A wireless signal processing method and system in an air-ground integrated satellite internet network

Abstract

The present invention relates to the field of wireless signal processing technology, and discloses a wireless signal processing method and system in an air-ground integrated satellite interconnection network, the method comprising the following steps: receiving orbital parameters in real time, wherein the orbital parameters include ground station reception information and satellite self-reporting information, using an orbital prediction model to predict the satellite position in the future, outputting a corrected state estimate and an updated covariance matrix as the final prediction result output, and re-evaluating the cost function of all potential paths according to the latest orbital prediction result. In the case of changes in the satellite orbit, the present invention can quickly find the best path, maximize the use of network resources through dynamic path evaluation and traffic redistribution, provide fast local processing capabilities, reduce the core network load, solve the key challenges of intelligent routing and traffic management in the SAGIN environment, and significantly improve network performance and service quality.

Description

Technical Field

[0001] The present invention relates to the field of wireless signal processing, and more specifically, to a wireless signal processing method and system in an air-ground-integrated satellite internet network. Background Art

[0002] The Space-Air-Ground Integrated Network (SAGIN) is a three-dimensional seamless communication system that integrates satellite communications, high-altitude platforms (such as drones and stratospheric balloons), and ground wireless networks.

[0003] In the SAGIN environment, since it contains multiple heterogeneous networks and dynamically changing topologies, traditional static routing protocols are difficult to meet complex and changing application requirements. Intelligent routing and traffic management become key factors in ensuring network performance and service quality.

[0004] Due to the influence of factors such as the earth's gravity and atmospheric resistance, the actual orbit of the satellite may deviate from the planned orbit, causing the original path planning to fail. The optimal path needs to be quickly re-evaluated and updated, which increases the difficulty of real-time data synchronization. Summary of the invention

[0005] The present invention provides a wireless signal processing method and system in an air-ground-integrated satellite internet network, which solves the technical problems in related technologies.

[0006] The present invention provides a method for processing wireless signals in an air-ground-integrated satellite internet network, comprising the following steps:

[0007] S100, orbit prediction and monitoring: receiving orbit parameters in real time, where the orbit parameters include ground station reception information and satellite self-reporting information, using the orbit prediction model to predict the satellite position in the future, and outputting the corrected state estimate and the updated covariance matrix as the final prediction result output;

[0008] S200, dynamic path evaluation: re-evaluate the cost functions of all potential paths based on the latest trajectory prediction results;

[0009] S300, fast path reselection: When a significant track change is detected, the path reselection mechanism is immediately triggered, and a heuristic search method is used to find the global optimal solution. For each candidate path, if its cost is lower than the current optimal path, the optimal path is updated;

[0010] S400, traffic redistribution: Based on the newly selected optimal path, traffic is reasonably distributed to each available link, and traffic distribution is solved by applying linear programming to obtain and implement the traffic distribution plan;

[0011] S500, edge computing task scheduling: analyze the incoming data packets, determine the data packets that can be processed at the edge nodes, assign appropriate tasks to the edge computing nodes, use the queuing theory model to evaluate the workload of the edge nodes, and adjust the task priority accordingly to generate a task scheduling plan, and actually assign tasks to the edge nodes according to the task scheduling plan.

[0012] Further, wherein the ground station receives information means collecting satellite telemetry data from a globally distributed network of ground stations, including position, velocity, and attitude;

[0013] The satellite self-reporting information means that the satellite sends its status report autonomously, and the status report includes control input and noise covariance matrix;

[0014] The noise covariance matrix includes the process noise covariance matrix and the observation noise covariance matrix.

[0015] Furthermore, S200 further includes the following steps:

[0016] S210, obtaining a set of corrected state estimates and updated covariance matrix outputted last in S100;

[0017] S220, calculating a link quality indicator, where the link quality indicator is measured by multiple factors, including a signal-to-noise ratio and a bit error rate;

[0018] S230, evaluating transmission delay, where the transmission delay includes propagation delay, processing delay, and queuing delay;

[0019] S240, considering other QoS parameters, in addition to link quality and transmission delay, other QoS parameters are also considered, including bandwidth and jitter;

[0020] S250, calculating a comprehensive cost function, integrating various indicators in S220-S240, and constructing a cost function in the form of a weighted sum, wherein the cost function is as follows:

[0021] ;

[0022] in Indicates the transmission delay, Indicates link reliability, For other QoS parameters, , , It is the weight coefficient corresponding to each indicator.

[0023] Furthermore, the calculation formula of link reliability is as follows:

[0024] ;

[0025] in is the satellite's position, is the location of a ground station or other node;

[0026] Assuming that link reliability is inversely proportional to distance, it is defined as:

[0027] ;

[0028] in is the Euclidean distance between two points. The influence of uncertainty is introduced and the position component in the covariance matrix is Add, then define as:

[0029] ;

[0030] ;

[0031] in represents the standard deviation of the position uncertainty, represents the location component in the covariance matrix.

[0032] Furthermore, the calculation formula of the transmission delay is as follows:

[0033] ;

[0034] in is the speed of light, where represents the standard deviation of the position uncertainty;

[0035] The calculation formulas for other QoS parameters are as follows:

[0036] ;

[0037] in Indicates bandwidth Indicates jitter, and are the weight coefficients of bandwidth and jitter respectively.

[0038] Furthermore, S300 also includes the following steps:

[0039] S310, detecting significant orbit changes: continuously monitoring the corrected state estimate and the updated covariance matrix, identifying the situation where the orbit deviates from the predetermined orbit, calculating the difference between the current orbit and the orbit at the previous moment, defining a significance threshold of the orbit change, and determining whether the threshold is exceeded;

[0040] S320, triggering a path reselection mechanism: constructing a set of all possible paths, including all potential paths from the source node to the target node, and selecting a suitable heuristic search algorithm according to the application scenario;

[0041] S330, update the optimal path: traverse all candidate paths, check the cost of each path, compare the cost of the candidate path with the cost of the current optimal path, and if a better path is found, update the optimal path and record its cost.

[0042] Furthermore, S400 further includes the following steps:

[0043] S410, defining decision variables: identifying all available communication links, and defining a flow allocation variable for each link, indicating the amount of data transmitted through the link;

[0044] S420, setting an objective function: the objective is to maximize the total flow of all links while ensuring that the capacity limit of each link is not exceeded. If there are different link costs, the objective may be to minimize the total cost;

[0045] S430, set constraints: the link capacity constraint is to ensure that the flow allocation of each link does not exceed its maximum capacity, the flow conservation constraint is to ensure that the total flow entering the node is equal to the total flow leaving the node, and the non-negativity constraint is to ensure that the flow allocation variable is non-negative;

[0046] S440, applying linear programming solution: combining the above objective function and constraint conditions into a linear programming model, and using an appropriate linear programming solver to solve the model to obtain an optimal traffic allocation on each link;

[0047] S450, implement the traffic distribution plan: configure routers and switches according to the solution result of the linear programming solver to achieve traffic distribution.

[0048] Furthermore, S500 further includes the following steps:

[0049] S510, analyzing the incoming data packets: performing preliminary classification according to the types of the data packets, identifying the QoS requirements of each type of data packets, including bandwidth, delay, and jitter, evaluating the processing capability of the edge node, and determining whether it can effectively process the specific type of data packets;

[0050] S520, determining data packets that can be processed by the edge node: for each data packet type, checking whether there is an edge node j that meets its QoS requirement, if the processing capability of the edge node can meet the QoS requirement of the data packet, marking the data packet as "locally processable", and prioritizing the locally processable data packets according to the importance of the QoS requirement;

[0051] S530, assigning appropriate tasks to edge computing nodes: defining appropriate tasks according to the data packet type and the capabilities of the edge nodes, assigning the determined tasks to appropriate edge nodes, and obtaining a list of tasks assigned to the edge nodes and their execution times;

[0052] S540, using a queuing theory model to evaluate the workload of the edge node, and generating a task scheduling plan according to the evaluation result;

[0053] S550, implementation and monitoring: According to the task scheduling plan generated in the above steps, tasks are actually assigned to edge nodes.

[0054] The present invention also proposes a wireless signal processing system in an air-ground-integrated satellite internet network, which is characterized in that it is used to execute the steps in the aforementioned wireless signal processing method in an air-ground-integrated satellite internet network, including:

[0055] Orbit prediction and monitoring module: receives orbit parameters in real time, including ground station reception information and satellite self-reporting information, uses orbit prediction model to predict satellite position in the future, and outputs corrected state estimation and updated covariance matrix as final prediction result output;

[0056] Dynamic path evaluation module: re-evaluates the cost functions of all potential paths based on the latest trajectory prediction results;

[0057] Fast path reselection module: When a significant change in the track is detected, the path reselection mechanism is immediately triggered, and the heuristic search method is used to find the global optimal solution. For each candidate path, if its cost is lower than the current optimal path, the optimal path is updated;

[0058] Traffic redistribution module: Based on the newly selected optimal path, traffic is reasonably distributed to each available link. Traffic distribution is solved by applying linear programming to obtain and implement traffic distribution solutions.

[0059] Edge computing task scheduling module: Analyzes incoming data packets, determines the data packets that can be processed at the edge nodes, assigns appropriate tasks to the edge computing nodes, uses queuing theory models to evaluate the workload of edge nodes, and adjusts task priorities accordingly to generate a task scheduling plan, and actually assigns tasks to edge nodes according to the task scheduling plan.

[0060] The present invention also proposes a storage medium storing non-temporary computer-readable instructions for executing the steps in the aforementioned method for processing wireless signals in an air-ground-integrated satellite Internet network.

[0061] The beneficial effects of the present invention are:

[0062] When the satellite orbit changes, the present invention can quickly find the best path, maximize the use of network resources through dynamic path evaluation and traffic redistribution, provide fast local processing capabilities, reduce core network load, solve the key challenges of intelligent routing and traffic management in the SAGIN environment, and significantly improve network performance and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a wireless signal processing method in an air-ground integrated satellite internet network proposed by the present invention;

[0064] Figure 2 It is a structural block diagram of a wireless signal processing system in an air-ground integrated satellite internet network proposed by the present invention. DETAILED DESCRIPTION

[0065] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Various processes or components may be omitted, substituted, or added to each example as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0066] like Figure 1-Figure 2 As shown, a wireless signal processing method in an air-ground integrated satellite Internet network comprises the following steps:

[0067] S100, orbit prediction and monitoring:

[0068] Receive orbital parameters in real time, including ground station reception information and satellite self-reporting information;

[0069] Ground station receives information: collects satellite telemetry data from a globally distributed network of ground stations, including position, speed, attitude and other information;

[0070] Satellite self-reporting information: The satellite sends its status report autonomously, which includes control input and noise covariance matrix;

[0071] The noise covariance matrix includes the process noise covariance matrix and the observation noise covariance matrix;

[0072] Use orbit prediction models to predict satellite positions for a period of time in the future;

[0073] The calculation formula of the orbit prediction model is as follows:

[0074] ;

[0075] ;

[0076] in is the predicted state vector, is the state transition matrix, is the control input model, is the control input, is the forecast covariance matrix, is the process noise covariance matrix, Indicates transpose.

[0077] The input to the orbit prediction model includes the initial state vector : Includes the satellite's position coordinates at a certain moment , velocity component As well as attitude angle, control input Refers to the mission instructions executed by the satellite, such as thruster ignition, solar panel adjustment, etc. The process noise covariance matrix Describes the statistical characteristics of the uncertainty within the system, the observation noise covariance matrix It reflects the statistical characteristics of measurement errors.

[0078] In one embodiment of the present invention, the initial state vector is set and the initial covariance matrix , based on known orbital parameters;

[0079] Used to initialize the covariance estimate, that is ; Used to initialize the state vector, that is ;

[0080] 1) Time update:

[0081] Prediction status:

[0082] ;

[0083] ;

[0084] Use the best state estimate from the previous moment and control input To predict the state at the next moment , initialize the state vector ;

[0085] Prediction covariance:

[0086] ;

[0087] ;

[0088] Use the covariance estimate from the previous moment and process noise covariance To update the covariance at the next moment , to reflect the uncertainty of the predicted state.

[0089] 2) Measurement update:

[0090] Compute the residual:

[0091]

[0092] Comparison of new observations and predicted status , and get the residual ;

[0093] Calculate the Kalman gain:

[0094] ;

[0095] According to the predicted covariance and the observation noise covariance Calculate Kalman gain , combining new observational data with predictive models.

[0096] Corrected state estimate:

[0097] ;

[0098] Using Kalman Gain and residual To correct the predicted state and get the final estimated state .

[0099] Update the covariance matrix:

[0100] ;

[0101] Using Kalman Gain and the observation matrix Update the covariance matrix to reflect the latest state estimate uncertainty.

[0102] The corrected state estimate and the updated covariance matrix Output as the final prediction result.

[0103] S200, Dynamic Path Evaluation:

[0104] Re-evaluate the cost function for all potential paths based on the latest trajectory predictions , considering factors such as link reliability, transmission delay, etc., the calculation formula is as follows:

[0105] ;

[0106] in Indicates the path Link reliability on is the transmission delay, For other QoS parameters, is the corresponding weight coefficient.

[0107] In one embodiment of the present invention, the specific steps are as follows:

[0108] S210, obtain the last output group from S100 and ;

[0109] State Estimation: ,in is the satellite position, It's speed, etc.

[0110] Covariance matrix: , represents the uncertainty of the state estimate.

[0111] S220, calculating the link quality index:

[0112] Link quality can be measured by various factors, such as signal-to-noise ratio (SNR), bit error rate (BER), etc. Here we use a simplified model to represent link reliability :

[0113] ;

[0114] in is the satellite's position, is the location of a ground station or other node.

[0115] Assuming that link reliability is inversely proportional to distance, it can be defined as:

[0116] ;

[0117] in is the Euclidean distance between two points or a more complex geometric distance calculation method. In order to introduce the influence of uncertainty, the position component in the covariance matrix can be Take it into account:

[0118] ;

[0119] in Represents the standard deviation of the position uncertainty.

[0120] S230, evaluate transmission delay:

[0121] Transmission delay Including propagation delay, processing delay and queuing delay. For SAGIN environment, the main concern is propagation delay, which is proportional to the distance the signal propagates:

[0122] ;

[0123] in is the speed of light (about 3×10 8 m / s), where represents the standard deviation of the position uncertainty;

[0124] S240, consider other quality of service (QoS) parameters:

[0125] In addition to link quality and transmission delay, other QoS parameters can also be considered, such as bandwidth , Jitter Etc. These parameters usually depend on specific application requirements and service level agreements (SLAs), so they can be adjusted according to actual conditions.

[0126] ;

[0127] in and are the weight coefficients of bandwidth and jitter respectively.

[0128] S250, calculate the comprehensive cost function:

[0129] Combining the above indicators, a cost function C(p) in the form of weighted sum can be constructed:

[0130] ;

[0131] in , , It is the weight coefficient corresponding to each indicator and can be adjusted according to the specific needs of the application.

[0132] S300, fast path reselection: When a significant change in the track is detected, the path reselection mechanism is immediately triggered; a heuristic search method (such as genetic algorithm, ant colony optimization) is used to find the global optimal solution; for each candidate path p, if its cost is lower than the current optimal path, the optimal path is updated;

[0133] In one embodiment of the present invention, the specific steps are as follows:

[0134] S310, detect significant changes in trajectory:

[0135] Monitoring orbit prediction results: Continuously monitor the corrected state estimate and the updated covariance matrix , identify situations where the track deviates from the intended track;

[0136] Calculate the difference between the current orbit and the orbit at the previous moment:

[0137] ;

[0138] in is the current satellite position, is the satellite position at the last moment, It is the distance calculation function between two points.

[0139] Set threshold: Define the significance threshold of track changes , such as position deviation exceeding a certain distance or speed change exceeding a certain range;

[0140] Determine whether the threshold is exceeded:

[0141] ;

[0142] S320, triggering the path reselection mechanism:

[0143] Initialize the search space: build a set of all possible paths , including all potential paths from the source node to the target node, and selecting a suitable heuristic search algorithm, such as genetic algorithm (GA), according to the application scenario;

[0144] The steps of Genetic Algorithm (GA) are as follows:

[0145] Population initialization: randomly generate a set of initial paths as the population ;

[0146] Fitness evaluation: Calculate the cost function for each path , and determine its fitness ;

[0147] ;

[0148] Select operation: Based on fitness The proportion of selecting individuals in the next generation population;

[0149] Crossover operation: Cross-combine the selected paths to generate new paths;

[0150] Mutation operation: Randomly mutate the newly generated paths to increase diversity;

[0151] Iterative update: Repeat the above process for several generations until the termination condition is met (such as the maximum number of iterations or fitness convergence);

[0152] S330, update the optimal path:

[0153] Traverse all candidate paths: Check each path Cost ;

[0154] Compare costs: Compare the cost of the candidate path with the cost of the current best path;

[0155] Update the optimal path: If a better path is found, update the optimal path and record its cost ;

[0156] ;

[0157] S400, traffic redistribution:

[0158] Based on the newly selected optimal path, traffic is reasonably distributed to each available link to ensure maximum utilization of resources.

[0159] Apply linear programming or other mathematical optimization techniques to solve the flow allocation problem.

[0160] In one embodiment of the present invention, the following steps are specifically included:

[0161] S410, define decision variables:

[0162] Determine available links: Identify all available communication links ;

[0163] Define traffic distribution variables: for each link Define a traffic distribution variable , represents the amount of data transmitted through the link;

[0164] in Indicates that the link The allocated traffic (in Mbps or packets per second), Indicates the number of available links.

[0165] S420, setting the objective function:

[0166] Maximizing resource utilization: The goal is to maximize the total flow across all links while ensuring that the capacity limits of each link are not exceeded;

[0167] Minimize cost: If there are different link costs (such as bandwidth charges, delays), the goal can be to minimize the total cost;

[0168] The calculation formula is as follows:

[0169] Maximize total flow:

[0170] ;

[0171] Minimize total cost:

[0172] ;

[0173] in Yes Link The unit flow cost, represents the objective function value of the total flow, The objective function value representing the total cost;

[0174] S430, set constraints:

[0175] Link capacity constraint: ensures that the traffic allocation of each link does not exceed its maximum capacity ;

[0176] Flow conservation constraint: ensures that the total flow entering a node is equal to the total flow leaving the node (applicable to every intermediate node in the network);

[0177] Non-negativity constraint: Ensure that the flow distribution variables non-negative;

[0178] Calculation formula:

[0179] Link capacity constraints:

[0180] ;

[0181] Flow conservation constraint (for node j):

[0182] ;

[0183] in and are the sets of links flowing into and out of node j respectively;

[0184] Non-negativity constraints:

[0185] ;

[0186] S440, linear programming is used to solve:

[0187] Construct a linear programming model: combine the above objective function and constraints into a linear programming model;

[0188] Select solver: Use an appropriate linear programming solver (such as Simplex algorithm, interior point method, etc.) to solve the model;

[0189] Get the optimal solution: Get the optimal traffic distribution on each link ;

[0190] The calculation formula is as follows:

[0191] ;

[0192] :

[0193] ;

[0194] ;

[0195] ;

[0196] in is a coefficient set according to the objective (maximizing total flow or minimizing total cost).

[0197] S450, implement the traffic distribution plan:

[0198] Configure network devices: Based on the solution results , configure network devices such as routers and switches to achieve traffic distribution;

[0199] Real-time monitoring and adjustment: Continuously monitor link status and traffic distribution, and dynamically adjust traffic distribution strategies when necessary;

[0200] In one embodiment of the present invention, the following examples are given:

[0201] Assume there are three links ;

[0202] The capacities are =100Mbps, =80Mbps, =120Mbps, the goal is to minimize the total cost, where the cost of each link is: = 1.5 yuan / Mbps, =2.0 yuan / Mbps, =1.8 yuan / Mbps.

[0203] Linear Programming Model:

[0204] ;

[0205] :

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] ;

[0211] in is the total demand flow. By solving this linear programming model, we can obtain the optimal flow allocation solution. , , , thereby ensuring maximum utilization of resources and minimizing total costs.

[0212] S500, edge computing task scheduling: analyze the incoming data packets, determine the data packets that can be processed at the edge nodes, assign appropriate tasks to the edge computing nodes, such as data compression, preliminary analysis, etc., use the queuing theory model to evaluate the workload of the edge nodes, and adjust the task priority accordingly to generate a task scheduling plan, and actually assign tasks to the edge nodes according to the task scheduling plan.

[0213] In one embodiment of the present invention, the specific steps are as follows:

[0214] S510, analyzing the incoming data packet:

[0215] Packet classification: according to the type of packet (such as video streams, sensor data, control instructions, etc.) for preliminary classification, Indicates the type identifier of the i-th type of data packet;

[0216] QoS Requirements Assessment: Identify the Quality of Service (QoS) requirements for each type of packet , including bandwidth, delay, jitter, etc.

[0217] in ,in is the bandwidth, is the maximum allowed delay, is the maximum allowed jitter.

[0218] Local processing capacity evaluation: Evaluate the processing capacity of edge nodes to determine whether they can effectively process specific types of data packets. The processing capacity of edge node j is ;

[0219] ;

[0220] in Indicates the processing power of the CPU. Indicates the processing power of memory. Indicates the processing capacity of storage resources. Indicates the bandwidth processing capability.

[0221] S520, determining a data packet that can be processed by the edge node:

[0222] Match QoS requirements with processing capabilities: For each packet type , check whether there is an edge node j that meets its QoS requirements;

[0223] If the processing capacity of edge node j Able to meet data packets QoS requirements , then mark the data packet as "locally processable";

[0224] Prioritization: Prioritize packets that can be processed locally based on the importance of QoS requirements;

[0225] For example, data packets with high real-time requirements (such as control instructions for autonomous driving) should have higher priority.

[0226] Calculation formula:

[0227] QoS matching conditions:

[0228] ;

[0229] in is the satellite position, is the edge node location, is the edge node processing time, is the maximum allowed jitter of the edge node.

[0230] S530, assigning appropriate tasks to edge computing nodes:

[0231] Task definition: Based on data packet type and the capabilities of edge node j, defining suitable tasks, such as data compression, preliminary analysis, cache services, etc.;

[0232] Task allocation: Assign the determined tasks to the appropriate edge node j and obtain the task list assigned to edge node j and execution time , ensuring load balancing and maximum resource utilization;

[0233] in Represents the task list assigned to edge node j, for packet type , Indicates execution of tasks The time required.

[0234] S540, using a queuing theory model to evaluate the workload of the edge node, and generating a task scheduling plan according to the evaluation result;

[0235] In one embodiment of the present invention, the specific steps are as follows:

[0236] S541, build a queuing model: select an appropriate queuing model (such as M / M / 1) to describe the process of edge nodes receiving, processing and sending data packets;

[0237] S542, evaluating workload: calculating average waiting time, queue length, and service utilization of edge nodes to evaluate current workload;

[0238] S543, Adjust task priority: Dynamically adjust task priority based on workload assessment results to ensure that critical tasks are processed first;

[0239] Based on the current arrival rate and service rate Calculating system utilization , average waiting time and the average queue length ,if If it is close to or exceeds 1, the task priority needs to be adjusted to give priority to processing data packets with high QoS requirements and reduce the resource usage of low-priority tasks;

[0240] The calculation formula of the M / M / 1 queue model is as follows:

[0241] Average waiting time :

[0242] ;

[0243] Average queue length :

[0244] ;

[0245] System Utilization :

[0246] ;

[0247] in represents the arrival rate, represents the service rate;

[0248] S544, generating a task scheduling plan according to the adjusted task priority:

[0249] Create a task allocation table: Based on the above analysis results, create a task allocation table to clarify the tasks that each edge node needs to perform and their priorities;

[0250] Set time window: Set the start time and estimated completion time for each task to ensure that the task is executed as planned;

[0251] Resource allocation: Describes the resources (such as CPU, memory, bandwidth) required by each edge node when performing tasks, and ensures that these resources do not exceed the capacity limit of the node.

[0252] S550, Implementation and Monitoring:

[0253] Task scheduling and execution: According to the task scheduling plan generated in the above steps, tasks are actually assigned to edge nodes;

[0254] Real-time monitoring: Continuously monitor the working status of edge nodes, including resource usage, queue length, task completion time, etc.

[0255] Feedback and optimization: Dynamically adjust task allocation strategies based on monitoring results to optimize overall performance.

[0256] The above specific steps can effectively analyze the incoming data packets, determine which ones can be processed at the edge nodes, and assign appropriate tasks to these nodes. The queuing theory model is used to evaluate the workload of the edge nodes and dynamically adjust the task priority, thereby ensuring the efficiency and robustness of the intelligent routing and traffic management strategies in the SAGIN environment.

[0257] In the present invention, a wireless signal processing system in an air-ground integrated satellite internet network is also proposed, comprising:

[0258] Orbit prediction and monitoring module 101: receives orbit parameters in real time, including ground station reception information and satellite self-reporting information, uses orbit prediction model to predict satellite position in the future, and outputs corrected state estimation and updated covariance matrix as final prediction result output

[0259] Dynamic path evaluation module 102: re-evaluates the cost functions of all potential paths according to the latest trajectory prediction results;

[0260] Fast path reselection module 103: When a significant track change is detected, the path reselection mechanism is immediately triggered, and a heuristic search method is used to find the global optimal solution. For each candidate path, if its cost is lower than the current optimal path, the optimal path is updated;

[0261] Traffic redistribution module 104: Based on the newly selected optimal path, traffic is reasonably distributed to each available link, traffic distribution is solved by applying linear programming, and a traffic distribution plan is obtained and implemented;

[0262] Edge computing task scheduling module 105: Analyze the incoming data packets, determine the data packets that can be processed at the edge nodes, assign appropriate tasks to the edge computing nodes, use the queuing theory model to evaluate the workload of the edge nodes, and adjust the task priority accordingly to generate a task scheduling plan, and actually assign tasks to the edge nodes according to the task scheduling plan.

[0263] At least one embodiment of the present disclosure provides a storage medium storing non-temporary computer-readable instructions for executing one or more steps in the aforementioned method for processing wireless signals in an integrated air-ground-space satellite internet network.

[0264] The computer program may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

[0265] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A wireless signal processing method in an air-ground integrated satellite internet network, characterized in that: The following steps are involved: S100, orbit prediction and monitoring: receiving orbit parameters in real time, where the orbit parameters include ground station reception information and satellite self-reporting information, using the orbit prediction model to predict the satellite position in the future, and outputting the corrected state estimate and the updated covariance matrix as the final prediction result output; S200, dynamic path evaluation: re-evaluate the cost functions of all potential paths based on the latest trajectory prediction results; S300, fast path reselection: When a significant track change is detected, the path reselection mechanism is immediately triggered, and a heuristic search method is used to find the global optimal solution. For each candidate path, if its cost is lower than the current optimal path, the optimal path is updated; S400, traffic redistribution: Based on the newly selected optimal path, traffic is reasonably distributed to each available link, and traffic distribution is solved by applying linear programming to obtain and implement the traffic distribution plan; S500, edge computing task scheduling: analyze the incoming data packets, determine the data packets that can be processed at the edge nodes, assign appropriate tasks to the edge computing nodes, use the queuing theory model to evaluate the workload of the edge nodes, and adjust the task priority accordingly to generate a task scheduling plan, and actually assign tasks to the edge nodes according to the task scheduling plan.

2. The wireless signal processing method in the space-ground integrated satellite internet network according to claim 1, characterized in that: The ground station receives information, which means collecting satellite telemetry data from a globally distributed network of ground stations, including position, speed, and attitude; The satellite self-reporting information means that the satellite sends its status report autonomously, and the status report includes control input and noise covariance matrix; The noise covariance matrix includes the process noise covariance matrix and the observation noise covariance matrix.

3. The wireless signal processing method in the space-ground integrated satellite internet network according to claim 2 is characterized in that: S200 also includes the following steps: S210, obtaining a set of corrected state estimates and updated covariance matrix outputted last in S100; S220, calculating a link quality indicator, where the link quality indicator is measured by multiple factors, including a signal-to-noise ratio and a bit error rate; S230, evaluating transmission delay, where the transmission delay includes propagation delay, processing delay, and queuing delay; S240, considering other QoS parameters, in addition to link quality and transmission delay, other QoS parameters are also considered, including bandwidth and jitter; S250, calculating a comprehensive cost function, integrating various indicators in S220-S240, and constructing a cost function in the form of a weighted sum, wherein the cost function is as follows: ； in Indicates the transmission delay, Indicates link reliability, For other QoS parameters, , , It is the weight coefficient corresponding to each indicator.

4. The wireless signal processing method in the space-ground integrated satellite internet network according to claim 3 is characterized in that: The calculation formula of link reliability is as follows: ； in is the satellite's position, is the location of a ground station or other node; Assuming that link reliability is inversely proportional to distance, it is defined as: ； in is the Euclidean distance between two points. The influence of uncertainty is introduced and the position component in the covariance matrix is Add, then define as: ； ； in represents the standard deviation of the position uncertainty, represents the location component in the covariance matrix.

5. The wireless signal processing method in the space-ground integrated satellite internet network according to claim 4 is characterized in that: The formula for calculating the transmission delay is as follows: ； in is the speed of light, where represents the standard deviation of the position uncertainty; The calculation formulas for other QoS parameters are as follows: ； in Indicates bandwidth Indicates jitter, and are the weight coefficients of bandwidth and jitter respectively.

6. The wireless signal processing method in the space-ground integrated satellite internet network according to claim 5, characterized in that: S300 also includes the following steps: S310, detecting significant orbit changes: continuously monitoring the corrected state estimate and the updated covariance matrix, identifying the situation where the orbit deviates from the predetermined orbit, calculating the difference between the current orbit and the orbit at the previous moment, defining a significance threshold of the orbit change, and determining whether the threshold is exceeded; S320, triggering a path reselection mechanism: constructing a set of all possible paths, including all potential paths from the source node to the target node, and selecting a suitable heuristic search algorithm according to the application scenario; S330, update the optimal path: traverse all candidate paths, check the cost of each path, compare the cost of the candidate path with the cost of the current optimal path, and if a better path is found, update the optimal path and record its cost.

7. The wireless signal processing method in the space-ground integrated satellite internet network according to claim 6 is characterized in that: S400 also includes the following steps: S410, defining decision variables: identifying all available communication links, and defining a flow allocation variable for each link, indicating the amount of data transmitted through the link; S420, setting an objective function: the objective is to maximize the total flow of all links while ensuring that the capacity limit of each link is not exceeded. If there are different link costs, the objective may be to minimize the total cost; S430, set constraints: the link capacity constraint is to ensure that the flow allocation of each link does not exceed its maximum capacity, the flow conservation constraint is to ensure that the total flow entering the node is equal to the total flow leaving the node, and the non-negativity constraint is to ensure that the flow allocation variable is non-negative; S440, applying linear programming solution: combining the above objective function and constraint conditions into a linear programming model, and using an appropriate linear programming solver to solve the model to obtain an optimal traffic allocation on each link; S450, implement the traffic distribution plan: configure routers and switches according to the solution result of the linear programming solver to achieve traffic distribution.

8. The wireless signal processing method in the space-ground integrated satellite internet network according to claim 7 is characterized in that: S500 also includes the following steps: S510, analyzing the incoming data packets: performing preliminary classification according to the types of the data packets, identifying the QoS requirements of each type of data packets, including bandwidth, delay, and jitter, evaluating the processing capability of the edge node, and determining whether it can effectively process the specific type of data packets; S520, determining the data packets that can be processed by the edge node: for each data packet type, checking whether there is an edge node j that meets its QoS requirement, if the processing capability of the edge node can meet the QoS requirement of the data packet, marking the data packet as "locally processable", and prioritizing the locally processable data packets according to the importance of the QoS requirement; S530, assigning appropriate tasks to edge computing nodes: defining appropriate tasks according to the data packet type and the capabilities of the edge nodes, assigning the determined tasks to appropriate edge nodes, and obtaining a list of tasks assigned to the edge nodes and their execution times; S540, using a queuing theory model to evaluate the workload of the edge node, and generating a task scheduling plan according to the evaluation result; S550, implementation and monitoring: According to the task scheduling plan generated in the above steps, tasks are actually assigned to edge nodes.

9. A wireless signal processing system in an air-ground integrated satellite internet network, characterized in that: The method for executing the steps of the wireless signal processing method in the air-ground integrated satellite internet network as claimed in any one of claims 1 to 8 comprises: Orbit prediction and monitoring module: receives orbit parameters in real time, including ground station reception information and satellite self-reporting information, uses orbit prediction model to predict satellite position in the future, and outputs corrected state estimation and updated covariance matrix as final prediction result output; Dynamic path evaluation module: re-evaluates the cost functions of all potential paths based on the latest trajectory prediction results; Fast path reselection module: When a significant change in the track is detected, the path reselection mechanism is immediately triggered, and the heuristic search method is used to find the global optimal solution. For each candidate path, if its cost is lower than the current optimal path, the optimal path is updated; Traffic redistribution module: Based on the newly selected optimal path, traffic is reasonably distributed to each available link. Traffic distribution is solved by applying linear programming to obtain and implement traffic distribution solutions. Edge computing task scheduling module: Analyzes incoming data packets, determines the data packets that can be processed at the edge nodes, assigns appropriate tasks to the edge computing nodes, uses queuing theory models to evaluate the workload of edge nodes, and adjusts task priorities accordingly to generate a task scheduling plan, and actually assigns tasks to edge nodes according to the task scheduling plan.

10. A storage medium, characterized in that: Non-temporary computer-readable instructions are stored for executing the steps in a wireless signal processing method in an air-ground integrated satellite Internet network as described in any one of claims 1-8.

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