Spacecraft multi-mode wireless transmission link adaptation method and system

Abstract

The present application relates to the technical field of satellite communication, and especially relates to a spacecraft multi-standard wireless transmission link adaptation method and system, which first collects the space-ground channel state and service demand, constructs a laser / radio heterogeneous link dynamic model, and calculates the transmission capacity and interruption probability. Secondly, the adaptation scheduling weight is generated based on the above indexes, and is decomposed into a slow-changing steady-state component and a fast-changing transient component; the potential change characteristics of the transmission load are analyzed in combination with the steady-state weight and the defined link jitter waveform. Then, the self-organizing adaptation strategy is generated according to the load characteristics, and the quality of service guarantee demand distribution of the multi-standard service is analyzed. Finally, the adaptation coefficient of the distribution is calculated, and if it meets the preset threshold, the strategy is established as the target adaptation scheme. Through the weight analysis and closed-loop verification mechanism of static and dynamic separation, the intelligent self-organizing scheduling of the heterogeneous link in the complex space environment is realized, and the transmission robustness and service quality guarantee capability are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a method and system for adapting multi-standard wireless transmission links for spacecraft. Background Technology

[0002] With the rapid development of low-Earth orbit satellite internet and integrated space-air communication networks, the demand for data transmission between spacecraft and the ground is exploding, and the types of services are becoming increasingly diversified, covering a variety of high-bandwidth, low-latency, and high-reliability application scenarios, such as high-definition remote sensing imaging, real-time video transmission, IoT access, and scientific exploration data backhaul. Traditional space-to-ground communication links mostly rely on single radio frequency (RF) or laser communication systems, making it difficult to balance transmission performance and robustness in complex and dynamic channel environments. RF links have good all-weather penetration capabilities, but spectrum resources are scarce and transmission rates are limited; laser links have advantages such as high bandwidth and strong anti-interference capabilities, but are susceptible to environmental factors such as atmospheric turbulence and cloud attenuation, resulting in poor link stability. Therefore, constructing a heterogeneous communication link system that integrates laser and RF technologies to achieve the coordinated utilization of multi-standard transmission resources has become an important technical direction for improving the efficiency of space-to-ground communication.

[0003] In existing technologies, some studies attempt to select between laser and radio frequency links through static switching or simple load balancing, lacking the ability to jointly perceive and intelligently adapt to dynamic channel changes and service quality (QoS) requirements. Especially in the context of multi-beam intelligent phased array terminals, satellite-to-ground links face challenges such as rapid switching, significant Doppler shift, and severe channel fading. Traditional fixed configurations or open-loop control strategies are insufficient to achieve efficient and stable transmission link management. Furthermore, limited onboard resources necessitate an intelligent adaptation method capable of self-organizing and adaptively allocating laser and radio frequency link resources dynamically to address transmission uncertainties caused by space environment disturbances and service load fluctuations.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for adapting multi-standard wireless transmission links in spacecraft. This aims to solve the technical problem that existing space-to-ground communication technologies lack the ability to jointly perceive and intelligently adapt to the dynamic channel characteristics and multi-service quality of service requirements of laser and radio frequency heterogeneous links, making it difficult to achieve efficient, stable, and low-interruption self-organizing scheduling of multi-standard resources in complex space environments.

[0006] To achieve the above objectives, the present invention provides a method for adapting multi-standard wireless transmission links in spacecraft, the method comprising: Collect the channel status parameters and service quality requirements of the star-to-ground link of the target spacecraft's multi-beam intelligent phased array terminal, establish a dynamic transmission model of the star-to-ground laser and radio frequency heterogeneous link, and calculate the transmission capacity and interruption probability of the heterogeneous link based on the channel status parameters and service quality requirements of the star-to-ground link using the dynamic transmission model. Based on the transmission capacity and interruption probability, the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous link is output, and the adaptation scheduling weight is decomposed into a slow-varying channel steady-state component and a fast-varying fading transient component. Determine the steady-state scheduling weight of the slow-varying channel steady-state component, define the link jitter waveform of the fast-varying fading transient component, and combine the steady-state scheduling weight and the link jitter waveform to analyze the potential variation characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link. Based on the potential changes in the transmission load, a self-organizing adaptation strategy for the heterogeneous space-to-ground laser and radio frequency links is generated, and the service quality assurance requirements of the target spacecraft's multi-standard services are analyzed. Based on the self-organizing adaptation strategy, the adaptation coefficient of the service quality assurance requirement distribution is calculated. When the adaptation coefficient meets the preset adaptation threshold standard, the self-organizing adaptation strategy is used as the target multi-standard link adaptation scheme for the target spacecraft.

[0007] Optionally, the step of outputting the adaptation scheduling weights for the satellite-to-ground laser and radio frequency heterogeneous links based on the transmission capacity and interruption probability includes: The transmission capacity and interruption probability are normalized and weighted to obtain the total link quality score; Define the candidate link transmission time slots for the aforementioned satellite-to-ground laser and radio frequency heterogeneous links; Calculate the link priority weight of the candidate link transmission slot; Based on the total link quality score and the link priority weight, the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links is calculated.

[0008] Optionally, calculating the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links based on the total link quality score and the link priority weight includes: Analyze the channel fading statistics and service delay sensitivity coefficient of the aforementioned satellite-to-ground laser and radio frequency heterogeneous links; Based on the channel fading statistical characteristics, the service delay sensitivity coefficient, the total link quality score, and the link priority weight, the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links is calculated using the following formula: ; in, This represents the adaptation scheduling weight of the k-th candidate heterogeneous link. This represents the link priority weight coefficient. This represents the priority score of the k-th candidate link. This represents the link quality normalization coefficient. This represents the transmission capacity weighting factor. This represents the normalized transmission capacity of the k-th candidate link. This represents the interruption probability penalty factor. This represents the normalized interruption probability of the k-th candidate link. This represents the business latency weighting coefficient. This represents the normalized score of the k-th candidate link that meets the service latency requirements.

[0009] Optionally, establishing the dynamic transmission model for the satellite-to-ground laser and radio frequency heterogeneous links includes: Establish the topological connection matrix between the multi-beam intelligent phased array terminal and the satellite and ground station in the space-ground coordinate system; Define the dynamic transmission parameters of the satellite-to-ground laser and radio frequency heterogeneous link, wherein the dynamic transmission parameters include the atmospheric attenuation parameters of the laser link and the rain attenuation parameters of the radio frequency link; Based on the dynamic transmission parameters, a dynamic transmission equation is constructed for the satellite-to-ground laser and radio frequency heterogeneous link, wherein the dynamic transmission equation includes a transmission capacity calculation equation and an interruption probability calculation equation. Based on the topology connection matrix and the dynamic transmission equation, a dynamic transmission model for the satellite-to-ground laser and radio frequency heterogeneous link is established.

[0010] Optionally, the step of decomposing the adaptive scheduling weights into slow-varying channel steady-state components and fast-varying fading transient components includes: The adapted scheduling weights are denoised and preprocessed to obtain preprocessed adapted scheduling weights. The preprocessed adaptive scheduling weights are subjected to wavelet transform to obtain transformed adaptive scheduling weights; Extract the slow-changing and fast-changing components of the transformation adaptation scheduling weights; The slow-changing component and the fast-changing component are reconstructed respectively to obtain the slow-changing channel steady-state component and the fast-changing fading transient component.

[0011] Optionally, the link jitter waveform defining the fast-fading transient component includes: Identify the spectral peaks of the rapidly fading transient component to calculate the total energy of the rapidly fading transient component; Construct the basic jitter waveform of the rapidly fading transient component; The non-stationary fading effect and natural attenuation characteristics of the fast-changing fading transient component are analyzed to increase the channel fading noise of the basic jitter waveform, thereby obtaining the noisy fast-changing fading component. Based on the natural attenuation characteristics and the total energy of the fast-changing component, the link jitter waveform of the fast-fading transient component is determined.

[0012] Optionally, the step of combining the steady-state scheduling weights and the link jitter waveform to analyze the potential changes in the transmission load of the satellite-to-ground laser and radio frequency heterogeneous links includes: The steady-state scheduling weight and the link jitter waveform are synchronized in time to obtain synchronized steady-state scheduling weight and synchronized link jitter waveform; Generate a composite link state signal consisting of the synchronous steady-state scheduling weight and the synchronous link jitter waveform; Analyze the time spectrum and transmission load steady-state coefficient of the composite link state signal; Calculate the rapidly varying energy difference of the time spectrum; Based on the rapidly varying energy differential and the steady-state coefficient of the transmission load, the potential variation characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link are determined.

[0013] Optionally, generating a self-organizing adaptation strategy for the satellite-to-ground laser and radio frequency heterogeneous links based on the potential variation characteristics of the transmission load includes: Construct a six-dimensional feature vector of the potential variation characteristics of the transmission load; The six-dimensional feature vector is normalized to obtain a normalized six-dimensional feature vector; Based on the normalized six-dimensional feature vector, the laser link power adjustment, radio frequency link beam pointing adjustment, and link switching threshold adjustment of the satellite-to-ground laser and radio frequency heterogeneous link are analyzed. By combining the laser link power adjustment, the radio frequency link beam pointing adjustment, and the link switching threshold adjustment, a self-organizing adaptation strategy for the heterogeneous satellite-to-ground laser and radio frequency links is generated.

[0014] Optionally, the analysis of the service quality assurance requirements distribution of the target spacecraft's multi-standard services includes: Collect multi-standard service quality requirements data for the target spacecraft; Construct a two-dimensional matrix of the multi-standard service quality requirement data to calculate the normalized QoS index of the service queue corresponding to the target spacecraft; Define the service type region of the target spacecraft; Based on the normalized QoS metric, the demand percentage for the service type region is calculated using the following formula: ; in, This represents the demand percentage for the s-th business type in the region. This represents the s-th business type region. This represents the index of the business queue in the s-th business type region. This indicates that the s-th business type region is located in Normalized QoS metrics for the service queue This indicates that the s-th business type region is located in The weight coefficient of the business queue, This represents the sum of QoS metrics for all service queues; The distribution of service quality assurance requirements for the target spacecraft's multi-mode services is determined by the aforementioned demand proportions.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a spacecraft multi-standard wireless transmission link adaptation system, the system comprising: The modeling and evaluation module is used to collect the channel status parameters and service quality requirements of the star-ground link of the target spacecraft's multi-beam intelligent phased array terminal, establish a dynamic transmission model of the star-ground laser and radio frequency heterogeneous link, and calculate the transmission capacity and interruption probability of the heterogeneous link based on the channel status parameters and service quality requirements of the star-ground link using the dynamic transmission model. The weight decomposition module is used to output the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links based on the transmission capacity and the interruption probability, and decompose the adaptation scheduling weight into a slow-varying channel steady-state component and a fast-varying fading transient component. The load analysis module is used to determine the steady-state scheduling weight of the slow-varying channel steady-state component, define the link jitter waveform of the fast-varying fading transient component, and analyze the potential change characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link by combining the steady-state scheduling weight and the link jitter waveform. The strategy generation module is used to generate a self-organizing adaptation strategy for the space-to-ground laser and radio frequency heterogeneous links based on the potential change characteristics of the transmission load, and to analyze the distribution of service quality assurance requirements for multi-standard services of the target spacecraft. The threshold verification module is used to calculate the adaptation coefficient of the service quality assurance requirement distribution based on the self-organizing adaptation strategy. When the adaptation coefficient meets the preset adaptation threshold standard, the self-organizing adaptation strategy is used as the target multi-standard link adaptation scheme for the target spacecraft.

[0016] This invention provides a method for adapting multi-standard wireless transmission links in spacecraft. This method constructs a dynamic transmission model of heterogeneous space-to-ground laser and radio frequency links, comprehensively considering channel state parameters and service quality requirements to achieve accurate modeling of transmission capacity and outage probability. Furthermore, it decomposes the adaptation scheduling weights into slow-varying channel steady-state components and fast-varying fading transient components, effectively distinguishing between long-term trends and short-term fluctuations in link quality, thus improving the stability and responsiveness of resource scheduling. By introducing a joint analysis mechanism of steady-state scheduling weights and link jitter waveforms, it can accurately characterize the potential changes in the transmission load of heterogeneous links, enhancing the system's ability to predict and adapt to channel mutations. Based on this, a self-organizing adaptation strategy is generated, and the adaptation coefficient is evaluated in conjunction with the distribution of multi-standard service quality assurance requirements. The optimal solution is only activated when a preset threshold is met, ensuring the reliability of link switching and service continuity. The overall method achieves closed-loop optimization from perception, modeling, analysis to decision-making, significantly improving the resource utilization, transmission robustness, and service quality assurance capabilities of multi-standard wireless transmission links in complex and dynamic space environments. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the spacecraft multi-standard wireless transmission link adaptation method of the present invention; Figure 2 This is a structural block diagram of an embodiment of the spacecraft multi-standard wireless transmission link adaptation system of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] Reference Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the spacecraft multi-standard wireless transmission link adaptation method of the present invention, which presents an embodiment of the spacecraft multi-standard wireless transmission link adaptation method of the present invention.

[0021] In one embodiment, the spacecraft multi-standard wireless transmission link adaptation method includes: Step S100: Collect the channel status parameters and service quality requirements of the satellite-to-ground link of the target spacecraft's multi-beam intelligent phased array terminal, establish a dynamic transmission model of the satellite-to-ground laser and radio frequency heterogeneous links, and calculate the transmission capacity and interruption probability of the heterogeneous links based on the channel status parameters and service quality requirements of the satellite-to-ground link using the dynamic transmission model.

[0022] The target spacecraft can be an on-orbit space platform performing space-to-ground communication missions, serving as the main implementer of multi-standard wireless transmission link adaptation methods and carrying laser and radio frequency communication payloads. In an exemplary embodiment, the target spacecraft's communication mission attributes can be determined through orbital deployment and mission planning. The multi-beam intelligent phased array terminal can be a spaceborne or ground-based radio frequency communication antenna system with multi-beamforming and dynamic pointing capabilities, used to support multi-user access and rapid beam switching in high-speed mobile scenarios, mitigating the effects of Doppler frequency shift and channel fading. Furthermore, the multi-beam intelligent phased array terminal can be based on digital beamforming technology, achieving electronic scanning of space beams through phase control. The space-to-ground link channel state parameters can be a set of quantitative indicators describing the current physical layer transmission characteristics of the space-to-ground communication link, used to evaluate link quality and support dynamic resource scheduling decisions. In a specific embodiment, the space-to-ground link channel state parameters can be obtained through pilot signal estimation, link detection, or feedback mechanisms. Furthermore, the space-to-ground link channel state parameters can include, but are not limited to, signal-to-noise ratio, bit error rate, Doppler frequency offset, path loss, and atmospheric attenuation coefficient.

[0023] Quality of Service (QoS) requirements can be the binding performance indicators imposed by different communication services on transmission. They can serve as the basis for link selection and resource allocation, ensuring differentiated service guarantees. For example, QoS requirements may include, but are not limited to, upper limits on latency, lower limits on bandwidth, packet loss tolerance, and reliability levels. A satellite-to-ground laser and radio frequency heterogeneous link can be a composite satellite-to-ground transmission channel composed of laser communication links and radio frequency communication links. It can be used to integrate the complementary advantages of the two standards, improving overall transmission efficiency under complex channel conditions. In an exemplary embodiment, in the satellite-to-ground laser and radio frequency heterogeneous link, the laser link provides high-bandwidth, low-interference transmission, while the radio frequency link provides all-weather robust connectivity; the two work collaboratively at the resource scheduling level.

[0024] A dynamic transmission model can be a mathematical model characterizing the capacity and reliability variations of heterogeneous links under time-varying channel and service load conditions. It can provide a theoretical basis for adaptive scheduling and enable joint prediction of transmission capacity and outage probability. In one specific embodiment, the dynamic transmission model can be constructed using stochastic processes or machine learning methods based on channel state parameters and service quality requirements. Transmission capacity can be the maximum effective data rate that the heterogeneous link can support under current channel conditions. It can be used to measure the available bandwidth resources of the link and for calculating scheduling weights. Furthermore, transmission capacity can be obtained through Shannon's formula or estimated through measured throughput. Outage probability can be the probability that the link cannot meet service quality requirements under given channel and service conditions. It can be used to reflect the link reliability level and participate in the generation of adaptive scheduling weights. In an exemplary embodiment, the outage probability can be derived based on a channel fading statistical model or historical link failure records.

[0025] Collecting the channel state parameters and service quality requirements of the satellite-to-ground link of the target spacecraft's multi-beam intelligent phased array terminal can be achieved by synchronously acquiring channel and service information through the terminal's built-in detection module and service management interface. Furthermore, this operation can be implemented through periodic polling, event-triggered acquisition, or active detection based on feedback loops, thereby achieving joint perception of the environment and requirements. Establishing a dynamic transmission model for the satellite-to-ground laser and radio frequency heterogeneous links can map the channel state parameters and service quality requirements into a functional relationship of link performance indicators. For example, this operation can be achieved by constructing an analytical expression based on the physical layer channel model and fitting a nonlinear mapping relationship using a neural network, thereby achieving a unified representation of the transmission capabilities of heterogeneous links. Calculating the transmission capacity and outage probability of the heterogeneous link using the dynamic transmission model can be achieved by substituting the current channel and service parameters into the model and outputting the capacity and outage probability values. In a specific embodiment, this operation can be achieved through analytical calculation, Monte Carlo simulation, and lookup table interpolation, thereby quantifying the current availability and risk level of the link.

[0026] Step S200: Based on the transmission capacity and interruption probability, output the adaptation scheduling weights of the satellite-to-ground laser and radio frequency heterogeneous links, and decompose the adaptation scheduling weights into slow-varying channel steady-state components and fast-varying fading transient components.

[0027] The adaptive scheduling weight can be a numerical indicator used to quantify the relative priority of laser and RF links in the current scenario, and can be used to guide link resource allocation and switching decisions. In an exemplary embodiment, the adaptive scheduling weight can be generated by weighted fusion of transmission capacity and outage probability. The slow-varying channel steady-state component can be the low-frequency variation part of the adaptive scheduling weight that reflects the long-term average state of the channel, and can be used to characterize the stable trend of link quality to maintain the continuity of the scheduling strategy. Furthermore, the slow-varying channel steady-state component can be extracted by low-pass filtering or moving average of the scheduling weight. The fast-varying fading transient component can be the high-frequency variation part of the adaptive scheduling weight that reflects the short-term drastic fluctuations of the channel, and can be used to capture sudden channel degradation or improvement events, and improve system response speed. In a specific embodiment, the fast-varying fading transient component can be separated from the scheduling weight by high-pass filtering or differential operation.

[0028] The adaptive scheduling weights for satellite-to-ground laser and radio frequency heterogeneous links, based on transmission capacity and outage probability, can be generated by weighted fusion or normalization of capacity and outage probability. For example, this operation can be achieved through linear weighted combination, Pareto optimal selection, and fuzzy comprehensive evaluation, thus forming a unified link priority evaluation standard. Decomposing the adaptive scheduling weights into slowly varying channel steady-state components and rapidly varying fading transient components can be achieved by frequency-domain or time-domain decomposition of the scheduling weight time series. Furthermore, this operation can be implemented through low-pass / high-pass filter bank decomposition, empirical mode decomposition, and wavelet transform, thereby separating long-term trends from short-term disturbances and supporting hierarchical scheduling.

[0029] Step S300: Determine the steady-state scheduling weight of the slow-changing channel steady-state component, define the link jitter waveform of the fast-changing fading transient component, and combine the steady-state scheduling weight and the link jitter waveform to analyze the potential variation characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous links.

[0030] The steady-state scheduling weight can be a benchmark weight value determined by the slow-varying channel steady-state components for long-term resource allocation. It can serve as a basic reference for link scheduling, avoiding frequent switching due to instantaneous fluctuations. In an exemplary embodiment, the steady-state scheduling weight can be jointly analyzed with the link jitter waveform to jointly characterize the load evolution features. The link jitter waveform can be a time series defined by the fast-varying fading transient components, representing the short-term fluctuation pattern of link quality. It can be used to reveal channel mutation patterns and assist in predicting future load change trends. Furthermore, the link jitter waveform can be represented by normalizing and time-series modeling the transient components. The potential change characteristics of transmission load can be the future load evolution law of heterogeneous links obtained based on the joint analysis of steady-state scheduling weights and link jitter waveforms. It can be used to provide forward-looking input for the generation of self-organizing adaptation strategies. In a specific embodiment, the potential change characteristics of transmission load can be extracted through time-series pattern recognition, trend extrapolation, or anomaly detection algorithms.

[0031] Determining the steady-state scheduling weights for the slow-varying channel steady-state components can be achieved by smoothing the slow-varying components or averaging them to obtain a baseline weight. For example, this operation can be implemented using sliding window averaging, exponentially weighted moving average, or Kalman filtering estimation, thus providing a stable scheduling reference baseline. Defining the link jitter waveform for the fast-varying fading transient components can be achieved by converting the transient components into a waveform representation with a time structure. Further, this operation can be implemented through normalized time series modeling, peak-valley feature extraction, and spectral envelope construction, thereby explicitly expressing the short-term fluctuation characteristics of the channel and facilitating pattern recognition. Combining the steady-state scheduling weights and link jitter waveforms to analyze the potential changes in transmission load of satellite-to-ground laser and RF heterogeneous links can involve jointly analyzing the steady-state baseline and jitter patterns to infer future load trends. In an exemplary embodiment, this operation can be achieved by combining trend extrapolation with anomaly warning, hidden Markov model state prediction, and LSTM time series prediction, thereby enhancing the ability to predict channel mutations and adjusting resources in advance.

[0032] Step S400: Based on the potential changes in transmission load, generate a self-organizing adaptation strategy for heterogeneous links between satellite-to-ground laser and radio frequency, and analyze the distribution of service quality assurance requirements for multi-standard services of the target spacecraft.

[0033] The self-organizing adaptation strategy can be an automatically generated link resource configuration and switching scheme based on the potential changes in transmission load. This can be used to achieve dynamic link optimization without manual intervention, enhancing system autonomy. In one specific embodiment, the self-organizing adaptation strategy can be generated based on a rule engine, reinforcement learning, or optimization algorithm. Multi-standard services can be a collection of different types of communication services that simultaneously rely on laser and radio frequency links for transmission. This can be used to drive differentiated QoS guarantee requirements and influence the formulation of adaptation strategies. Furthermore, multi-standard services can include, but are not limited to, remote sensing image backhaul, real-time video streaming, scientific exploration data, and IoT sensor reporting. The service quality assurance requirement distribution can be a statistical or structured expression of the QoS indicator requirements of multi-standard services. This can be used to evaluate whether the adaptation strategy meets the overall service guarantee objectives. In an exemplary embodiment, the service quality assurance requirement distribution can be constructed through service classification and QoS template matching.

[0034] Generating self-organizing adaptation strategies for heterogeneous space-to-ground laser and radio frequency links based on potential changes in transmission load can automatically plan link usage ratios, switching timings, and redundancy configurations based on predictive features. For example, this operation can be achieved through rule-based policy library matching, online optimization, and deep reinforcement learning decision-making, enabling dynamic optimization without manual intervention. Analyzing the distribution of QoS assurance requirements for multi-standard services on the target spacecraft can involve structuring and weighting the QoS requirements of currently operating services. Further, this operation can be achieved through QoS template matching, service priority ranking, and requirement clustering analysis, thereby clearly defining adaptation targets and ensuring that the strategy meets service constraints.

[0035] Step S500: Based on the self-organizing adaptation strategy, calculate the adaptation coefficient of the service quality assurance requirement distribution. When the adaptation coefficient meets the preset adaptation threshold standard, use the self-organizing adaptation strategy as the target multi-standard link adaptation scheme for the target spacecraft.

[0036] The adaptation coefficient can be a quantitative indicator measuring the degree of matching between the self-organizing adaptation strategy and the distribution of service quality assurance requirements. It can be used as a basis for determining whether to enable a new strategy, preventing ineffective handover. In a specific embodiment, the adaptation coefficient can be generated through similarity calculation, cost function evaluation, or satisfaction scoring. The preset adaptation threshold standard can be a threshold condition for determining whether the adaptation coefficient is sufficient to trigger a strategy update. It can be used to ensure that link switching is only performed when there is a significant benefit, ensuring business continuity. Furthermore, the preset adaptation threshold standard can be set by the system designer based on fault tolerance and handover overhead. The target multi-standard link adaptation scheme can be a satellite-ground heterogeneous link resource configuration scheme that is finally adopted after threshold verification. It can be used to guide the configuration of operating parameters and handover execution of actual communication links.

[0037] The adaptation coefficient, calculated based on the self-organizing adaptation strategy to determine the distribution of QoS requirements, can be used to evaluate the degree to which the strategy meets the QoS requirements of various services and to give a comprehensive score. For example, this operation can be implemented through weighted satisfaction scoring, constraint violation counting, and utility function integration, thereby quantifying the effectiveness of the strategy and providing a basis for switching decisions. Determining whether the adaptation coefficient meets a preset adaptation threshold standard can be achieved by comparing the adaptation coefficient with the threshold to decide whether to adopt the new strategy. In an exemplary embodiment, this operation can be implemented through hard threshold comparison, fuzzy membership judgment, and confidence interval testing, thereby avoiding low-return or high-risk switching and ensuring service continuity. Using the self-organizing adaptation strategy as a target multi-standard link adaptation scheme for the target spacecraft allows the strategy to be issued to the communication control system for execution after the adaptation coefficient meets the standard, thus completing a closed loop from decision-making to execution and achieving dynamic link optimization.

[0038] Taking the high-definition image backhaul from a low-orbit remote sensing satellite as an example, the spacecraft multi-mode wireless transmission link adaptation method in this embodiment can be as follows: When a low-orbit remote sensing satellite is passing through a cloud region, its multi-beam intelligent phased array terminal continuously collects the atmospheric attenuation parameters of the laser link and the signal-to-noise ratio of the radio frequency link, and simultaneously obtains the bandwidth and latency requirements of the high-definition image service to be transmitted; the system constructs a dynamic transmission model to calculate that the probability of laser link interruption due to clouds and fog increases sharply, while the radio frequency link capacity is low but stable; after the adaptation scheduling weight is decomposed, the steady-state component shows that the radio frequency link is more reliable in the long term, while the jitter waveform reveals that the laser link may recover after a few seconds; joint analysis predicts that the laser link will recover after a brief interruption, and a self-organizing strategy generates a scheme of temporarily cutting radio frequency as a backup and reserving a laser reconnection window; after the adaptation coefficient is evaluated, the strategy meets the minimum bandwidth threshold of the image service, and the system enables the scheme to achieve seamless switching and efficient backhaul.

[0039] In one embodiment, based on transmission capacity and outage probability, the adaptive scheduling weights for satellite-to-ground laser and radio frequency heterogeneous links are output, including: The transmission capacity and outage probability are normalized and weighted to obtain the total link quality score.

[0040] Normalization weighting can be a mathematical processing method that transforms indicators with different dimensions or value ranges to a unified scale and then performs weighted fusion. It can be used to eliminate evaluation biases caused by differences in units or orders of magnitude between transmission capacity and outage probability, achieving fair quantification of overall performance. In this embodiment, normalization weighting can map the original indicators to the [0, 1] interval through linear scaling, Z-score normalization, or minimum-maximum normalization, and then sum them according to preset weight coefficients. Furthermore, the total link quality score can be a single quantitative indicator representing the overall performance of heterogeneous links, generated by the weighted fusion of normalized transmission capacity and outage probability. It can be used to provide a unified evaluation benchmark for link scheduling, taking into account both bandwidth potential and reliability. In an exemplary embodiment, the total link quality score is obtained by taking a positive contribution to the normalized capacity and a negative contribution to the normalized outage probability (e.g., 1 minus the normalized value), and then assigning weights according to service sensitivity. Normalizing and weighting the transmission capacity and outage probability to obtain the total link quality score can be achieved by normalizing the transmission capacity and outage probability separately and then linearly combining them according to preset weights to generate a comprehensive score. For example, this operation can be achieved by weighted averaging after min-max normalization and fusion after nonlinear mapping of utility functions, thereby achieving unified quantification of bandwidth and reliability and avoiding decision bias based on a single indicator.

[0041] Define candidate link transmission time slots for satellite-to-ground laser and radio frequency heterogeneous links.

[0042] The candidate link transmission time slot can be a pre-divided set of potential time windows available for laser or radio frequency link communication on the time axis. This can be used to introduce time-dimensional scheduling granularity, supporting dynamic beam switching and resource reservation based on multi-beam phased array terminals. In one specific embodiment, the candidate link transmission time slot can be pre-divided based on orbit prediction, visible arc calculation, or service arrival time windows. Furthermore, the candidate link transmission time slot can include, but is not limited to, one or more of the following: primary communication time slot, redundant backup time slot, and detection calibration time slot. Defining the candidate link transmission time slots for satellite-to-ground laser and radio frequency heterogeneous links can be based on satellite-to-ground visibility prediction and service scheduling plans, dividing several discrete communication windows in the time domain. For example, this operation can be achieved by calculating continuous visible arcs based on orbital mechanics models and equally dividing the time slots, and dynamically generating non-uniform time slots based on service arrival times. This allows the scheduling granularity to be refined from the link level to a spatiotemporal joint dimension, improving resource planning flexibility.

[0043] Calculate the link priority weight of the candidate link transmission time slot.

[0044] The link priority weight can be a scheduling priority value assigned to each candidate link transmission time slot based on service QoS requirements. It can be used to reflect the adaptation value of different time slots for specific services, guiding resources towards high-value periods. In this embodiment, the link priority weight can score or map time slots based on the sensitivity of service type to latency and bandwidth. Furthermore, the link priority weight can be jointly used with the total link quality score in the adaptation scheduling weight calculation; the former reflects the matching degree of service requirements, and the latter reflects the channel physical performance. Calculating the link priority weight of candidate link transmission time slots can be done by assigning priority values ​​based on the QoS requirements of the services carried within each time slot (such as deadlines and bandwidth requirements). In an exemplary embodiment, this operation can be achieved through weighting based on the reciprocal of the service deadline and dynamic scoring based on the bandwidth gap ratio, thereby enabling scheduling decisions to consider both channel status and service timeliness, improving service satisfaction.

[0045] Based on the total link quality score and link priority weight, the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links is calculated.

[0046] Based on the total link quality score and link priority weights, the adaptive scheduling weights for satellite-to-ground laser and radio frequency heterogeneous links are calculated. This can be achieved by multiplying or weighting the total link quality score with the link priority weights of the corresponding time slots to generate the final scheduling weights. Furthermore, this operation can generate link-level weights by multiplying and summing them in each time slot, constructing a two-dimensional weight matrix for time slot-link joint scheduling. This enables coordinated optimization of channel performance and service requirements in the spatiotemporal dimension, providing structured input for subsequent component decomposition.

[0047] For example, in a scenario of sudden high load in low-Earth orbit satellite video live streaming services, the spacecraft multi-standard wireless transmission link adaptation method of this embodiment can be as follows: A low-Earth orbit satellite performs a high-definition video live streaming mission for a ground emergency event. The system collects data in real time on the capacity fluctuations of the laser link due to atmospheric disturbances and the stability of the radio frequency link but limited bandwidth. First, the transmission capacity and interruption probability of the two links are normalized and weighted to obtain a higher overall quality score for the laser link but with large fluctuations, and a medium but stable score for the radio frequency link. Then, every 100 milliseconds within the next 5 seconds is defined as a candidate link transmission time slot. Based on the high sensitivity of video services to continuity and latency, time slots closer to the current moment are assigned higher link priority weights. Finally, the total link quality score is multiplied by the priority weights of each time slot to generate a fine-grained adaptation scheduling weight down to the time slot. This weight sequence is subsequently decomposed into a slow-varying steady-state component (reflecting the overall advantages of the laser link) and a fast-varying transient component (capturing sudden fading), supporting a self-organizing strategy to maximize the use of the high bandwidth of the laser while ensuring the continuity of the live stream.

[0048] In one embodiment, the adaptation scheduling weights for satellite-to-ground laser and radio frequency heterogeneous links are calculated based on the total link quality score and link priority weights, including: Analyze the channel fading statistics and service delay sensitivity coefficient of satellite-to-ground laser and radio frequency heterogeneous links; Based on channel fading statistical characteristics, service delay sensitivity coefficient, total link quality score, and link priority weight, the adaptation scheduling weight of satellite-to-ground laser and radio frequency heterogeneous links is calculated using the following formula: ; in, This represents the adaptation scheduling weight of the k-th candidate heterogeneous link. This represents the link priority weight coefficient. This represents the priority score of the k-th candidate link. This represents the link quality normalization coefficient. This represents the transmission capacity weighting factor. This represents the normalized transmission capacity of the k-th candidate link. This represents the interruption probability penalty factor. This represents the normalized interruption probability of the k-th candidate link. This represents the business latency weighting coefficient. This represents the normalized score of the k-th candidate link that meets the service latency requirements.

[0049] The channel fading statistical features can be a set of statistical parameters describing the signal strength fluctuation patterns of satellite-to-ground laser and radio frequency links over long time scales. These features can reflect the long-term stability and predictability of the channel, thereby adjusting the tolerance of scheduling weights to sudden fading. In this embodiment, the channel fading statistical features can be fitted to a fading distribution model based on historical channel observation data or features such as variance and correlation time can be extracted. For example, channel fading statistical features may include, but are not limited to, amplitude fluctuation standard deviation, fading duration, fading depth probability, and channel coherence time. The service delay sensitivity coefficient can be a normalized index quantifying the tolerance of a specific service to transmission delay. It can be used to distinguish between high real-time services and low-sensitivity services, thereby guiding resource priority allocation. In an exemplary embodiment, the service delay sensitivity coefficient can be obtained by mapping the service type to a preset QoS template, or dynamically calculated based on the end-to-end delay budget. The link priority weight coefficient can be a configurable parameter used to adjust the influence of the link priority score in the final adaptive scheduling weights. It can be used to balance the weight relationship between service scheduling intent and physical link performance. In a specific embodiment, the link priority weight coefficient can be dynamically set by the system policy engine based on task priority or network load status.

[0050] The priority score of the k-th candidate link can be a quantified priority value obtained by comprehensively considering service and time factors within the current scheduling period. This score reflects the link's scheduling value in terms of task urgency and time slot availability. Furthermore, the priority score of the k-th candidate link can serve as a specific numerical input for the link priority weight, participating in the calculation of the adaptation scheduling weight formula. The link quality normalization coefficient can be a scaling factor used to unify the dimensions of different link quality indicators, ensuring comparability of transmission capacity and outage probability during weighted fusion. For example, the link quality normalization coefficient can be determined through maximum value normalization, root mean square normalization, or by referencing baseline link performance. The transmission capacity weight factor can be a relative importance coefficient assigned to the normalized transmission capacity in the adaptation scheduling weight calculation, used to adjust the system's preference for high-bandwidth links. In a specific embodiment, the transmission capacity weight factor can be dynamically adjusted according to the current service bandwidth requirements, taking a larger value under high-throughput tasks. The normalized transmission capacity of the k-th candidate link can be a dimensionless value obtained by normalizing the maximum effective rate currently supported by the k-th link. It can be used to characterize the link's bandwidth potential and serve as one of the basic inputs for scheduling decisions. Furthermore, the normalized transmission capacity of the k-th candidate link can be multiplied by the transmission capacity weighting factor to jointly determine the contribution of the bandwidth dimension to the total weight.

[0051] The interruption probability penalty factor can be an adjustment coefficient used to amplify the negative impact of high interruption risk on scheduling weights, and can be used to strengthen reliability constraints and suppress the selection of unstable links. In an exemplary embodiment, the interruption probability penalty factor can increase with the improvement of service reliability requirements, and significantly improve in mission-critical scenarios. The normalized interruption probability of the k-th candidate link can be a normalized expression of the probability that the k-th link cannot currently meet QoS requirements, and can be used to quantify link failure risk and participate in reliability assessment. Furthermore, the normalized interruption probability of the k-th candidate link can be multiplied by the interruption probability penalty factor and then deducted or suppressed from the total weight. The service latency weight coefficient can be a proportional factor that adjusts the influence of latency satisfaction score in the adaptation scheduling weight, and can be used to highlight the resource guarantee priority of latency-sensitive services. For example, the service latency weight coefficient can be directly mapped from the service latency sensitivity coefficient or dynamically generated through scheduling policies. The normalized score of the k-th candidate link meeting the service latency requirements can be a quantitative score that measures whether the k-th link can meet the end-to-end latency constraints of the service under the current conditions, and can be used to transform higher-layer QoS requirements into physical layer scheduling basis. In one specific embodiment, the normalized score of the k-th candidate link that meets the service latency requirement can be obtained by normalizing the link propagation latency, queuing latency estimate and service latency budget.

[0052] Analyzing the channel fading statistical characteristics and service delay sensitivity coefficients of heterogeneous satellite-to-ground laser and radio frequency links can be achieved by extracting fading statistical parameters from historical channel data and resolving delay tolerance from service metadata. Furthermore, this operation can obtain channel fading statistical characteristics through offline modeling combined with online table lookup and online sliding window estimation of fading features; and obtain service delay sensitivity coefficients through QoS template matching and SLA contract-based delay constraint resolution, thus providing prior knowledge reflecting long-term channel behavior and service differentiation for the adaptive scheduling weight formula. Based on channel fading statistical characteristics, service delay sensitivity coefficients, total link quality score, and link priority weights, the adaptive scheduling weights for heterogeneous satellite-to-ground laser and radio frequency links can be calculated using a formula. This can be achieved by substituting multidimensional parameters into a composite formula containing four weighted terms: capacity, interruption, delay, and priority, and calculating the scheduling weights link by link. Further, this operation can be implemented using a linear combination formula with fixed weight coefficients or a nonlinear fusion function with adaptive weights, thereby achieving joint optimization of four-dimensional objectives: capacity, reliability, delay, and service priority, generating a high-dimensional interpretable scheduling basis.

[0053] Taking low-Earth orbit satellite communication scheduling under multi-service concurrency as an example, the spacecraft multi-standard wireless transmission link adaptation method in this embodiment can be that a certain low-Earth orbit satellite simultaneously carries scientific exploration data backhaul (low latency sensitivity, high reliability) and ground IoT sensor reporting (high latency tolerance, low bandwidth). The system first analyzes that the laser link has a small fading variance under clear sky conditions but has the risk of sudden interruption, while the radio frequency link has stable fading but limited capacity. Based on the service type, it is determined that the latency sensitivity coefficient of scientific data is high and the coefficient of IoT service is low. Then, the normalized transmission capacity, interruption probability and latency satisfaction score of each candidate link are calculated. In the weight formula, the scientific data task corresponds to a larger service latency weight coefficient and interruption probability penalty factor. Finally, the laser link obtains a higher adaptation scheduling weight due to its high capacity and latency satisfaction score, but its weight has an embedded suppression term for sudden interruption. This weight is subsequently decomposed into a slow variable component (reflecting the long-term advantages of laser) and a fast variable component (responding to instantaneous fading), supporting the system to make reasonable use of high bandwidth resources while ensuring critical tasks.

[0054] In one embodiment, a dynamic transmission model for the satellite-to-ground laser and radio frequency heterogeneous links is established, including: Establish a topological connection matrix between the multi-beam intelligent phased array terminal and satellites and ground stations in the space-ground coordinate system.

[0055] In this system, a satellite can be a spacecraft operating in Earth orbit, undertaking communication or remote sensing missions. It can serve as one end node of a satellite-to-ground link, participating in the construction of heterogeneous communication links. In an exemplary embodiment, the satellite can determine its real-time position in the space-ground coordinate system through orbital parameters and mission planning. A ground station can be a fixed or mobile station deployed on the ground for wireless communication with the spacecraft. It can serve as the other end node of the satellite-to-ground link, providing access points for radio frequency and laser links. Furthermore, the ground station can determine its position and coverage area in the space-ground coordinate system based on geographic coordinates and antenna pointing capabilities. The space-ground coordinate system can be a unified three-dimensional reference coordinate system describing the spatial positions of the satellite, ground station, and terminal. It can be used to provide a geometric benchmark for constructing the topology connection matrix and ensure the accuracy of link spatial relationship modeling. For example, the space-ground coordinate system can adopt a geocentric coordinate system or an inertial coordinate system, combined with timestamps to synchronize the positions of each node. The topology connection matrix can be a structured matrix representing the relationship between the multi-beam intelligent phased array terminal and the satellite and ground station in the space-ground coordinate system. It can be used to accurately characterize multi-beam pointing, link reachability, and spatial geometric constraints, supporting dynamic channel modeling. In one specific embodiment, the topology connection matrix can be generated as a binary or weighted matrix based on the coordinates of each node, beam pointing angle, and visibility. Furthermore, the topology connection matrix can include, but is not limited to, one or more of beam-level connection matrices, link-level connection matrices, and node-level connection matrices. Establishing the topology connection matrix in the space-ground coordinate system between the multi-beam intelligent phased array terminal and the satellite / ground station can be achieved by determining and constructing the matrix based on the real-time position and beam pointing capability of each entity in the space-ground coordinate system. Further, this operation can be implemented by determining the existence of connections based on visibility cones and generating a weighted connection matrix using beam gain thresholds, thereby accurately describing the spatial topology of heterogeneous links and providing a geometric basis for subsequent channel modeling.

[0056] Define the dynamic transmission parameters of the satellite-to-ground laser and radio frequency heterogeneous links, including the atmospheric attenuation parameters of the laser link and the rain attenuation parameters of the radio frequency link.

[0057] The dynamic transmission parameters can be a set of key physical parameters that change with time and environment and affect the performance of heterogeneous satellite-to-ground links. They can be used to reflect the differentiated impact of real atmospheric and meteorological conditions on links of different standards. In a specific embodiment, the dynamic transmission parameters may include, but are not limited to, one or more of the following: laser link atmospheric attenuation parameters, radio frequency link rain attenuation parameters, and ionospheric scintillation index. Laser link atmospheric attenuation parameters can be quantitative indicators describing the power loss of laser signals due to scattering, absorption, and other effects when traversing the atmosphere. They can be used to assess the availability and stability of the laser link under current meteorological conditions. For example, laser link atmospheric attenuation parameters can be estimated based on meteorological data, visibility, or real-time optical power monitoring. Radio frequency link rain attenuation parameters can be environment-dependent parameters characterizing the degree of attenuation caused by rainfall to radio frequency signal propagation. They can be used to reflect the performance degradation level of the radio frequency link under precipitation conditions. In an exemplary embodiment, radio frequency link rain attenuation parameters can be obtained through ITU-R model or measured calibration based on rainfall rate, frequency, and path length. Defining the dynamic transmission parameters of satellite-to-ground laser and radio frequency heterogeneous links can identify and formalize key environmental and channel variables that affect link performance. Furthermore, this operation can be achieved by injecting parameters into the meteorological database in real time and retrieving parameters through link detection, thereby enabling the model to respond to real atmospheric and meteorological disturbances.

[0058] Based on dynamic transmission parameters, dynamic transmission equations are constructed for the heterogeneous link between satellite-to-ground laser and radio frequency. These equations include transmission capacity calculation equations and outage probability calculation equations.

[0059] The dynamic transmission equation can be a set of mathematical expressions based on dynamic transmission parameters for calculating key performance indicators of heterogeneous links. It can be used to achieve calculable and predictable modeling of transmission capacity and outage probability. In one specific embodiment, the dynamic transmission equation can be used in conjunction with a topology connection matrix to map space to specific performance outputs. The transmission capacity calculation equation can be a functional relationship used to calculate the maximum effective throughput of the heterogeneous link based on the current channel state and transmission parameters. It can be used to quantify link bandwidth resources and support scheduling weight generation. For example, the transmission capacity calculation equation can be based on an extension of Shannon's formula, introducing atmospheric attenuation and rain attenuation correction terms. The outage probability calculation equation can be a probability model used to evaluate whether a link can meet QoS requirements under given dynamic transmission parameters. It can be used to measure link reliability and participate in adaptation decisions. In an exemplary embodiment, the outage probability calculation equation can be combined with a fading statistical model and service thresholds to construct a cumulative distribution function or tail probability expression. Constructing the dynamic transmission equation for satellite-to-ground laser and radio frequency heterogeneous links can involve substituting dynamic transmission parameters into physical or empirical models to form analytical or numerical expressions of capacity and outage probability. Furthermore, this operation can be achieved by constructing analytical equations based on ITU standard models and fitting nonlinear equations using data-driven methods, thereby enabling the quantification and computable expression of heterogeneous link performance indicators.

[0060] A dynamic transmission model for satellite-to-ground laser and radio frequency heterogeneous links is established based on the topology connection matrix and dynamic transmission equation.

[0061] Establishing a dynamic transmission model for heterogeneous satellite-to-ground laser and radio frequency links based on topological connectivity matrices and dynamic transmission equations can couple spatial and performance calculation equations to form an end-to-end link performance prediction model. Furthermore, this operation can be achieved through a matrix-equation joint solution framework and graph neural network embedding of topology and parameter joint inference, thereby addressing the problem of traditional modeling neglecting spatial topology and environmental dynamics and improving model fidelity.

[0062] Taking a low-Earth orbit (LEO) constellation traversing a region of heavy rainfall as an example, the spacecraft multi-mode wireless transmission link adaptation method in this embodiment can be as follows: A LEO satellite constellation encounters sudden heavy rainfall while flying over a tropical region. The ground station and the multi-beam intelligent phased array terminal update their positions in real time in the space-ground coordinate system. The system constructs a topology connection matrix to confirm the currently visible link combinations. At the same time, the atmospheric attenuation parameter of the laser link is slightly increased from the meteorological interface, while the rain attenuation parameter of the radio frequency (RF) link is significantly increased. Based on this, the dynamic transmission equation calculates that the capacity of the RF link drops sharply and the probability of interruption soars, while the laser link still maintains high capacity but faces the risk of cloud cover. Finally, the dynamic transmission model outputs a comprehensive performance profile of the heterogeneous links, providing high-precision input for subsequent adaptation and scheduling, and avoiding blind switching to the RF link that is severely affected by rain attenuation.

[0063] In one embodiment, the adaptive scheduling weights are decomposed into slow-varying channel steady-state components and fast-varying fading transient components, including: The adapted scheduling weights are preprocessed by denoising to obtain the preprocessed adapted scheduling weights.

[0064] The denoising preprocessing can be a signal conditioning operation that suppresses noise in the adapted scheduling weight signal. This can be used to eliminate interference from measurement errors, quantization noise, or abnormal disturbances on component extraction, improving the accuracy of subsequent wavelet decomposition. In this embodiment, the denoising preprocessing can employ filtering, smoothing, or statistical elimination methods to purify the original scheduling weight sequence. Furthermore, the preprocessed adapted scheduling weight can be the denoised adapted scheduling weight signal, which can be used as input to the wavelet transform, ensuring that the frequency domain decomposition result reflects the true channel characteristics rather than noise. For example, the preprocessed adapted scheduling weight can be generated from the original adapted scheduling weight through denoising preprocessing. Denoising the adapted scheduling weight to obtain the preprocessed adapted scheduling weight can be achieved by applying a denoising algorithm to the original adapted scheduling weight time series, outputting a purified signal. Further, this operation can be implemented through sliding median filtering, wavelet thresholding, and Kalman smoothing filtering, thereby suppressing spurious fluctuations caused by non-channel factors and improving the reliability of component decomposition.

[0065] Wavelet transform is applied to the preprocessed adaptive scheduling weights to obtain the transformed adaptive scheduling weights.

[0066] Wavelet transform, a time-frequency localization analysis method, decomposes a signal into components of different scales (frequency) and locations (time). It can be used to achieve multi-resolution decomposition of scheduling weight signals, separating slow-changing trends from fast-changing fluctuations. In an exemplary embodiment, wavelet transform can be performed on the preprocessed adapted scheduling weights using discrete or continuous wavelet transforms by selecting wavelet basis functions. Furthermore, the transformed adapted scheduling weights can be a set of adapted scheduling weight coefficients represented in the time-frequency domain after wavelet transform, which can be used to provide a distinguishable frequency band structure, facilitating the extraction of channel variation characteristics at different time scales. In a specific embodiment, the transformed adapted scheduling weights can be obtained by applying wavelet transform to the preprocessed adapted scheduling weights. Performing wavelet transform on the preprocessed adapted scheduling weights to obtain the transformed adapted scheduling weights can be achieved by selecting a suitable wavelet basis to perform multi-scale decomposition on the preprocessed signal and outputting coefficients at each scale. Furthermore, this operation can be implemented using Daubechies wavelet for discrete wavelet transform or Morlet wavelet for continuous wavelet transform, thereby achieving time-frequency localization characterization and providing a structural basis for frequency band separation.

[0067] Extract the slow and fast components of the transformation adaptation scheduling weights.

[0068] The slow-varying component can be the wavelet coefficient part corresponding to the low-frequency scale in the transform-adaptive scheduling weights, which can be used to characterize the long-term average state of the channel and reconstruct the steady-state scheduling benchmark. In this embodiment, the slow-varying component can be obtained by selecting the approximation coefficients (low-frequency subbands) in the wavelet transform result. For example, the fast-varying component can be the wavelet coefficient part corresponding to the high-frequency scale in the transform-adaptive scheduling weights, which can be used to characterize the short-term drastic fluctuations of the channel and reconstruct transient jitter characteristics. Further, the fast-varying component can be obtained by selecting the detail coefficients (high-frequency subbands) in the wavelet transform result. Extracting the slow-varying and fast-varying components of the transform-adaptive scheduling weights can be done by dividing according to the frequency scale and selecting the low-frequency approximation coefficients and high-frequency detail coefficients respectively. Further, this operation can be achieved by dividing the high and low frequency subbands according to a preset scale threshold and automatically selecting the dominant component based on energy concentration, thereby decoupling the trend and disturbance components and supporting differentiated processing.

[0069] The slow-varying components and the fast-varying components are reconstructed separately to obtain the slow-varying channel steady-state component and the fast-varying fading transient component.

[0070] Reconstruction can be the process of inversely transforming the component coefficients in the wavelet domain back to the time domain signal. This can be used to generate time-domain steady-state and transient components with clear physical meaning for subsequent analysis. In a specific embodiment, reconstruction can be based on inverse wavelet transform, independently reconstructing the slow-changing and fast-changing components respectively. Reconstructing the slow-changing and fast-changing components separately yields the slow-changing channel steady-state component and the fast-changing fading transient component. This can be achieved by performing inverse wavelet transforms on the two types of wavelet coefficients respectively, restoring them to the time domain signal. Furthermore, this operation can be achieved by fully reconstructing while retaining all coefficients, or partially reconstructing using only the dominant coefficients to reduce computational overhead. This allows for the generation of steady-state baselines and jitter waveforms that can be used for scheduling decisions, maintaining time-domain interpretability.

[0071] For example, in the scenario of low-Earth orbit satellite communication traversing ionospheric disturbance zones, the spacecraft multi-mode wireless transmission link adaptation method of this embodiment can be as follows: When a low-Earth orbit satellite flies over the equatorial anomaly zone, its adaptation scheduling weights experience high-frequency jitter due to ionospheric scintillation. The system first performs median filtering to denoise the weight sequence and eliminates the instantaneous anomalies of the sensors. Then, it uses Db4 wavelets to perform three-level decomposition, treating the low-frequency approximation coefficients as slowly varying components and the high-frequency detail coefficients as rapidly varying components. After reconstruction, the slowly varying channel steady-state component shows that the radio frequency link is better in the long run, while the rapidly varying fading transient component (i.e., the link jitter waveform) shows periodic spikes, indicating that the laser link is about to be briefly interrupted. This decomposition result enables the self-organizing strategy to avoid immediate switching due to instantaneous spikes, but instead maintains the current configuration and activates the early warning mechanism, adjusting only after the jitter continues to exceed the threshold, thereby reducing the number of invalid switching and ensuring continuous telemetry data transmission.

[0072] In one embodiment, the link jitter waveform of the fast-fading transient component is defined, including: Identify the spectral peaks of the rapidly fading transient components to calculate the total energy of the rapidly fading transient components.

[0073] The spectral peak can be a local maximum point of energy concentration in the frequency domain of the rapidly fading transient component. It can be used to identify the main frequency components of rapid channel fluctuations and assist in quantifying the disturbance intensity. In this embodiment, the spectral peak can be obtained by detecting local maxima after performing a Fourier transform or short-time Fourier transform on the rapidly fading transient component. The total energy of the rapidly fading component can be the energy integral value of the rapidly fading transient component over the entire time or frequency domain. It can be used to characterize the overall intensity of short-term channel fluctuations and constrain the energy scale of the final jitter waveform. For example, the total energy of the rapidly fading component can be obtained by integrating the square of the transient component or summing the spectral energy. Identifying the spectral peak of the rapidly fading transient component can be achieved by locating the extreme points of the energy concentration region after performing a frequency domain transform on the rapidly fading transient component. Further, this operation can be achieved by identifying local maxima using a peak detection algorithm and tracing the dominant frequency component using wavelet ridges, thereby extracting the dominant fluctuation frequency and providing a basis for energy distribution analysis. Calculating the total energy of the rapidly fading transient component can be achieved by integrating the energy of the rapidly fading transient component in the time or frequency domain. In one exemplary embodiment, this operation can be achieved by time-domain squared integration and frequency-domain energy summation under the Parseval theorem, thereby quantifying the overall intensity of short-term fluctuations for subsequent waveform energy constraints.

[0074] Construct the basic jitter waveform for the fast-fading transient components.

[0075] The basic jitter waveform can be an initial waveform representation that reflects only the basic shape of the rapidly fading transient components without added noise, and can be used as the skeleton structure for constructing a high-fidelity link jitter waveform. In a specific embodiment, the basic jitter waveform can be generated by normalizing and smoothing the time series based on the rapidly fading transient components. Constructing the basic jitter waveform of the rapidly fading transient components can be achieved by converting the rapidly fading transient components into a standardized time waveform expression. Furthermore, this operation can be achieved by directly normalizing the original transient sequence and reconstructing the main fluctuation modes through principal component extraction, thereby forming a noise-free reference waveform structure.

[0076] The non-stationary fading effect and natural attenuation characteristics of the fast-varying transient component are analyzed to increase the channel fading noise of the basic jitter waveform, thus obtaining the noisy fast-varying fading component.

[0077] Non-stationary fading effects can be a phenomenon where the statistical characteristics of channel fading are not constant in the time dimension. This can be used to reveal the source of time-varying characteristics in rapidly changing components and guide the dynamics of noise modeling. For example, non-stationary fading effects can include, but are not limited to, scintillation caused by atmospheric turbulence, time-frequency coupling caused by Doppler spread, and step fading caused by sudden blockage. Natural attenuation characteristics can be the physical law of channel disturbances naturally weakening over time, and can be used to determine the time-varying envelope and duration of channel fading noise. In this embodiment, natural attenuation characteristics can be obtained by fitting measured data or deducing from an atmospheric propagation model. Channel fading noise can be a synthetic noise signal simulating random disturbance components in a real channel, and can be used to enhance the realism of the basic jitter waveform, making it contain random fluctuations that conform to physical laws. Furthermore, channel fading noise can be obtained by generating colored noise with time-varying variance based on non-stationary fading effects and natural attenuation characteristics. Noisy rapidly changing fading components can be the basic jitter waveform superimposed with channel fading noise, and can be used to more accurately reflect the complex dynamic characteristics of rapidly changing fading in actual links. In one specific embodiment, the noisy fast-changing fading component is used as an intermediate product for energy calibration and morphology determination of the final link jitter waveform.

[0078] Analyzing the non-stationary fading effect and natural attenuation characteristics of rapidly fading transient components can be achieved by modeling the time-varying statistical properties and attenuation rate of the transient components. Furthermore, this operation can be implemented by using time-frequency analysis methods to identify non-stationarity and fitting exponential or power-law attenuation curves to describe natural attenuation, thus providing a physical basis for the generation of channel fading noise. Adding channel fading noise to the basic jitter waveform to obtain noisy rapidly fading components can be achieved by superimposing synthetic noise that conforms to non-stationarity and attenuation characteristics onto the basic jitter waveform. In an exemplary embodiment, this operation can be achieved by generating a superposition of time-varying variance Gaussian processes and injecting a random process based on Markov modulation, thereby improving the waveform's fit to real channel disturbances.

[0079] Based on the natural decay characteristics and the total energy of the fast-changing components, the link jitter waveform of the fast-fading transient components is determined.

[0080] Based on the natural decay characteristics and the total energy of the fast-decaying components, the link jitter waveform of the fast-decaying transient components is determined. This can be achieved by taking the noisy fast-decaying components as a base, normalizing them according to the total energy, and matching them with the natural decay envelope. Furthermore, this operation can be achieved by matching the decay curve after energy scaling and minimizing the energy deviation from the original transient components through an optimization algorithm, thereby outputting a final jitter waveform that combines morphological realism and energy consistency.

[0081] For example, in the scenario where a low-orbit satellite passes through an ionospheric disturbance region, the spacecraft multi-mode wireless transmission link adaptation method in this embodiment can be as follows: When the target spacecraft flies through an ionospheric scintillation active region, the fast-changing fading transient component exhibits high-frequency and violent fluctuations; the system first identifies that its spectral peak is located near 2Hz and calculates the total energy as 0.85 units; then it constructs a basic jitter waveform and analyzes that there is a strong non-stationary fading effect and a natural decay time constant of about 3 seconds in this region; based on this, it generates channel fading noise with time-varying variance and exponential decay, which is superimposed to form a noisy fast-changing fading component; finally, based on the total energy of 0.85 and the 3-second decay characteristic, it calibrates the waveform amplitude and duration and outputs a high-fidelity link jitter waveform; this waveform is used for joint steady-state weight analysis to predict in advance that the laser link will experience two deep fadings within 5 seconds, thereby triggering the radio frequency link pre-activation mechanism to avoid service interruption.

[0082] In one embodiment, the potential variation characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous links are analyzed by combining steady-state scheduling weights and link jitter waveforms, including: The steady-state scheduling weight and the link jitter waveform are synchronized in time to obtain the synchronized steady-state scheduling weight and the synchronized link jitter waveform.

[0083] Time synchronization can be a process of aligning steady-state scheduling weights and link jitter waveforms to a unified time reference. This can eliminate timing discrepancies caused by acquisition or processing delays between the two types of signals, ensuring the accuracy of subsequent fusion analysis. In this embodiment, time synchronization can be achieved through timestamp alignment, interpolation resampling, or buffer synchronization mechanisms. The synchronized steady-state scheduling weights can be a sequence of steady-state scheduling weights after time synchronization processing, which can be used as the low-frequency stable component input of the composite link state signal. Furthermore, the synchronized steady-state scheduling weights and the synchronized link jitter waveform can be superimposed on the same time axis to generate a composite signal. The synchronized link jitter waveform can be a sequence of link jitter waveforms after time synchronization processing, which can be used as the high-frequency disturbance component input of the composite link state signal. For example, the synchronized link jitter waveform and the synchronized steady-state scheduling weights can together constitute a composite link state signal. Synchronizing the steady-state scheduling weights and the link jitter waveforms to obtain synchronized steady-state scheduling weights and synchronized link jitter waveforms can be an alignment process of two heterogeneous signal sequences based on a common time reference. Furthermore, this operation can be achieved by interpolating after hardware timestamp alignment, using dynamic matching with a FIFO buffer, and jointly estimating the time offset using Kalman filtering, thereby ensuring that the steady-state and transient components strictly correspond in time and avoiding fusion distortion.

[0084] Generate a composite link status signal consisting of synchronous steady-state scheduling weights and synchronous link jitter waveforms.

[0085] The composite link state signal can be a unified representation signal formed by fusing the synchronous steady-state scheduling weights and the synchronous link jitter waveform, which can be used to fully reflect the comprehensive state evolution of heterogeneous links in the time domain. In an exemplary embodiment, the composite link state signal can be synthesized by weighted superposition, splicing, or modulation of two types of synchronization signals. Furthermore, the composite link state signal can be one or more of the following, including but not limited to linear superposition signals, amplitude-modulated signals, and feature splicing vectors. Generating the composite link state signal of synchronous steady-state scheduling weights and synchronous link jitter waveforms can be achieved by merging the two types of synchronization signals into a single signal expression according to preset rules. Furthermore, this operation can be achieved by directly adding them to form a synthesized waveform, using the steady-state weights as carriers to amplitude-modulate the jitter waveform, and constructing a dual-channel feature vector, thereby constructing a unified state representation that includes long-term trends and short-term fluctuations.

[0086] Analyze the time spectrum and steady-state coefficient of the transmission load of the composite link state signal.

[0087] The time-frequency spectrum can represent the energy distribution of the composite link-state signal in the joint time and frequency domains, and can be used to reveal the evolution of different frequency components in the link state over time. In a specific embodiment, the time-frequency spectrum can be obtained by short-time Fourier transform, wavelet transform, or Wigner-Ville distribution. For example, the time-frequency spectrum can include, but is not limited to, STFT time-frequency spectrum, continuous wavelet time-frequency spectrum, Choi-Williams distribution spectrum, etc. The transmission load steady-state coefficient can be an index that quantifies the proportion of low-frequency stable components in the composite link-state signal, and can be used to characterize the long-term stability level of the current transmission load. Further, the transmission load steady-state coefficient can be calculated by integrating the low-frequency energy in the time-frequency spectrum or by normalizing the variance of the steady-state component. Analyzing the time-frequency spectrum and transmission load steady-state coefficient of the composite link-state signal can be achieved by performing time-frequency transform on the composite signal and extracting the low-frequency energy proportion index. Further, this operation can be achieved by using STFT to calculate the time-frequency spectrum and integrating the 0-1Hz frequency band to obtain the steady-state coefficient, and using wavelet packet decomposition to statistically approximate the energy proportion of the coefficients, thereby simultaneously obtaining the dynamic details and overall stability of the link state.

[0088] The rapidly varying energy difference of the time spectrum is calculated.

[0089] The rapidly varying energy difference can be the differential change in high-frequency energy in the time spectrum between adjacent time windows. It can be used to capture energy abrupt changes caused by sudden channel disturbances and identify potential degradation events. In this embodiment, the rapidly varying energy difference can be obtained by calculating the time derivative of the high-frequency subband energy or the energy difference between adjacent frames. Furthermore, the rapidly varying energy difference can include, but is not limited to, one or more of the following: first-order forward difference, center difference, and sliding window slope estimation. Calculating the rapidly varying energy difference in the time spectrum can be achieved by performing a difference operation on the energy sequence of the high-frequency region in the time spectrum. Furthermore, this operation can be achieved by performing inter-frame first-order difference on the subband energy above 5 GHz and calculating the local slope of the high-frequency energy envelope, thereby quantifying the severity of high-frequency disturbance changes and identifying sudden channel events.

[0090] Based on the rapidly varying energy difference and the steady-state coefficient of the transmission load, the potential variation characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link are determined.

[0091] Based on the rapidly varying energy difference and the steady-state coefficient of the transmission load, the potential variation characteristics of the transmission load in the satellite-to-ground laser and radio frequency heterogeneous links can be determined. This can be achieved by fusing high-frequency abrupt change indicators and low-frequency stability indicators to infer the future direction of load evolution. Furthermore, this operation can be realized by constructing a two-dimensional feature space for clustering to identify change patterns and inputting it into an LSTM network to predict the next window of load interval, thereby enabling accurate prediction of short-term trends in transmission load.

[0092] For example, in the scenario of low-Earth orbit satellite communication traversing ionospheric disturbance zones, the spacecraft multi-mode wireless transmission link adaptation method of this embodiment can be as follows: A low-Earth orbit satellite enters a high-latitude ionospheric scintillation region. Its steady-state scheduling weight shows that the long-term availability of the radio frequency link is still relatively high, but the link jitter waveform exhibits high-frequency oscillation. The system first performs time synchronization between the two to eliminate millisecond-level offsets caused by differences in the processing pipeline. Subsequently, a composite link status signal is generated and its STFT time spectrum is calculated. It is found that the energy in the 2-10Hz band increases sharply within 3 seconds. At the same time, the transmission load steady-state coefficient drops from 0.85 to 0.62. The fast-changing energy differential shows that the high-frequency energy slope is positive and exceeds the threshold. Based on this, the system determines that the radio frequency link is about to experience deep fading. Although it is not currently interrupted, the load stability has significantly decreased. Thus, the resource tilting strategy to the laser link is triggered in advance. Even if the capacity of the laser link is currently limited due to cloud cover, a switching window is reserved to avoid subsequent sudden interruptions.

[0093] In one embodiment, a self-organizing adaptation strategy for the satellite-to-ground laser and radio frequency heterogeneous links is generated based on the potential changes in transmission load, including: Construct a six-dimensional feature vector of potential changes in transmission load.

[0094] The six-dimensional feature vector is a numerical vector containing six independent dimensions, formed by structurally extracting the potential changes in transmission load. It can be used to uniformly characterize the coupling relationship between channel dynamics and service load, providing structured input for subsequent parameter adjustments. In this embodiment, the six-dimensional feature vector can be composed of six discriminative time-series or statistical indicators selected from the potential changes in transmission load. Further, the six-dimensional feature vector may include trend slope, fluctuation variance, mutation frequency, recovery rate, correlation coefficient, skewness index, etc. Constructing the six-dimensional feature vector of the potential changes in transmission load can be achieved by extracting six physically meaningful and non-redundant quantitative indicators from the potential changes in transmission load. For example, this operation can be implemented by selecting six fixed indicators based on expert rules, automatically reducing the dimensionality to six dimensions through principal component analysis, and using a feature selection algorithm to screen the optimal six-dimensional combination, thereby achieving a compact and computable representation of complex dynamic characteristics.

[0095] Normalize the six-dimensional eigenvectors to obtain normalized six-dimensional eigenvectors.

[0096] The normalized six-dimensional feature vector can be a six-dimensional feature vector that has undergone dimension unification, with each dimension's values ​​mapped to the same numerical range. This can be used to eliminate magnitude differences between different feature dimensions, improving the numerical stability and generalization ability of subsequent analysis models. In an exemplary embodiment, the normalized six-dimensional feature vector can be transformed from the original six-dimensional vector using min-max scaling, Z-score normalization, or vector unitization. Normalizing the six-dimensional feature vector to obtain the normalized six-dimensional feature vector can be achieved by scaling each component of the six-dimensional vector to a standard numerical range according to a unified rule. Furthermore, this operation can be implemented using min-max normalization to the 0-1 interval, z-score normalization to a mean of 0 and a variance of 1, or L2 norm unit vectorization, thereby improving the robustness and cross-scenario applicability of subsequent parameter analysis.

[0097] Based on the normalized six-dimensional feature vector, we analyze the laser link power adjustment, RF link beam pointing adjustment, and link switching threshold adjustment of the satellite-to-ground laser and RF heterogeneous links.

[0098] The laser link power adjustment can be a dynamic adjustment applied to the laser emission power to compensate for atmospheric attenuation or link loss. It can be used to maintain the laser link signal-to-noise ratio above the effective communication threshold, improving link availability. In one specific embodiment, the laser link power adjustment can be calculated based on the component reflecting the intensity of atmospheric disturbance in the normalized six-dimensional eigenvector. Furthermore, the laser link power adjustment can work in conjunction with the link switching threshold adjustment to avoid false switching due to insufficient power. The radio frequency link beam pointing adjustment can be a correction applied to the phased array beam direction to cope with Doppler frequency shift or terminal attitude changes caused by high-speed spacecraft motion. It can be used to maintain radio frequency link alignment accuracy and reduce the risk of signal loss. For example, the radio frequency link beam pointing adjustment can be driven by the component representing the spatial pointing deviation trend in the normalized six-dimensional eigenvector. Furthermore, the radio frequency link beam pointing adjustment can be coordinated with the multi-beam phased array terminal to perform beam redirection.

[0099] The link switching threshold adjustment amount can be a threshold offset for dynamically adjusting the triggering conditions of laser and RF link switching. It can be used to flexibly set the switching sensitivity based on the channel prediction state to prevent frequent oscillations or delayed responses. In an exemplary embodiment, the link switching threshold adjustment amount can be generated based on the component of the normalized six-dimensional feature vector related to the link stability evolution trend. Furthermore, the link switching threshold adjustment amount can be linked with the adaptation coefficient determination mechanism to jointly determine whether the strategy takes effect. Based on the normalized six-dimensional feature vector, analyzing the laser link power adjustment amount, RF link beam pointing adjustment amount, and link switching threshold adjustment amount of the satellite-to-ground laser and RF heterogeneous links can be achieved by mapping the normalized six-dimensional feature vector to the adjustment amounts of three types of physical control parameters. Furthermore, this operation can be implemented by using a preset mapping function lookup table, using lightweight neural network regression prediction, and using a fuzzy inference system to generate adjustment instructions, thereby achieving a precise conversion from abstract features to specific system actions.

[0100] By combining the laser link power adjustment, the radio frequency link beam pointing adjustment, and the link switching threshold adjustment, a self-organizing adaptation strategy for satellite-to-ground laser and radio frequency heterogeneous links is generated.

[0101] By combining laser link power adjustment, RF link beam pointing adjustment, and link switching threshold adjustment, a self-organizing adaptation strategy for heterogeneous satellite-to-ground laser and RF links can be generated. This can be achieved by integrating the three types of adjustment into a unified set of link control commands. For example, this operation can be packaged into parameter configuration commands and issued to the laser / RF modules, generating state machine transition rules, and constructing solution vectors for a multi-objective optimization problem. This results in an executable, collaborative optimization strategy covering both the physical and link layers.

[0102] Taking a low-Earth orbit satellite crossing an ionospheric disturbance region as an example, the spacecraft multi-mode wireless transmission link adaptation method in this embodiment can be as follows: When a low-Earth orbit satellite enters a high-latitude ionospheric scintillation region, its potential transmission load change characteristics show that the radio frequency link signal-to-noise ratio exhibits periodic deep fading, while the laser link is relatively stable due to clear sky conditions; the system constructs a six-dimensional feature vector, including indicators such as radio frequency fluctuation variance and laser trend slope; after normalization, it is input into the analysis module, and outputs a slight increase in laser link power to reserve margin, a high-frequency fine adjustment of radio frequency beam pointing to track rapid offset, and a temporary relaxation of link switching threshold to tolerate short-term jitter; the three are fused to generate a self-organizing adaptation strategy, which is activated after the adaptation coefficient is verified to meet the QoS requirements for remote sensing data backhaul, successfully avoiding link interruption caused by ionospheric scintillation.

[0103] In one embodiment, the distribution of service quality assurance requirements for multiple service standards of the target spacecraft is analyzed, including: Collect multi-standard service quality requirements data for target spacecraft.

[0104] The multi-standard service quality of service (QoS) requirement data of the target spacecraft can be a set of specific QoS indicator requirements for various communication services currently operating on the target spacecraft. This data can serve as the basic input for constructing a QoS requirement distribution, reflecting the actual demands of different services on dimensions such as bandwidth, latency, and reliability. In this embodiment, the multi-standard service QoS requirement data of the target spacecraft can be read in real-time from the service scheduling module or mission management system, using the QoS template parameters of each service flow. Collecting the multi-standard service QoS requirement data of the target spacecraft can be achieved by obtaining the QoS parameters of currently active service flows from the mission management system or communication scheduling interface. Furthermore, the collection of the multi-standard service QoS requirement data of the target spacecraft can be achieved through periodic polling, event-driven reporting, or real-time stream subscription based on API, thereby providing raw input for subsequent structured modeling.

[0105] A two-dimensional matrix of multi-standard service quality requirements data is constructed to calculate the normalized QoS index of the service queue corresponding to the target spacecraft.

[0106] The two-dimensional matrix of multi-standard service quality of service (QoS) requirements data can be a structured matrix organized according to service queues and QoS dimensions. This facilitates unified quantification and normalization calculations for heterogeneous services. In an exemplary embodiment, the two-dimensional matrix of multi-standard service QoS requirements data can be constructed with rows representing service queues and columns representing QoS indicators (such as bandwidth, latency, and packet loss rate). Constructing the two-dimensional matrix of multi-standard service QoS requirements data can be achieved by arranging the collected QoS requirements into a matrix with service queues as rows and QoS dimensions as columns. For example, constructing the two-dimensional matrix of multi-standard service QoS requirements data can employ sparse matrix storage to save on-board memory and dense matrix storage for fast linear algebra operations, thereby enabling a structured expression of heterogeneous service requirements and facilitating batch processing.

[0107] Define the target spacecraft's operational type region.

[0108] The target spacecraft's business type region can be a set of categories formed by logically grouping business queues based on business semantics or functional attributes. This can be used to achieve structured classification and management of businesses, supporting the aggregation of regional resource requirements. Furthermore, the target spacecraft's business type region can be divided according to business semantics, including one or more categories such as remote sensing imaging, real-time video, scientific exploration, and IoT access. Defining the target spacecraft's business type region can be achieved by dividing business queues into several semantic categories based on preset rules or business metadata. In a specific embodiment, defining the target spacecraft's business type region can be achieved through classification based on business ID prefixes, matching based on task type labels, and clustering based on machine learning, thereby enabling logical clustering of businesses and supporting the aggregation of regional resource requirements.

[0109] Based on the normalized QoS metric, the demand percentage for the service type region is calculated using the following formula: ; in, This represents the demand percentage for the s-th business type in the region. This represents the s-th business type region. This represents the index of the business queue in the s-th business type region. This indicates that the s-th business type region is located in Normalized QoS metrics for the service queue This indicates that the s-th business type region is located in The weight coefficient of the business queue, This represents the sum of QoS metrics for all service queues; The normalized QoS index of the service queue corresponding to the target spacecraft can be obtained by converting the original QoS index into a dimensionless, comparable, and unified-scale value through normalization processing. This can be used to eliminate the differences in dimensions and orders of magnitude between different types of QoS indices and support cross-service priority evaluation. In this embodiment, the normalized QoS index of the service queue corresponding to the target spacecraft can adopt a minimum-maximum scaling, Z-score normalization, or a proportional mapping method based on reference values. The s-th service type region can be a specific category instance within a service type region and can be used as the basic unit for calculating the demand proportion. For example, the s-th service type region can contain several service queues, and its demand proportion is determined by the normalized QoS index and weight of the internal queues. The index of the service queue in the s-th service type region can be a location number identifying a specific service queue within the s-th service type region. This index can be used to traverse and locate each service queue within the region and participate in the demand proportion summation calculation.

[0110] The normalized QoS index of the service queue located at the index in the s-th service type region can be the normalized comprehensive QoS score of the service queue at the specified index. This score can reflect the service quality demand intensity of the queue under a unified scale and participate in the calculation of the regional demand share. The weight coefficient of the service queue located at the index in the s-th service type region can be a coefficient representing the relative importance or priority of the service queue within its respective type region. This coefficient can be used to adjust the contribution of different queues to the total regional demand, reflecting differences in task criticality. In a specific embodiment, the weight coefficient of the service queue located at the index in the s-th service type region can be preset by task level, user contract SLA, or system policy. The sum of the QoS indices of all service queues can be the cumulative value of the product of the normalized QoS indices of all service queues in the entire system and their weights. This sum can be used as the normalization benchmark for calculating the demand share, ensuring that the sum of the regional shares is 1.

[0111] Calculating the demand share of service type regions based on normalized QoS metrics can be achieved by multiplying and summing the normalized QoS metrics of each queue within each service type region by their respective weights, and then dividing by the total value across the entire system. Furthermore, this calculation can be implemented through online incremental updates, offline batch processing, and distributed parallel summation, thereby quantifying the relative intensity of each service type's demand for link resources.

[0112] By determining the proportion of demand, we can identify the distribution of service quality assurance requirements for multiple service standards of the target spacecraft.

[0113] The service quality assurance (QoS) requirement distribution for multi-standard services is represented by a probability distribution or vector based on the demand proportion of each service type region. This distribution can serve as a key basis for generating self-organizing adaptation strategies and evaluating adaptation coefficients, reflecting the overall QoS assurance focus of the system. Determining the QoS requirement distribution for multi-standard services on a target spacecraft through demand proportions can be achieved by combining the demand proportions of each service type region into a vector or distribution function. For example, determining the QoS requirement distribution for multi-standard services on a target spacecraft through demand proportions can form a structured QoS requirement view oriented towards adaptation decisions, supporting precise resource allocation.

[0114] For example, in a scenario of multi-mission low-Earth orbit satellite on-orbit service, the spacecraft multi-standard wireless transmission link adaptation method in this embodiment can be that a low-Earth orbit satellite simultaneously performs three tasks: high-definition remote sensing imaging backhaul, space environment scientific data acquisition, and ground IoT terminal data aggregation. The system collects the QoS requirements of three types of services: remote sensing requires high bandwidth and low interruption, scientific data requires high reliability but tolerates latency, and IoT data packets require small data packets but low power consumption transmission. After constructing a two-dimensional matrix, the bandwidth, latency, and reliability indicators are normalized and synthesized into QoS scores for each queue. Three service type regions are defined: remote sensing region, scientific region, and IoT region. The weights of the remote sensing queue are set to 0.8, scientific queues to 0.6, and IoT queues to 0.3. The calculated demand proportions are 0.65 for the remote sensing region, 0.25 for the scientific region, and 0.10 for the IoT region. This distribution indicates that the system should prioritize ensuring the bandwidth of the remote sensing link and allocate it when the laser link is available. This demand distribution is then used for adaptation coefficient evaluation to ensure that the self-organizing strategy meets the principle of prioritizing high-value services.

[0115] In addition, refer to Figure 2 To achieve the above objectives, the present invention also provides a spacecraft multi-standard wireless transmission link adaptation system, the system comprising: Modeling and evaluation module 10 is used to collect the channel status parameters and service quality requirements of the star-ground link of the target spacecraft's multi-beam intelligent phased array terminal, establish a dynamic transmission model of the star-ground laser and radio frequency heterogeneous link, and calculate the transmission capacity and interruption probability of the heterogeneous link based on the channel status parameters and service quality requirements of the star-ground link using the dynamic transmission model. The weight decomposition module 20 is used to output the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links based on the transmission capacity and the interruption probability, and decompose the adaptation scheduling weight into a slow-varying channel steady-state component and a fast-varying fading transient component. The load analysis module 30 is used to determine the steady-state scheduling weight of the slow-changing channel steady-state component, define the link jitter waveform of the fast-changing fading transient component, and analyze the potential change characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link by combining the steady-state scheduling weight and the link jitter waveform. The strategy generation module 40 is used to generate a self-organizing adaptation strategy for the space-to-ground laser and radio frequency heterogeneous links based on the potential change characteristics of the transmission load, and to analyze the distribution of service quality assurance requirements for multi-standard services of the target spacecraft. The threshold verification module 50 is used to calculate the adaptation coefficient of the service quality assurance requirement distribution based on the self-organizing adaptation strategy. When the adaptation coefficient meets the preset adaptation threshold standard, the self-organizing adaptation strategy is used as the target multi-standard link adaptation scheme for the target spacecraft.

[0116] Other embodiments or specific implementations of the spacecraft multi-standard wireless transmission link adaptation system described in this invention can be referred to the above-described method embodiments, and will not be repeated here.

[0117] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A spacecraft multi-standard wireless transmission link adaptation method, characterized by, The method includes: Collect the channel status parameters and service quality requirements of the star-to-ground link of the target spacecraft's multi-beam intelligent phased array terminal, establish a dynamic transmission model of the star-to-ground laser and radio frequency heterogeneous link, and calculate the transmission capacity and interruption probability of the heterogeneous link based on the channel status parameters and service quality requirements of the star-to-ground link using the dynamic transmission model. Based on the transmission capacity and interruption probability, the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous link is output, and the adaptation scheduling weight is decomposed into a slow-varying channel steady-state component and a fast-varying fading transient component. Determine the steady-state scheduling weight of the slow-varying channel steady-state component, define the link jitter waveform of the fast-varying fading transient component, and combine the steady-state scheduling weight and the link jitter waveform to analyze the potential variation characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link. Based on the potential changes in the transmission load, a self-organizing adaptation strategy for the heterogeneous space-to-ground laser and radio frequency links is generated, and the service quality assurance requirements of the target spacecraft's multi-standard services are analyzed. Based on the self-organizing adaptation strategy, the adaptation coefficient of the service quality assurance requirement distribution is calculated. When the adaptation coefficient meets the preset adaptation threshold standard, the self-organizing adaptation strategy is used as the target multi-standard link adaptation scheme for the target spacecraft.

2. The spacecraft multi-mode wireless transmission link adaptation method of claim 1, wherein, The step of outputting the adaptation scheduling weights for the satellite-to-ground laser and radio frequency heterogeneous links based on the transmission capacity and interruption probability includes: The transmission capacity and interruption probability are normalized and weighted to obtain the total link quality score; Define the candidate link transmission time slots for the aforementioned satellite-to-ground laser and radio frequency heterogeneous links; Calculate the link priority weight of the candidate link transmission slot; Based on the total link quality score and the link priority weight, the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links is calculated.

3. The spacecraft multi-mode wireless transmission link adaptation method of claim 2, wherein, The calculation of the adaptation scheduling weights for the satellite-to-ground laser and radio frequency heterogeneous links based on the total link quality score and the link priority weights includes: Analyze the channel fading statistics and service delay sensitivity coefficient of the aforementioned satellite-to-ground laser and radio frequency heterogeneous links; Based on the channel fading statistical characteristics, the service delay sensitivity coefficient, the total link quality score, and the link priority weight, the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links is calculated using the following formula: ； in, This represents the adaptation scheduling weight of the k-th candidate heterogeneous link. This represents the link priority weight coefficient. This represents the priority score of the k-th candidate link. This represents the link quality normalization coefficient. This represents the transmission capacity weighting factor. This represents the normalized transmission capacity of the k-th candidate link. This represents the interruption probability penalty factor. This represents the normalized interruption probability of the k-th candidate link. This represents the business latency weighting coefficient. This represents the normalized score of the k-th candidate link that meets the service latency requirements.

4. The spacecraft multi-mode wireless transmission link adaptation method of claim 1, wherein, The establishment of the dynamic transmission model for the satellite-to-ground laser and radio frequency heterogeneous links includes: Establish the topological connection matrix between the multi-beam intelligent phased array terminal and the satellite and ground station in the space-ground coordinate system; Define the dynamic transmission parameters of the satellite-to-ground laser and radio frequency heterogeneous link, wherein the dynamic transmission parameters include the atmospheric attenuation parameters of the laser link and the rain attenuation parameters of the radio frequency link; Based on the dynamic transmission parameters, a dynamic transmission equation is constructed for the satellite-to-ground laser and radio frequency heterogeneous link, wherein the dynamic transmission equation includes a transmission capacity calculation equation and an interruption probability calculation equation. Based on the topology connection matrix and the dynamic transmission equation, a dynamic transmission model for the satellite-to-ground laser and radio frequency heterogeneous link is established.

5. The spacecraft multi-mode wireless transmission link adaptation method of claim 1, wherein, The step of decomposing the adaptive scheduling weights into slow-varying channel steady-state components and fast-varying fading transient components includes: The adapted scheduling weights are denoised and preprocessed to obtain preprocessed adapted scheduling weights. The preprocessed adaptive scheduling weights are subjected to wavelet transform to obtain transformed adaptive scheduling weights; Extract the slow-changing and fast-changing components of the transformation adaptation scheduling weights; The slow-changing component and the fast-changing component are reconstructed respectively to obtain the slow-changing channel steady-state component and the fast-changing fading transient component.

6. The spacecraft multi-mode wireless transmission link adaptation method of claim 1, wherein, The link jitter waveform defining the fast-fading transient component includes: Identify the spectral peaks of the rapidly fading transient component to calculate the total energy of the rapidly fading transient component; Construct the basic jitter waveform of the rapidly fading transient component; The non-stationary fading effect and natural attenuation characteristics of the fast-changing fading transient component are analyzed to increase the channel fading noise of the basic jitter waveform, thereby obtaining the noisy fast-changing fading component. Based on the natural attenuation characteristics and the total energy of the fast-changing component, the link jitter waveform of the fast-changing fading transient component is determined.

7. The spacecraft multi-mode wireless transmission link adaptation method of claim 1, wherein, The analysis of the potential load variation characteristics of the satellite-to-ground laser and radio frequency heterogeneous links, combining the steady-state scheduling weights and the link jitter waveform, includes: The steady-state scheduling weight and the link jitter waveform are synchronized in time to obtain synchronized steady-state scheduling weight and synchronized link jitter waveform; Generate a composite link state signal consisting of the synchronous steady-state scheduling weight and the synchronous link jitter waveform; Analyze the time spectrum and transmission load steady-state coefficient of the composite link state signal; Calculate the rapidly varying energy difference of the time spectrum; Based on the rapidly varying energy differential and the steady-state coefficient of the transmission load, the potential variation characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link are determined.

8. The spacecraft multi-mode wireless transmission link adaptation method of claim 1, wherein, The step of generating a self-organizing adaptation strategy for the heterogeneous satellite-to-ground laser and radio frequency links based on the potential changes in the transmission load includes: Construct a six-dimensional feature vector of the potential variation characteristics of the transmission load; The six-dimensional feature vector is normalized to obtain a normalized six-dimensional feature vector; Based on the normalized six-dimensional feature vector, the laser link power adjustment, radio frequency link beam pointing adjustment, and link switching threshold adjustment of the satellite-to-ground laser and radio frequency heterogeneous link are analyzed. By combining the laser link power adjustment, the radio frequency link beam pointing adjustment, and the link switching threshold adjustment, a self-organizing adaptation strategy for the heterogeneous satellite-to-ground laser and radio frequency links is generated.

9. The spacecraft multi-mode wireless transmission link adaptation method of claim 1, wherein, The analysis of the service quality assurance requirements distribution of the target spacecraft's multi-mode services includes: Collect multi-standard service quality requirements data for the target spacecraft; Construct a two-dimensional matrix of the multi-standard service quality of service requirement data to calculate the normalized QoS index of the service queue corresponding to the target spacecraft; Define the service type region of the target spacecraft; Based on the normalized QoS metric, the demand percentage for the service type region is calculated using the following formula: ； in, This represents the demand percentage for the s-th business type in the region. This represents the s-th business type region. This represents the index of the business queue in the s-th business type region. This indicates that the s-th business type region is located in Normalized QoS metrics for the service queue This indicates that the s-th business type region is located in The weight coefficient of the business queue, This represents the sum of QoS metrics for all service queues; The distribution of service quality assurance requirements for the target spacecraft's multi-mode services is determined by the aforementioned demand proportions.

10. A spacecraft multi-mode wireless transmission link adaptation system, characterized by, The system includes: The modeling and evaluation module is used to collect the channel status parameters and service quality requirements of the star-ground link of the target spacecraft's multi-beam intelligent phased array terminal, establish a dynamic transmission model of the star-ground laser and radio frequency heterogeneous link, and calculate the transmission capacity and interruption probability of the heterogeneous link based on the channel status parameters and service quality requirements of the star-ground link using the dynamic transmission model. The weight decomposition module is used to output the adaptation scheduling weight of the satellite-to-ground laser and radio frequency heterogeneous links based on the transmission capacity and the interruption probability, and decompose the adaptation scheduling weight into a slow-varying channel steady-state component and a fast-varying fading transient component. The load analysis module is used to determine the steady-state scheduling weight of the slow-varying channel steady-state component, define the link jitter waveform of the fast-varying fading transient component, and analyze the potential change characteristics of the transmission load of the satellite-to-ground laser and radio frequency heterogeneous link by combining the steady-state scheduling weight and the link jitter waveform. The strategy generation module is used to generate a self-organizing adaptation strategy for the space-to-ground laser and radio frequency heterogeneous links based on the potential change characteristics of the transmission load, and to analyze the distribution of service quality assurance requirements for multi-standard services of the target spacecraft. The threshold verification module is used to calculate the adaptation coefficient of the service quality assurance requirement distribution based on the self-organizing adaptation strategy. When the adaptation coefficient meets the preset adaptation threshold standard, the self-organizing adaptation strategy is used as the target multi-standard link adaptation scheme for the target spacecraft.

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