and the fallback mode is speech-to-text, the transmitter can convert the data into a text digest and generate a short message data packet based on it. For example, the transmitter can directly encapsulate the text digest into a short message to generate a short message data packet, or it can further process the text digest with a quantum signature and then encapsulate the text digest and its quantum signature together into a short message to generate a short message data packet.

[0080] When a satellite communication transmitter processes satellite communication data in a fallback mode, if the fallback mode is an identification data mode, the satellite communication transmitter can directly obtain its own position and urgency level (such as needing rescue after a collision), and encapsulate the identification data corresponding to these status data into short messages to generate short message data packets.

[0081] In this embodiment, the satellite communication transmitter, based on a preset artificial intelligence model, performs short message fallback prediction processing on the bandwidth and / or terminal power consumption data corresponding to the real-time link quality data to obtain the fallback mode. This allows for flexible matching of multiple fallback modes, thus balancing the quality and power consumption of satellite communication. For example, by dynamically switching between three fallback modes (voice segment + quantum signature mode (sufficient bandwidth), speech-to-text digest mode (limited bandwidth), and location only + emergency identification mode (extremely low power consumption)) according to the link and power consumption status, it is possible to reduce terminal power consumption while ensuring core communication needs, thereby adapting to complex weak link scenarios such as outdoor exploration and emergency rescue.

[0082] Furthermore, this embodiment can adaptively select from multiple fallback modes, thus balancing satellite communication quality with the transmission efficiency of short-term data packets, thereby adapting to diverse business scenarios.

[0083] Furthermore, in this embodiment, the voice data is split into standardized 100ms segments + timestamp + segment index marker by the satellite communication transmitter, so that the receiver can automatically splice them together. This can achieve a segment loss rate of <0.5% and realize accurate voice recovery, thereby eliminating splicing stutters, misalignments and other problems, and ensuring the quality of satellite communication.

[0084] In some embodiments, the multi-dimensional satellite communication feature data includes terminal power consumption data and satellite overpass window remaining time data, and the message triggering scheme includes fallback trigger probability.

[0085] When the satellite communication transmitter performs short message fallback prediction processing on multi-dimensional satellite communication feature data using a preset artificial intelligence model, after extracting the remaining time data of the satellite overpass window from the multi-dimensional satellite communication feature data, the artificial intelligence model can use this data to make a decision analysis on the trigger probability of short message fallback, thereby obtaining the fallback trigger probability as a message triggering scheme.

[0086] Please refer to Figure 5 , Figure 5 for Figure 1 A detailed flowchart of step S103.

[0087] like Figure 5 As shown, in some embodiments, step S103 above: triggering short message fallback based on the message triggering scheme may include steps S501 and S502 as shown below.

[0088] Step S501: When the fallback trigger probability in the message triggering scheme is greater than or equal to a preset probability threshold, the trigger threshold for short message fallback is adaptively adjusted based on the terminal power consumption data and the remaining time data of the satellite overpass window, to obtain the adjusted trigger threshold.

[0089] It should be noted that the trigger threshold can be a pre-set critical indicator value by the satellite communication transmitter to determine whether the current satellite communication can no longer maintain regular service transmission. When the real-time detected communication characteristic data is lower than the trigger threshold, it is determined that the short message fallback condition is met, and thus the system switches to short message mode. The trigger threshold can include at least one of the following: signal-to-noise ratio (SNR) threshold, carrier-to-noise ratio (CNR) threshold, and voice-to-code rate (VCR) threshold.

[0090] When a satellite communication transmitter triggers short message fallback based on a message triggering scheme, if the message triggering scheme includes a fallback trigger probability, the satellite communication transmitter can first compare the fallback trigger probability with a preset probability threshold (such as 85%). If the fallback trigger probability is greater than or equal to the preset probability threshold, the satellite communication transmitter can further adaptively adjust the short message fallback trigger threshold based on its own terminal power consumption data and the remaining time of the satellite overpass window in the multi-dimensional satellite communication feature data, and obtain the adjusted trigger threshold.

[0091] In some embodiments, when the satellite communication transmitter dynamically adjusts the trigger threshold for short message fallback based on the remaining time of the satellite overpass window and terminal power consumption data, the rule for dynamically adjusting the signal-to-noise ratio (SNR) trigger threshold can be as follows: if the remaining time of the satellite overpass window is greater than a first duration and the terminal power consumption is greater than a first power consumption threshold, the SNR trigger threshold is adjusted to the first SNR trigger threshold; if the remaining time of the satellite overpass window is less than a second duration and the terminal power consumption is less than a second power consumption threshold, the SNR trigger threshold is adjusted to the second SNR trigger threshold. Here, the first duration is greater than the second duration, the first power consumption threshold is greater than the second power consumption threshold, and the first SNR trigger threshold is less than the second SNR trigger threshold. For example, when the remaining time of the satellite overpass window is ≥3 minutes and the terminal power consumption is ≥50%, the SNR trigger threshold is set to <3dB; when the remaining time of the satellite overpass window is <30 seconds and the terminal power consumption is <30%, the SNR trigger threshold is increased to <5dB.

[0092] Step S502: Trigger short message fallback based on the adjusted trigger threshold.

[0093] After receiving the adjusted trigger threshold, the satellite communication transmitter further triggers short message fallback based on this adjusted threshold to switch from voice call to short message mode to maintain basic communication. For example, after adjusting the SNR trigger threshold to <3dB based on the remaining time of the satellite overpass window and its own terminal power consumption, if the SNR value of the current satellite communication link is detected to drop below 3dB (e.g., to 2.8dB), the short message fallback process is immediately initiated, voice transmission is stopped, and the transmitter switches to short message transmission mode to send encapsulated short message data packets.

[0094] In some embodiments, the message triggering scheme further includes a fallback timing, and the adjusted triggering threshold includes an adjusted signal-to-noise ratio triggering threshold.

[0095] Step S502 above: Triggering short message fallback based on the adjusted signal-to-noise ratio trigger threshold may include the following steps: In response to the arrival of the fallback timing, and when the real-time signal-to-noise ratio is less than or equal to the adjusted signal-to-noise ratio trigger threshold, short message fallback is triggered.

[0096] When a satellite communication transmitter triggers short message fallback based on a message triggering scheme, if the message triggering scheme includes both a fallback trigger probability and a fallback timing, the satellite communication transmitter can dynamically adjust the signal-to-noise ratio (SNR) trigger threshold for short message fallback based on the fallback trigger probability to obtain the adjusted SNR trigger threshold. Then, in response to reaching the fallback timing and detecting that the real-time SNR is lower than the adjusted SNR trigger threshold, the transmitter triggers short message fallback to switch from voice call to short message mode to maintain basic communication.

[0097] For example, the satellite communication transmitter, based on a lightweight CNN-LSTM hybrid prediction model, predicts that the link quality will drop to SNR < 3dB after 1 minute. The model outputs a decision result: a fallback trigger probability of 92% (≥ preset threshold 85%), and a fallback timing of 1 minute. After this, the satellite communication transmitter, based on the remaining time of the satellite overpass window (2 minutes, after a 1-minute wait) and the current terminal power consumption (still 58%), executes a dynamic threshold adjustment rule: because the condition of a remaining window time ≥ 3 minutes is not met, but the terminal power consumption ≥ 50%, combined with the current high obstruction scenario with an obstruction angle > 70°, the SNR trigger threshold is set to < 3dB. That is, when the link SNR value drops below 3dB, the short message fallback process is immediately initiated. Thus, after 1 minute, when the link SNR value drops to 2.8dB, the satellite communication transmitter can trigger the short message fallback process, stop voice transmission, and switch to short message transmission mode to send encapsulated short message data packets.

[0098] In this embodiment, when the satellite communication transmitter performs short message fallback prediction processing on multi-dimensional satellite communication feature data using a preset artificial intelligence model, the artificial intelligence model analyzes the trigger probability of short message fallback based on the remaining time of the satellite overpass window, thereby obtaining the fallback trigger probability. Then, the satellite communication transmitter further adaptively adjusts the signal-to-noise ratio (SNR) trigger threshold for short message fallback based on its own terminal power consumption data and the remaining time of the satellite overpass window from the multi-dimensional satellite communication feature data, to obtain an adjusted SNR trigger threshold. Thus, if the real-time SNR is detected to be lower than the adjusted SNR trigger threshold, short message fallback can be triggered to switch from voice call to short message mode to maintain basic communication. In this way, this embodiment can adapt to complex scenario requirements by dynamically adjusting the SNR trigger threshold, that is, by using dynamic threshold adjustment rules based on the remaining time of the window and terminal power consumption to adapt to different window periods and power consumption scenarios, avoiding resource waste in scenarios with short window periods and low power consumption using a fixed threshold, and effectively improving the short message transmission success rate (e.g., up to 99.2%).

[0099] Please refer to Figure 6 , Figure 6 for Figure 1 A schematic diagram of another detailed step in step S103.

[0100] like Figure 6 As shown, in some embodiments, step S103 above: triggering short message fallback based on the message triggering scheme may include steps S601 to S603 as shown below.

[0101] Step S601: Process the satellite communication data based on the message triggering scheme to obtain the processed target data.

[0102] When the short message fallback is triggered by the message triggering scheme, the satellite communication transmitter can first process the satellite communication data based on the message triggering scheme to obtain the processed target data.

[0103] In some embodiments, when a satellite communication transmitter processes satellite communication data based on a message triggering scheme, if the message triggering scheme includes a fallback mode, the satellite communication data can be processed according to that fallback mode to obtain the processed target data. For example, when the fallback mode is a voice segment mode, the satellite communication transmitter divides the voice data in the satellite communication data into fixed 100ms segments according to this voice segment mode. In this case, multiple 100ms fixed segments constitute the processed target data.

[0104] Step S602: Perform quantum encryption processing on the target data to obtain quantum encrypted data of the target data.

[0105] The satellite communication transmitter can further perform quantum encryption on the processed target data to obtain quantum-encrypted data. For example, after dividing the voice data into fixed 100ms segments according to the voice segment pattern, the satellite communication transmitter can generate a unique quantum signature for each 100ms segment using a random number seed generated by QKD. At this point, the quantum signature of each segment is the quantum-encrypted data of the target data.

[0106] Step S603: Generate a short message data packet based on the target data and the quantum encrypted data.

[0107] After performing quantum encryption on the target data, the satellite communication transmitter can further generate short message data packets based on the target data and its quantum-encrypted data. For example, the satellite communication transmitter can divide voice data into fixed 100ms segments, generate a unique quantum signature for each segment using a random number seed generated by QKD, and then encapsulate the divided voice segments, quantum signatures, timestamps, and segment indexes together into a short message to generate a short message data packet.

[0108] In this embodiment, satellite communication data is processed by the satellite communication transmitter based on a message triggering scheme to obtain processed target data. This target data is then subjected to quantum encryption to obtain quantum-encrypted data. Finally, a short message data packet is generated based on this target data and the quantum-encrypted data. Thus, this embodiment achieves secure and controllable data transmission after short message fallback triggering, while also ensuring data integrity and tamper resistance. For example, this embodiment can use a QKD random number seed to generate a quantum signature, achieving 100% accuracy in identifying tampering of voice segments transmitted after short message fallback triggering, with a segment loss rate of <0.5%, effectively solving the problem of lack of security protection for traditional short message fallback data. In other words, this embodiment, by protecting short message data with quantum signatures, can eliminate the risk of data tampering at its source, thereby ensuring the security of data transmitted through weak links.

[0109] In some embodiments, the AI-based short message fallback method provided in this application is applied to a satellite communication transmitter.

[0110] It should be noted that, as Figure 7As shown, the satellite communication transmitter may include a data acquisition module, a preprocessing module, an AI prediction and decision-making module, a dynamic threshold adjustment module, a quantum signature encapsulation module, a collaborative scheduling module, and a data recovery module. These modules work collaboratively to execute the AI-based short message fallback method described in any of the above embodiments, adapting to weak link scenarios with an obstruction angle > 60° and a satellite overpass window period < 5 minutes. The data acquisition module includes a GPS submodule, a link monitoring submodule, and a power consumption sensing submodule, responsible for the real-time acquisition of five types of characteristic data: satellite ephemeris trajectory, link quality, terminal power consumption, obstruction type quantification, and remaining time of the satellite overpass window period. The preprocessing module performs data filtering, noise reduction, and standardization. The AI ​​prediction and decision-making module outputs the fallback trigger probability, timing, and mode. The dynamic threshold adjustment module adjusts the SNR threshold according to scenario parameters. The quantum signature encapsulation module performs voice segmentation and secure encapsulation. Furthermore, the collaborative scheduling module can be used to implement time slot pre-scheduling with the edge gateway; and the data recovery module can perform voice splicing after link recovery.

[0111] In some embodiments, step S103 above: triggering short message fallback based on the message triggering scheme may include: The satellite communication data is processed based on the aforementioned message triggering scheme to obtain short message data packets.

[0112] When the satellite communication transmitter triggers the short message fallback in the message triggering scheme, it can first trigger the short message fallback process to switch the current communication mode (such as voice transmission mode) to short message transmission mode, and process the satellite communication data based on the message triggering scheme to obtain short message data packets.

[0113] Please refer to Figure 8 , Figure 8 The flowchart of the short message fallback method based on artificial intelligence provided in this application is illustrated in some other embodiments.

[0114] like Figure 8 As shown, in some embodiments, the short message fallback method based on artificial intelligence provided in this application may further include the following step S801.

[0115] Step S801: Send data volume estimation information of short message data packets to the edge gateway, so that the edge gateway reserves time slot resources for the short message data packets based on the data volume estimation information; the time slot resources are used to transmit the short message data packets to the satellite communication receiver.

[0116] When the satellite communication transmitter communicates with the satellite communication receiver through the edge gateway, after performing the above step S103 and / or the refined steps of step S103 to generate the short message data packet, the coordinated adjustment module sends the estimated data volume information of the short message data packet to the edge gateway (e.g., estimated transmission of 180 voice segments, requiring a total of 12 time slots).

[0117] After receiving the data volume estimate, the edge gateway immediately reserves time slots (e.g., 12 time slots) for transmitting the short message data packet. Then, after the satellite communication transmitter officially sends the short message data packet to the edge gateway, the edge gateway receives the packet and directly uses the reserved time slots to prioritize its transmission to the satellite communication receiver.

[0118] In some embodiments, the satellite communication transmitter can process voice data according to the fallback mode in the message triggering scheme to generate short message data packets. At the same time as sending a short message fallback request to the edge gateway, the transmitter can synchronously send the estimated data volume information of the short message data packets to the edge gateway through the coordination adjustment module. The edge gateway then generates a pre-scheduling instruction based on the estimated data volume information to reserve short message time slot resources and feeds back the time slot allocation result to the satellite communication transmitter.

[0119] For example, the satellite communication transmitter, following the fallback mode recommended in the message triggering scheme, splits the voice data in the satellite communication data into 100ms / segment voice segments. Then, using the built-in quantum key generation module, it generates a unique quantum signature for each segment based on the random number seed generated by QKD. The satellite communication transmitter then encapsulates the voice segment, quantum signature, timestamp, and segment index into a short message data packet. Simultaneously, the satellite communication transmitter sends a fallback request and data volume estimation information (estimated transmission of 180 voice segments, requiring a total of 12 time slots) to the ground emergency rescue edge gateway.

[0120] After receiving the data volume estimation information, the edge gateway completes the pre-allocation of time slot resources within 8ms or 10ms based on the data volume estimation information to reserve 12 time slot resources, and feeds back the time slot allocation results to the satellite communication transmitter, thereby ensuring that short message data packets are transmitted first and avoiding competition for resources with other rescue terminals.

[0121] Subsequently, the satellite communication transmitter responds to the fallback timing in the arrival message triggering scheme, and the real-time SNR value of the link (e.g., 2.8dB) also drops to the adjusted SNR triggering threshold (e.g., 3dB), triggering the short message fallback process to stop voice transmission and switch to short message transmission mode. In this mode, the encapsulated short message data packets are sent to the satellite communication receiver according to the 12 time slot resources pre-allocated by the edge gateway.

[0122] In this embodiment, the satellite communication transmitter sends a data volume estimate of the short message data packet to the edge gateway, enabling the edge gateway to reserve time slot resources for the short message data packet based on this data volume estimate. Thus, compared to traditional AI-based short message fallback methods, this embodiment, through collaborative scheduling of time slot resources between the satellite communication transmitter and the edge gateway (e.g., the transmitter synchronously estimates the data volume, and the gateway reserves time slots for a latency of ≤10ms), can ensure that short message data is transmitted preferentially after short message fallback is triggered, thereby effectively solving the problem of insufficient resource allocation when the window period is short.

[0123] Next, we present a specific embodiment of the short message fallback method based on artificial intelligence provided in this application, applied to a satellite communication receiver.

[0124] Please refer to Figure 9 , Figure 9 The flowchart of the short message fallback method based on artificial intelligence provided in this application is shown in some other embodiments.

[0125] like Figure 9 As shown, in some embodiments, when the satellite communication receiver applies the short message fallback method based on artificial intelligence provided in the embodiments of this application, it may include the following steps S901 and S902.

[0126] Step S901: Receive a short message data packet sent by the satellite communication transmitter; the short message data packet includes target data and quantum encrypted data of the target data, the target data being obtained by the satellite communication transmitter processing the satellite communication data.

[0127] When a satellite communication receiver communicates with a satellite communication transmitter via an edge gateway, the receiver can receive the short message data packet after the transmitter triggers a short message fallback procedure and sends it through the edge gateway. This short message data packet can include target data obtained by the transmitter processing satellite communication data based on a message triggering scheme, and quantum encrypted data (such as a quantum signature) generated by encrypting the target data using a random number seed generated by QKD.

[0128] Step S902: Perform security verification on the target data based on the quantum encryption data, and if the target data passes the security verification, reassemble the target data to obtain the recovered satellite communication data.

[0129] After receiving a short message data packet, the satellite communication receiver can perform security verification on the target data based on the quantum-encrypted data in it to confirm the integrity of the target data and whether it has been tampered with. Then, if the target data passes the security verification and is confirmed to be complete and tamper-free, the satellite communication receiver will reassemble the target data (e.g., automatically splicing it according to timestamps) to obtain the recovered satellite communication data (e.g., a complete voice formed by splicing multiple 100ms segments).

[0130] In some embodiments, the short message data packet includes a short message data packet obtained by processing voice data based on a fallback mode. In this case, after receiving the short message data packet, the satellite communication receiver can complete the voice splicing after link restoration based on the built-in data recovery module.

[0131] For example, the satellite communication receiver can be a ground rescue command center. The receiver can process the received short message data packets to verify the integrity of each voice segment through quantum signatures, and remove tampered and abnormal segments (segment loss rate of 1.1%, which can be further reduced by algorithm optimization if it is lower than the preset threshold of 0.5%). Finally, the remaining voice segments are spliced ​​together in the order of timestamps to recover the complete rescue voice communication content.

[0132] In this embodiment, a satellite communication receiver receives short message data packets sent by a satellite communication transmitter. Then, based on the quantum-encrypted data within these packets, a security verification process is performed on the target data to confirm its integrity and whether it has been tampered with. Once the target data passes the security verification and is confirmed to be intact and untampered, it is reassembled to obtain the recovered satellite communication data. For example, the satellite communication transmitter divides the voice data into 100ms standardized segments + timestamps + segment index markers, and the receiver automatically splices them together. This reduces the voice segment loss rate to <0.5%, enabling accurate voice recovery, eliminating splicing stutters and misalignments, and ensuring high-quality voice communication.

[0133] Next, we present a specific embodiment of the short message fallback method based on artificial intelligence provided in this application, applied to an edge gateway.

[0134] Please refer to Figure 10 , Figure 10 The flowchart of the short message fallback method based on artificial intelligence provided in this application is shown in some other embodiments.

[0135] like Figure 10As shown, in some embodiments, when the satellite communication receiver applies the short message fallback method based on artificial intelligence provided in the embodiments of this application, it may include the following steps S1001 to S1003.

[0136] Step S1001: Receive the estimated data volume information of the short message data packet sent by the satellite communication transmitter; the short message data packet is obtained by the satellite communication transmitter processing the satellite communication data.

[0137] When a satellite communication transmitter communicates with a satellite communication receiver via an edge gateway, it can send an estimated data volume of the short message data packet to the edge gateway through a coordination module after generating the short message data packet. The edge gateway can then receive this estimated data volume information.

[0138] Step S1002: Reserve time slot resources for the short message data packet based on the data volume estimation information.

[0139] After receiving the data volume estimation information, the edge gateway immediately reserves time slot resources for the short message data packet based on the data volume estimation information to prioritize the transmission of the short message data packet.

[0140] Step S1003: In response to receiving the short message data packet sent by the satellite communication transmitter, the short message data packet is preferentially transmitted to the satellite communication receiver based on the time slot resources.

[0141] After the satellite communication transmitter formally sends the short message data packet to the edge gateway, the edge gateway can respond by receiving the short message data packet and directly use the previously reserved time slot resources to prioritize the transmission of the short message data packet to the satellite communication receiver.

[0142] For example, the satellite communication transmitter, following the fallback mode recommended in the message triggering scheme, splits the voice data in the satellite communication data into 100ms / segment voice segments. Then, using the built-in quantum key generation module, it generates a unique quantum signature for each segment based on the random number seed generated by QKD. The satellite communication transmitter then encapsulates the voice segment, quantum signature, timestamp, and segment index into a short message data packet. Simultaneously, the satellite communication transmitter sends a fallback request and data volume estimation information (estimated transmission of 180 voice segments, requiring a total of 12 time slots) to the ground emergency rescue edge gateway.

[0143] After receiving the data volume estimation information, the edge gateway completes the pre-allocation of time slot resources within 8ms or 10ms based on the data volume estimation information to reserve 12 time slot resources, and feeds back the time slot allocation results to the satellite communication transmitter, thereby ensuring that short message data packets are transmitted first and avoiding competition for resources with other rescue terminals.

[0144] Subsequently, the satellite communication transmitter responds to the fallback timing in the arrival message triggering scheme, and the real-time SNR value of the link (e.g., 2.8dB) also drops to the adjusted SNR triggering threshold (e.g., 3dB), triggering the short message fallback process to stop voice transmission and switch to short message transmission mode. In this mode, the encapsulated short message data packets are sent to the satellite communication receiver according to the 12 time slot resources pre-allocated by the edge gateway.

[0145] In this embodiment, the satellite communication transmitter sends a data volume estimate of the short message data packet to the edge gateway, enabling the edge gateway to reserve time slot resources for the short message data packet based on this data volume estimate. Thus, compared to traditional AI-based short message fallback methods, this embodiment, through collaborative scheduling of time slot resources between the satellite communication transmitter and the edge gateway—for example, the transmitter synchronously estimates the data volume, and the gateway reserves time slots with a latency of ≤10ms—ensures that short message data is transmitted preferentially after short message fallback is triggered, effectively solving the problem of insufficient resource allocation when the window period is short.

[0146] Please refer to Figure 11 , Figure 11 The overall flowchart of the AI-based short message fallback method provided in the embodiments of this application is shown in a complete embodiment.

[0147] like Figure 11 As shown, in one embodiment, when the satellite communication transmitter communicates with the satellite communication receiver through an edge gateway, it can apply the artificial intelligence-based short message fallback method provided in this application embodiment to achieve the following processes a) to e) by coordinating the edge gateway and the satellite communication receiver.

[0148] a) Acquisition and preprocessing of multi-dimensional satellite communication feature data: The satellite communication transmitter's built-in GPS module, link monitoring module, and power consumption sensor collect satellite ephemeris trajectory data, real-time link quality data (SNR, bit error rate), and terminal power consumption data, respectively. Using a pre-stored terrain database and combined with terminal positioning information, it matches obstruction types and quantifies obstruction scenarios such as mountains, canyons, and urban high-rise buildings into obstruction coefficients in the 0-1 range. Simultaneously, it extracts the remaining time data of the satellite overpass window. These five types of data undergo filtering and noise reduction preprocessing to remove outliers, forming a standardized feature dataset.

[0149] b) Short message fallback prediction processing based on artificial intelligence models: The preprocessed feature dataset is input into a lightweight CNN-LSTM hybrid prediction model. The CNN layer is used to extract spatial features such as occlusion coefficient and link quality, while the LSTM layer is used to capture temporal features such as satellite ephemeris trajectory and remaining window time. The model outputs three decision results as the message triggering scheme: fallback trigger probability, fallback timing, and fallback mode. When the fallback trigger probability in the message triggering scheme is ≥85%, the subsequent fallback process is initiated. The fallback mode in the message triggering scheme is dynamically matched according to the link bandwidth and terminal power consumption, and is divided into speech segment + quantum signature mode (sufficient bandwidth), speech-to-text summarization mode (limited bandwidth), and location only + emergency identification mode (extremely low power consumption).

[0150] c) Dynamic adjustment of trigger threshold: Instead of a fixed threshold mechanism, a dynamic threshold adjustment rule is established based on the remaining time data of the satellite overpass window and the terminal power consumption data: when the remaining time data of the window is ≥3 minutes and the terminal power consumption data is ≥50%, the SNR trigger threshold is set to <3dB to prioritize the complete transmission of voice segments; when the remaining time data of the window is <30 seconds and the terminal power consumption data is <30%, the SNR trigger threshold is increased to <5dB to prioritize the transmission of core emergency data and avoid resource waste.

[0151] d) Quantum signature encapsulation and edge gateway coordinated scheduling: Voice data is processed in a fallback mode, splitting the voice data in satellite communication into fixed 100ms segments. A unique quantum signature is generated for each segment using a random number seed generated by QKD. The voice segment, quantum signature, timestamp, and segment index are encapsulated into a short message. Simultaneously with the terminal sending a fallback request, data volume estimation information is sent to the edge gateway. The gateway pre-allocates short message time slot resources within 10ms, prioritizing short message transmission and avoiding transmission failures caused by link congestion.

[0152] e) Link restoration and voice data reassembly: When the link quality is determined to have recovered to an SNR ≥ the adjusted threshold and last for 200ms based on real-time link quality data, the terminal automatically switches back to voice communication mode. After receiving the short message, the receiving end verifies the integrity of each voice segment using quantum signatures, eliminates tampered or missing segments, and automatically splices the remaining segments in timestamp order to restore the complete voice stream.

[0153] In this embodiment, the hardware configuration of the satellite communication terminal (including the transmitter and receiver) includes: a data acquisition module, a preprocessing module, an AI prediction and decision-making module, a dynamic threshold adjustment module, a quantum signature encapsulation module, a collaborative scheduling module, and a data recovery module. These modules work collaboratively to execute the aforementioned AI-based short message fallback method, which can adapt to weak link scenarios with an obstruction angle > 60° and a satellite overpass window period < 5 minutes. It performs AI-driven intelligent fallback of short messages from weak links, thereby solving the defects of traditional satellite weak link short message fallback technologies, such as delayed triggering, static and rigid thresholds, and lack of data security guarantees.

[0154] Next, the artificial intelligence-based short message fallback method provided in this application embodiment will be described in detail, taking into account the application scenario of emergency rescue in mountainous areas.

[0155] It should be noted that the application scenario of this embodiment is mountain canyon rescue. In this area, the satellite obstruction angle is >70°, the satellite overpass window is only 3 minutes, and the real-time signal-to-noise ratio (SNR) of the link fluctuates between 2-6dB. This is a typical scenario of weak link with incomplete satellite coverage. The satellite communication terminal used integrates a GPS positioning module, a link monitoring module, a power consumption sensor, a lightweight CNN-LSTM chip, and a quantum key generation module, and establishes a communication connection with the ground emergency rescue edge gateway.

[0156] Step 1: Multi-dimensional satellite communication feature data acquisition and preprocessing.

[0157] The terminal obtains the orbital parameters of the target satellite through the satellite ephemeris receiving module and calculates that the remaining time of the satellite's overhead window is 3 minutes. The link monitoring module collects link quality data such as real-time SNR value (fluctuation range 2-6dB) and bit error rate (approximately 0.02) at a sampling interval of 10ms. The power consumption sensor monitors that the terminal's current power consumption is 60%, which is within the sufficient range. The GPS module locates the terminal's current position as a mountain canyon. Based on the pre-stored terrain database, it matches the obstruction type as "mountain shading on both sides of the canyon" and quantifies it as an obstruction coefficient of 0.85.

[0158] The five types of data collected above are preprocessed by filtering and noise reduction to remove instantaneous spikes in the SNR values. All data are then standardized to feature values ​​in the 0-1 range to form a standardized feature dataset.

[0159] Step 2: AI model predicts decisions.

[0160] The preprocessed feature dataset is input into the lightweight CNN-LSTM hybrid prediction model built into the terminal. The CNN layer of the model extracts spatial features such as occlusion coefficient and SNR value, identifying the current scene as a high-occlusion, weak-link environment; the LSTM layer captures the temporal variation features of the satellite overpass window, predicting that the link quality will drop to SNR < 3dB after 1 minute. The model outputs the following decision results: fallback trigger probability 92% (≥ preset threshold 85%), fallback timing 1 minute later, and fallback mode is speech segment + quantum signature mode (because the current terminal power consumption is sufficient, the link bandwidth can support segment transmission).

[0161] Step 3: Dynamic threshold adjustment.

[0162] Based on the remaining time data of the satellite overpass window (2 minutes, after a 1-minute wait) and the current terminal power consumption data (still 58%), the dynamic threshold adjustment rule is executed: Since the condition of remaining time data of the window ≥ 3 minutes is not met, but the terminal power consumption data is ≥ 50%, combined with the current high occlusion scenario with occlusion angle > 70°, the SNR trigger threshold is set to < 3dB, that is, when the link SNR value drops below 3dB, the short message fallback process is immediately started.

[0163] Step 4: Quantum signature encapsulation and edge gateway coordinated scheduling.

[0164] The terminal splits the voice data into 100ms segments in fallback mode. Using the built-in quantum key generation module, a unique quantum signature is generated for each segment based on the random number seed generated by QKD. The terminal encapsulates the voice segment, quantum signature, timestamp, and segment index into a short message data packet and sends a fallback request and data volume estimation information (estimated to transmit 180 voice segments, requiring a total of 12 time slots) to the ground emergency rescue edge gateway.

[0165] The edge gateway completes the pre-allocation of time slot resources within 8ms and feeds back the time slot allocation results to the terminal, ensuring that short messages are transmitted first and avoiding competition for resources with other rescue terminals.

[0166] Step 5: Short message data packet transmission and voice reconstruction after link recovery.

[0167] One minute later, the link SNR value dropped to 2.8dB, triggering the short message fallback process. The terminal stopped voice transmission, switched to short message transmission mode, and sent the encapsulated short message data packets according to the pre-allocated time slots.

[0168] Three minutes later, the satellite overhead window ended, the link quality recovered, the SNR value stabilized at 5.5dB for 200ms, and the terminal automatically switched back to voice communication mode.

[0169] The receiving end (ground rescue command center) processes the received short message data packets, verifies the integrity of each voice segment through quantum signature, removes two tampered abnormal segments (segment loss rate of 1.1%, which can be further reduced by algorithm optimization if it is below the preset threshold of 0.5%), and splices the remaining 178 voice segments in timestamp order to restore the complete rescue voice communication content.

[0170] like Figure 12 As shown, using the timeline as a guide and combining specific parameters (such as 3 minutes remaining in the window period and SNR dropping to 2.8dB), the execution time nodes of each step, the module interaction process, and key performance indicators (such as 8ms gateway time slot allocation latency and 99.3% transmission success rate) are described in actual application scenarios. That is, through practical application verification, in mountainous high-obstruction weak link scenarios, the voice-to-short message switching lag time is shortened to 0.8 seconds, the short message transmission success rate reaches 99.3%, and the voice segment tampering recognition accuracy rate is 100%, which fully meets the communication needs of emergency rescue scenarios.

[0171] Please refer to Figure 13 This application also provides an AI-based short message fallback device, which can implement the above-mentioned AI-based short message fallback method.

[0172] like Figure 13 As shown in the embodiments of this application, the artificial intelligence-based short message fallback device may include: The acquisition module is used to acquire at least one of the following as multi-dimensional satellite communication characteristic data: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and satellite overpass window remaining time data; The hybrid prediction module is used to perform short message fallback prediction processing on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model, and to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme. The fallback triggering module is used to trigger short message fallback based on the message triggering scheme.

[0173] In some embodiments, the artificial intelligence model includes a lightweight hybrid prediction model, which includes a convolutional neural network layer and a deep learning layer; The hybrid prediction module is further configured to extract spatial feature data from the multi-dimensional satellite communication feature data based on the convolutional neural network layer; extract temporal feature data from the multi-dimensional satellite communication feature data based on the deep learning layer; and perform the short message fallback prediction processing on the spatial feature data and the temporal feature data to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme.

[0174] In some embodiments, the multi-dimensional satellite communication feature data includes terminal power consumption data and satellite overpass window remaining time data, and the message triggering scheme includes fallback trigger probability; The fallback triggering module is further configured to, when the fallback triggering probability in the message triggering scheme is greater than or equal to a preset probability threshold, adaptively adjust the short message fallback triggering threshold based on the terminal power consumption data and the remaining time data of the satellite overpass window, to obtain an adjusted triggering threshold; and trigger short message fallback based on the adjusted triggering threshold.

[0175] In some embodiments, the message triggering scheme further includes a fallback timing, the adjusted triggering threshold includes an adjusted signal-to-noise ratio triggering threshold, and the fallback triggering module is further configured to trigger short message fallback in response to the arrival of the fallback timing and the real-time signal-to-noise ratio being less than or equal to the adjusted signal-to-noise ratio triggering threshold.

[0176] In some embodiments, the multi-dimensional satellite communication feature data includes terminal power consumption data and link real-time quality data, and the model prediction results include fallback modes; The hybrid prediction module is further configured to perform short message fallback prediction processing on the bandwidth and / or terminal power consumption data corresponding to the real-time quality data of the link based on a preset artificial intelligence model, so as to obtain the fallback mode.

[0177] In some embodiments, the fallback triggering module is further configured to process satellite communication data according to the fallback mode to obtain short message data packets.

[0178] In some embodiments, the satellite communication data includes voice data, and the fallback mode includes one of a voice segment mode, a speech-to-text mode, and an identifier data mode; the voice segment mode is used to instruct the voice data to be split into at least two voice segments, and the short message data packet is generated based on the at least two voice segments; the speech-to-text mode is used to instruct the voice data to be converted into text summary data, and the short message data packet is generated based on the text summary data; the identifier data mode is used to instruct the generation of a short message data packet based on terminal status data, wherein the terminal status data is the status data of the satellite communication transmitter.

[0179] In some embodiments, the fallback triggering module is further configured to process satellite communication data based on the message triggering scheme to obtain processed target data; perform quantum encryption processing on the target data to obtain quantum encrypted data of the target data; and generate short message data packets based on the target data and the quantum encrypted data.

[0180] In some embodiments, the fallback triggering module is further configured to process satellite communication data based on the message triggering scheme to obtain short message data packets; The short message fallback device based on artificial intelligence provided in this application embodiment may further include: The collaborative scheduling module is also used to send the data volume estimation information of the short message data packet to the edge gateway, so that the edge gateway reserves the time slot resources of the short message data packet based on the data volume estimation information; the time slot resources are used to transmit the short message data packet to the satellite communication receiver.

[0181] In some embodiments, the AI-based short message fallback device provided in this application may further include: The data recovery module is used to receive short message data packets sent by a satellite communication transmitter; the short message data packets include target data and quantum-encrypted data of the target data, the target data being obtained by the satellite communication transmitter processing satellite communication data; and, based on the quantum-encrypted data, performing security verification processing on the target data, and if the target data passes the security verification, performing reconstructing processing on the target data to obtain the recovered satellite communication data.

[0182] In some embodiments, the AI-based short message fallback device provided in this application may further include: The coordination command receiving module is used to receive data volume estimation information of short message data packets sent by the satellite communication transmitter; the short message data packets are obtained by the satellite communication transmitter processing satellite communication data; The resource pre-allocation module is used to reserve time slot resources for the short message data packets based on the data volume estimation information. The short message transmission module is used to, in response to receiving the short message data packet sent by the satellite communication transmitter, prioritize transmitting the short message data packet to the satellite communication receiver using the reserved time slot resources.

[0183] It should be noted that the specific implementation of the AI-based short message fallback device provided in this application is basically the same as the specific implementation of the AI-based short message fallback method described above, and will not be repeated here.

[0184] Please see Figure 14 This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-mentioned short message fallback method based on artificial intelligence.

[0185] In some embodiments, the electronic device may be any smart terminal such as an in-vehicle terminal, an in-vehicle hardware platform (e.g., an in-vehicle computer), a tablet computer, a smartphone, or a wearable device; or, the electronic device may be a vehicle including a memory and a processor.

[0186] like Figure 14 As shown, the electronic device provided in this application embodiment may include: The processor 1401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1402 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1402 and is called and executed by the processor 1401 to implement the artificial intelligence-based short message fallback method of the embodiments of this application. The input / output interface 1403 is used to implement information input and output; The communication interface 1404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1405 transmits information between various components of the device (e.g., processor 1401, memory 1402, input / output interface 1403, and communication interface 1404); The processor 1401, memory 1402, input / output interface 1403 and communication interface 1404 are connected to each other within the device via bus 1405.

[0187] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned artificial intelligence-based short message fallback method.

[0188] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0189] This application also provides a computer program product, including a computer program. The steps implemented by the computer program when executed by a processor are basically the same as those in the specific embodiments of the artificial intelligence-based short message fallback method described above, and will not be repeated here.

[0190] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of this application, and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0191] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0192] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0193] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0194] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0195] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0196] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0197] The units described above as separate components may or may not be physically separate. 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 can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0199] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A short message fallback method based on artificial intelligence, characterized in that, The method includes: Acquire at least one of the following as multi-dimensional satellite communication characteristic data: satellite ephemeris trajectory data, link real-time quality data, terminal power consumption data, obstruction type quantification data, and satellite overpass window remaining time data; Based on a preset artificial intelligence model, the multi-dimensional satellite communication feature data is processed for short message fallback prediction to obtain at least one of the fallback trigger probability, fallback timing, and fallback mode as a message triggering scheme. The short message fallback is triggered based on the aforementioned message triggering scheme.

2. The method according to claim 1, characterized in that, The artificial intelligence model includes a lightweight hybrid prediction model, which includes a convolutional neural network layer and a deep learning layer. The process of performing short message fallback prediction on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model yields at least one of the following fallback triggering schemes: fallback trigger probability, fallback timing, and fallback mode: Spatial feature data is extracted from the multi-dimensional satellite communication feature data based on the convolutional neural network layer; Based on the deep learning layer, temporal feature data is extracted from the multi-dimensional satellite communication feature data; The spatial feature data and the temporal feature data are subjected to the short message fallback prediction processing to obtain at least one of the following: fallback trigger probability, fallback timing, and fallback mode, which is a message triggering scheme.

3. The method according to claim 1, characterized in that, The multi-dimensional satellite communication feature data includes terminal power consumption data and satellite overpass window remaining time data; the message triggering scheme includes fallback trigger probability. The triggering of short message fallback based on the message triggering scheme includes: When the fallback trigger probability in the message triggering scheme is greater than or equal to a preset probability threshold, the trigger threshold for short message fallback is adaptively adjusted based on the terminal power consumption data and the remaining time data of the satellite overpass window, to obtain the adjusted trigger threshold. The short message fallback is triggered based on the adjusted trigger threshold.

4. The method according to claim 3, characterized in that, The message triggering scheme also includes a fallback timing mechanism. The adjusted triggering threshold includes an adjusted signal-to-noise ratio triggering threshold. Triggering short message fallback based on the adjusted triggering threshold includes: In response to the arrival of the fallback timing, and when the real-time signal-to-noise ratio is less than or equal to the adjusted signal-to-noise ratio trigger threshold, short message fallback is triggered.

5. The method according to claim 1, characterized in that, The multi-dimensional satellite communication feature data includes terminal power consumption data and link real-time quality data, and the message triggering scheme includes fallback mode; The process of performing short message fallback prediction on the multi-dimensional satellite communication feature data based on a preset artificial intelligence model yields at least one of the following fallback triggering schemes: fallback trigger probability, fallback timing, and fallback mode: Based on a preset artificial intelligence model, short message fallback prediction processing is performed on the bandwidth and / or terminal power consumption data corresponding to the real-time quality data of the link to obtain the fallback mode.

6. The method according to claim 5, characterized in that, The triggering of short message fallback based on the message triggering scheme includes: The satellite communication data is processed according to the described fallback mode to obtain short message data packets.

7. The method according to claim 6, characterized in that, The satellite communication data includes voice data, and the fallback mode includes one of voice segment mode, speech-to-text mode, and identification data mode; the voice segment mode is used to indicate that the voice data is split into at least two voice segments and that the short message data packet is generated based on the at least two voice segments; the speech-to-text mode is used to indicate that the voice data is converted into text summary data and that the short message data packet is generated based on the text summary data. The identification data pattern is used to indicate the generation of short message data packets based on terminal status data, where the terminal status data is the status data of the satellite communication transmitter.

8. The method according to claim 1, characterized in that, The triggering of short message fallback based on the message triggering scheme includes: The satellite communication data is processed based on the aforementioned message triggering scheme to obtain the processed target data; The target data is subjected to quantum encryption to obtain quantum encrypted data of the target data; A short message data packet is generated based on the target data and the quantum encrypted data.

9. The method according to any one of claims 1 to 8, characterized in that, The method is applied to a satellite communication transmitter, and the triggering of short message fallback based on the message triggering scheme includes: The satellite communication data is processed based on the aforementioned message triggering scheme to obtain short message data packets; After triggering short message fallback based on the message triggering scheme, the method further includes: The data volume estimation information of the short message data packet is sent to the edge gateway, so that the edge gateway reserves time slot resources for the short message data packet based on the data volume estimation information; the time slot resources are used to transmit the short message data packet to the satellite communication receiver.

10. A short message fallback method based on artificial intelligence, characterized in that, The method is applied to a satellite communication receiver, and the method includes: Receive short message data packets sent by a satellite communication transmitter; the short message data packets include target data and quantum-encrypted data of the target data, the target data being obtained by the satellite communication transmitter processing satellite communication data; The target data is subjected to security verification based on the quantum encrypted data, and if the target data passes the security verification, the target data is reassembled to obtain the recovered satellite communication data.

11. A short message fallback method based on artificial intelligence, characterized in that, The method is applied to an edge gateway, and the method includes: The system receives data volume estimation information for short message data packets sent by a satellite communication transmitter; the short message data packets are obtained by the satellite communication transmitter processing satellite communication data. Based on the data volume estimation information, time slot resources for the short message data packets are reserved; In response to receiving the short message data packet sent by the satellite communication transmitter, the short message data packet is preferentially transmitted to the satellite communication receiver based on the time slot resources.

12. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the short message fallback method based on artificial intelligence as described in any one of claims 1 to 11.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the artificial intelligence-based short message fallback method as described in any one of claims 1 to 11.

14. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the artificial intelligence-based short message fallback method as described in any one of claims 1 to 11.

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