A task offloading method for zero-trust satellite networks

Abstract

The present invention discloses a task offloading method for a zero-trust satellite network, which relates to the technical field of communication networks. The method comprises: sending model parameters to other low-orbit satellites so that other low-orbit satellites vote on the low-orbit satellite based on the empirical data of other low-orbit satellites and the model parameters of the low-orbit satellite; determining the pre-submission status of the low-orbit satellite based on the voting results of other low-orbit satellites on the low-orbit satellite; when the ratio between the number of low-orbit satellites in the pre-submission state and the total number of low-orbit satellites in the communication network is greater than a preset ratio threshold, correcting the model parameters of the low-orbit satellite in the pre-submission state, and performing global aggregation based on the corrected model parameters to obtain global model parameters; using the global model parameters as the model parameters of the model of each low-orbit satellite, and calculating the task offloading strategy for the corresponding low-orbit satellite through the model with updated parameters. The method improves the accuracy of the calculation task offloading results of the low-orbit satellite.

Description

Technical Field

[0001] The present invention relates to the field of communication network technology, and in particular to a task offloading method for a zero-trust satellite network. Background Art

[0002] With the rapid development and widespread application of 6G technology, computationally intensive services, such as metaverse entertainment, intelligent transportation, and immersive communications, will become more widespread in people's daily lives, providing convenience and fun. However, these computationally intensive services often require frequent uploading of computing tasks to cloud servers, placing a significant computational load on them. Furthermore, the high transmission latency and cost associated with this interactive approach make it difficult to meet the Quality of Service (QoS) requirements of 6G communication technology. In urban areas with well-established infrastructure, latency-sensitive, computationally intensive, and highly confidential tasks are typically offloaded to ground-based edge servers, significantly reducing the latency and cost associated with offloading to cloud servers, improving QoS, and alleviating pressure on cloud servers. However, in some remote or environmentally harsh areas, existing communication technologies cannot meet the seamless wireless transmission and wide coverage requirements of 6G, as the potential revenue cannot match the cost of deploying and maintaining ground infrastructure, such as base stations (BSs).

[0003] In existing technologies, non-terrestrial networks using low Earth orbit (LEO) satellites can serve as a supplement to ground infrastructure, offloading computing tasks that should be transmitted to cloud servers or ground edge servers to LEO satellite networks for processing, achieving seamless communication coverage and high throughput while reducing transmission latency and costs.

[0004] However, trust issues between distributed LEO satellites still pose a huge challenge: LEO satellites launched by different providers are usually selected to minimize costs and improve QoS. However, this approach leads to potential trust issues between different LEO satellites, and malicious behaviors such as calculation result forgery and data tampering may occur, reducing the reliability of mission results and leading to inaccurate results of computing task offloading from LEO satellites. Summary of the Invention

[0005] Based on this, it is necessary to provide a task offloading method for zero-trust satellite networks to address the above technical problems. This method improves the accuracy of computing task offloading results of low-orbit satellites.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a task offloading method for a zero-trust satellite network, comprising:

[0008] When a low-orbit satellite receives a computing task, a model parameter of a model used by the low-orbit satellite at the current moment to determine a computing task offloading strategy is obtained; the low-orbit satellite is any low-orbit satellite in a communication network; and the communication network is a network formed by a low-orbit satellite and other low-orbit satellites that are accessible within a communication range of the low-orbit satellite.

[0009] sending model parameters to other low-orbit satellites in the communication network so that the other low-orbit satellites vote on the low-orbit satellite based on the experience data of the other low-orbit satellites and the model parameters of the low-orbit satellite; the experience data is data exchanged between the other low-orbit satellites and the low-orbit satellites in the communication network;

[0010] Determine the pre-commitment status of the low-orbit satellite based on the voting results of other low-orbit satellites on the low-orbit satellite;

[0011] When the ratio between the number of low-orbit satellites in the pre-submission state and the total number of low-orbit satellites in the communication network is greater than a preset ratio threshold, the model parameters of the low-orbit satellites in the pre-submission state are corrected, and global aggregation is performed based on the corrected model parameters through a voting mechanism and a cold start mechanism to obtain global model parameters;

[0012] The global model parameters are used as the model parameters of each low-orbit satellite model, and the task offloading strategy of the corresponding low-orbit satellite is calculated through the model with updated parameters.

[0013] Preferably, obtaining empirical data of other low-orbit satellites at the current moment includes:

[0014] According to the positions of other low-orbit satellites at the current moment, the state space of other low-orbit satellites at the current moment is obtained, and the reward values ​​of other low-orbit satellites at the current moment are calculated;

[0015] Analyze the state space based on the local models of other low-orbit satellites to determine the action space of other low-orbit satellites at the next moment;

[0016] Transfer the states of other low-orbit satellites according to the action space to obtain the state spaces of other low-orbit satellites at the next moment;

[0017] The state space, action space, reward value of other low-orbit satellites at the current moment and the state space of other low-orbit satellites at the next moment are determined as experience data.

[0018] Preferably, other low-orbit satellites k The expression of the state space at time t is:

[0019]

[0020] Where D is the distance between every two low-orbit satellites The set of L(t,s k' ) are other low-orbit satellites k' The calculated load at time t, For other low-orbit satellites k' To low-orbit satellites k The transmission rate, For other low-orbit satellites k' The required computing resources, is the delay threshold.

[0021] Preferably, calculating the reward values ​​of other low-orbit satellites at the current moment includes:

[0022] Obtain the number of successful missions and failed missions processed by other low-orbit satellites within a preset time period;

[0023] Determine the active reward and penalty values ​​based on the number of successful and failed tasks;

[0024] The difference between the active reward value and the penalty value is determined as the reward value of other low-orbit satellites.

[0025] Preferably, voting for the low-orbit satellites based on empirical data of other low-orbit satellites and model parameters of the low-orbit satellites includes:

[0026] Calculate the loss rate between the models of the other LEO satellites and the model of the LEO satellite based on the model parameters and empirical data of the other LEO satellites and the model parameters of the LEO satellite;

[0027] If the loss rate is greater than the loss tolerance threshold, the voting result of the other low-orbit satellites on the low-orbit satellite is determined to be in favor;

[0028] If the loss rate is less than or equal to the loss tolerance threshold, it is determined that the voting result of other low-orbit satellites on the low-orbit satellite is against.

[0029] Preferably, calculating the loss rate between the model of the other low-orbit satellite and the model of the low-orbit satellite based on the model parameters and empirical data of the other low-orbit satellites and the model parameters of the low-orbit satellite comprises:

[0030] Calculating the arrival time difference of the model of the other low-orbit satellites based on the model parameters and empirical data of the other low-orbit satellites, and calculating the arrival time difference of the model of the low-orbit satellite based on the model parameters of the low-orbit satellite;

[0031] The ratio of the arrival target time difference of the models of other low-orbit satellites to the arrival target time difference of the model of the low-orbit satellite is determined as the loss rate between the models of the other low-orbit satellites and the model of the low-orbit satellite.

[0032] Preferably, the calculation formula for the time difference to the target is:

[0033]

[0034] Where γ is the discount factor, are model parameters, It is a low-orbit satellite k In the state space Next execution action space The model parameters are Q value when ; Q value represents the model parameter In the state space Execution Action Space The expected value of the reward obtained.

[0035] Preferably, determining the pre-commitment status of the low-orbit satellite according to the voting results of other low-orbit satellites on the low-orbit satellite includes:

[0036] Obtain the number of other low-orbit satellites that vote in favor of the low-orbit satellite;

[0037] The ratio of the number of affirmative votes to the total number of other low-orbit satellites is determined as the verification ratio;

[0038] If the verification ratio is greater than the preset verification threshold, the state of the low-orbit satellite is determined to be a pre-submission state.

[0039] Preferably, the model parameters of the low-orbit satellite in the pre-submission state are modified through a voting mechanism and a cold start mechanism, including:

[0040] For any low-orbit satellite in a pre-submission state, obtain a first objection number of other low-orbit satellites that voted against the low-orbit satellite, and a second objection number of other low-orbit satellites that voted against the other low-orbit satellites;

[0041] Determine the parameters of the constraint-corrected voting mechanism based on the first and second objection numbers;

[0042] The cold start reputation aggregation parameter is determined based on the accumulated historical reputation and reputation threshold parameters of the low-orbit satellite; the accumulated historical reputation represents the number of consecutive rounds of global aggregation in which the low-orbit satellite successfully participated;

[0043] According to the constraint correction voting mechanism parameters and cold start reputation aggregation parameters, the model parameters of the low-orbit satellite are corrected to obtain the corrected model parameters.

[0044] Preferably, global aggregation is performed based on the corrected model parameters to obtain global model parameters, including:

[0045] The weighted average of the corrected model parameters is performed to obtain the global model parameters.

[0046] The present invention provides a task offloading device for a zero-trust satellite network, comprising:

[0047] an acquisition module, configured to, when a low-orbit satellite receives a computing task, acquire model parameters of a model used by the low-orbit satellite at the current moment to determine a computing task offloading strategy; the low-orbit satellite is any low-orbit satellite in a communication network; and the communication network is a network formed by a low-orbit satellite and other low-orbit satellites accessible within a communication range of the low-orbit satellite;

[0048] A voting module is configured to send model parameters to other low-orbit satellites in the communication network, so that the other low-orbit satellites vote for the low-orbit satellite based on the empirical data of the other low-orbit satellites and the model parameters of the low-orbit satellite; the empirical data is the data exchanged between the other low-orbit satellites and the low-orbit satellites in the communication network;

[0049] a determination module, configured to determine a pre-commitment status of a low-orbit satellite based on voting results of other low-orbit satellites on the low-orbit satellite;

[0050] an obtaining module, configured to, when a ratio between the number of low-orbit satellites in a pre-committed state in the communication network and the total number of low-orbit satellites in the communication network is greater than a preset ratio threshold, modify the model parameters of each low-orbit satellite through a voting mechanism and a cold start mechanism, and perform global aggregation based on the modified model parameters to obtain global model parameters;

[0051] The calculation module is used to use the global model parameters as the model parameters of each low-orbit satellite model, and calculate the task offloading strategy for the corresponding low-orbit satellite through the model with updated parameters.

[0052] The present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned task offloading method for zero-trust satellite networks.

[0053] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned task offloading method for a zero-trust satellite network is implemented.

[0054] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0055] In the present invention, the model parameters of the low-orbit satellite are voted on by other low-orbit satellites, and the model parameters are modified through a voting mechanism and a cold start mechanism based on the voting results between the low-orbit satellites. The model parameters of each low-orbit satellite are dynamically adjusted, which can promote model convergence and ensure the consistency of the global model parameters. Voting on the model parameters of the low-orbit satellite by other low-orbit satellites is equivalent to considering the consensus problem of each low-orbit satellite in the communication network. In addition, the model parameters are modified through a voting mechanism and a cold start mechanism based on the voting results between the low-orbit satellites, which can reduce the impact of malicious attack satellites on the global model. In this way, the computing task offloading strategy of the low-orbit satellite is calculated based on the global model parameters after global aggregation, thereby improving the accuracy of computing task offloading. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0057] Figure 1 A flowchart of a task offloading method for a zero-trust satellite network provided by the present invention;

[0058] Figure 2 A schematic diagram of a scenario applicable to a task offloading method for a zero-trust satellite network provided by the present invention;

[0059] Figure 3 A flowchart of a LEO satellite initiating synchronization request verification provided by the present invention;

[0060] Figure 4 A flowchart of a single-round training of the BCSA-FRL model provided by the present invention;

[0061] Figure 5 A schematic diagram of the CCVM and CSRA mechanism provided by the present invention;

[0062] Figure 6 A comparison chart of reward values ​​with and without the CCVM mechanism provided by the present invention;

[0063] Figure 7 A comparison chart of rewards under replay buffer poisoning under different federated reinforcement learning methods provided by the present invention;

[0064] Figure 8 A comparison chart of rewards under different federated reinforcement learning methods provided by the present invention that are affected by model parameter poisoning;

[0065] Figure 9A performance comparison chart showing the change in packet loss rate with the proportion of malicious satellites under different federated reinforcement learning methods provided by the present invention;

[0066] Figure 10 A performance comparison chart showing the average task processing delay as the proportion of malicious satellites changes under different federated reinforcement learning methods provided by the present invention;

[0067] Figure 11 A performance comparison diagram showing the change in packet loss rate with the number of task loads under different task offloading decisions provided by the present invention;

[0068] Figure 12 A performance comparison chart showing the average task processing delay as a function of the number of task loads under different task offloading decisions provided by the present invention;

[0069] Figure 13 A schematic diagram of a task offloading device for zero-trust satellite networks provided by the present invention;

[0070] Figure 14 A schematic diagram of a computer device for implementing a task offloading method for a zero-trust satellite network provided by the present invention.

[0071] Description of reference numerals:

[0072] 101. Low-orbit satellite; 102. Ground users. DETAILED DESCRIPTION

[0073] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0074] Existing approaches to task offloading in LEO satellite networks primarily model the problem as a Markov decision problem, leveraging deep reinforcement learning and other techniques to build highly robust artificial intelligence (AI) neural networks to address the frequent topological changes, varying deployment altitudes, and diverse QoS requirements for ground services. However, deep reinforcement learning requires unified training on user-uploaded data, neglecting user data privacy. To protect private data during AI model training, federated reinforcement learning (FRL), a method that combines federated learning with deep reinforcement learning, protects user data privacy by transmitting only model parameters during training, rather than specific data.

[0075] However, there are several urgent issues to be addressed in offloading tasks in LEO satellite networks. First, the trust issue between distributed LEO satellites remains a huge challenge: service providers may choose LEO satellites launched by different companies to minimize costs and improve QoS, which leads to potential trust issues between different LEO satellites and, in turn, security risks. Malicious voting attacks launched by malicious satellites during the verification process also seriously hinder the security consensus of LEO satellites. In this Zero Trust (ZT) architecture, the traditional network security boundary that assumes inherent trust between devices within the preset network boundary no longer makes sense. Therefore, it is crucial to propose a continuous and robust authentication, authorization, and attack detection solution. Secondly, in this zero-trust architecture, federated reinforcement learning also has drawbacks: since the zero-trust architecture lacks a central reputation evaluation system, it cannot guide the federated learning aggregation model parameters, and it is difficult for each LEO satellite to reach a secure consensus by evaluating reputation in a distributed manner; network attacks such as data poisoning and malicious models against FRL further deteriorate the credibility between LEO satellites, and existing technologies are difficult to defend against the above attacks; for the zero-trust architecture, blockchain technology can use distributed ledgers to ensure the transparency and traceability of historical behavior, and use smart contracts for voting consensus. However, in the federated learning environment, smart contracts can only bipolarly distinguish between malicious models and normal models, and the contribution of the sub-model that has just recovered from the attack is significantly smaller than that of other continuously credible models, requiring additional time for regular contributions, and commonly used smart contracts cannot defend against malicious voting.

[0076] Based on this, the present invention provides a task offloading method for zero-trust satellite networks, which weights the local model according to the voting results of blockchain participants, promotes model convergence and reduces the impact of malicious attack satellites on the global model; secondly, the invention can dynamically adjust the model parameters of each low-orbit satellite and update the global model parameters to ensure the consistency of the global model during persistent and continuous malicious satellite attacks; finally, the invention comprehensively considers the security consensus problem between heterogeneous low-orbit satellites, the impact of malicious attack satellites on the global model and the QoS requirements of services and formulates a flexible task offloading strategy to solve the zero-trust problem of distributed low-orbit satellites and the problem that federated learning is vulnerable to malicious network attacks, and provide high-quality, high-stability and customized communication transmission services for users in remote or harsh areas.

[0077] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0078] In an exemplary embodiment, Figure 1 As shown, Figure 1 The following is a flowchart of a task offloading method for a zero-trust satellite network in the present invention, which specifically includes the following steps:

[0079] S101, when a low-orbit satellite receives a computing task, obtain model parameters of a model used by the low-orbit satellite to determine a computing task offloading strategy at the current moment; the low-orbit satellite is any low-orbit satellite in the communication network.

[0080] The communication network is a network formed by low-orbit satellites and other low-orbit satellites that can be accessed within the communication range of the low-orbit satellite. Figure 2 As shown, the ground user 102 offloads the computing task to the low-orbit satellite 101, and the low-orbit satellite 101 makes intelligent offloading decisions on the computing task through the model.

[0081] Each low-orbit satellite corresponds to a model, which is used to calculate the task offloading strategy, and the model parameters are the parameters of the model.

[0082] S102, sending model parameters to other low-orbit satellites in the communication network, so that other low-orbit satellites vote for the low-orbit satellite based on the experience data of other low-orbit satellites and the model parameters of the low-orbit satellite; the experience data is the data exchanged between other low-orbit satellites and the low-orbit satellites in the communication network.

[0083] Among them, voting on low-orbit satellites is carried out through blockchain smart contracts.

[0084] In an exemplary embodiment, the experience data of other low-orbit satellites at the current moment is obtained, including: obtaining the state space of other low-orbit satellites at the current moment based on the positions of other low-orbit satellites at the current moment, and calculating the reward value of other low-orbit satellites at the current moment; analyzing the state space based on the local model of other low-orbit satellites to determine the action space of other low-orbit satellites at the next moment; performing state transfer on other low-orbit satellites according to the action space to obtain the state space of other low-orbit satellites at the next moment; and determining the state space, action space, reward value of other low-orbit satellites at the current moment and the state space of other low-orbit satellites at the next moment as experience data.

[0085] All low-orbit satellites can be modeled in the satellite orbit simulation software (Satellite Tool Kit, STK) to obtain the low-orbit satellite s at each moment. k coordinate And build the network topology of low-orbit satellites according to the coordinates of low-orbit satellites. After obtaining the network topology, initialize the low-orbit satellites s k ∈{s1,…,s k ,…,s K} and global model parameters; initialize the position-related parameters of the low-orbit satellite; the system time node is recorded as t∈{1,...,t,...,T}, and the state space at time t is recorded as The action space is denoted as The reward value is recorded as The verification experience buffer is denoted as The verification buffer stores the experience data; the task set is represented as C = {c1,...,c i ,...,c I}.

[0086] Use other low-orbit satellites k' For example, other low-orbit satellites s k' The expression of the state space at time t is:

[0087]

[0088] Where D is the distance between every two low-orbit satellites The set of L(t,s k' ) are other low-orbit satellites k' The calculated load at time t, For other low-orbit satellites k' To low-orbit satellites k The transmission rate, For other low-orbit satellites k' The required computing resources, is the delay threshold.

[0089] Optionally, the state space Transmission rate The specific derivation process is as follows:

[0090] The communication link between LEO satellites can be approximated as propagating in free space, and its free space path loss coefficient PL F The calculation expression is as follows:

[0091]

[0092] Among them, G T is the LEO satellite transmitting antenna gain, G R is the LEO satellite receiving antenna gain, λ is the wavelength of the signal, Is launching satellites k' and receive satellites k The distance between them, L is the system loss coefficient.

[0093] In addition, in other practical environments, the average received signal power decreases logarithmically with distance, so the free space path loss coefficient PL LD The calculation expression is as follows:

[0094]

[0095] Where d0 is the reference distance and n is the environmental correlation coefficient.

[0096] At the same time, the path loss also depends on the surrounding environment between the transmitting satellite and the receiving satellite. Therefore, in order to simulate the path loss in the actual communication environment, a Gaussian random variable X with a mean of 0 and a standard deviation of σ is used. σ Further description of the free space path loss coefficient PL NOR , its calculation expression is as follows:

[0097]

[0098] Therefore, the transmission rate between satellite links The calculation expression is as follows:

[0099]

[0100] in, is the channel bandwidth, LEO satellites k' The signal transmission power, is the spatial noise power.

[0101] Optionally, in state space, the actual delay of the task It consists of three parts: transmission delay, waiting delay, and calculation delay. Its calculation expression is as follows:

[0102]

[0103] in, is the computation task c i The size of α is a binary factor with a value range of [0,1], indicating local processing or forwarding to other satellites for processing. is the queuing delay time that the task waits in the queue, is the computation task c i The required computing resources, Other low-orbit satellites k' computing power.

[0104] Other computing tasks exist in other low-orbit satellites c i' When the queue delay The calculation expression is as follows:

[0105]

[0106] Among them, in the calculation task c i' Timeout case β i' The value is 0. If there is no timeout, the value is 1.

[0107] Optionally, the reward values ​​of other low-orbit satellites at the current moment are calculated, including: obtaining the number of successful tasks successfully processed and the number of failed tasks failed to be processed by other low-orbit satellites within a preset time period; determining the active reward value and the penalty value based on the number of successful tasks and the number of failed tasks; and determining the difference between the active reward value and the penalty value as the reward value of the other low-orbit satellites.

[0108] The preset duration represents the interval duration ΔT, which is based on other low-orbit satellites s k' Take time t as an example to illustrate, other low-orbit satellites s k' The reward function at time t It is the difference between the number of tasks that were successfully processed and the number of tasks that failed to be processed within the time interval ΔT. Its calculation expression is as follows:

[0109]

[0110] in, represents the number of computing tasks successfully processed within ΔT, represents the number of computing tasks that failed to be processed within ΔT, r t,+ represents the active reward value for successful processing within ΔT, r t,- Indicates the penalty value for processing failure within ΔT.

[0111] Analyze the state space according to the model of other low-orbit satellites to determine the action space of other low-orbit satellites at the next moment, including: inputting the state space of other low-orbit satellites into the model corresponding to other low-orbit satellites, and obtaining the action space of other low-orbit satellites at the next moment output by the model; wherein the model can be a federated learning model, with other low-orbit satellites s k' The action space at time t For example, the action space The expression is:

[0112]

[0113] Among them, the action space Other low-orbit satellites k The set of offloading destinations that can be reached within two hops; that is, the low-orbit satellites to which the computing task can be offloaded within two hops. For example, if Other low-orbit satellites k' The computing task can be offloaded to any low-orbit satellite among s1, s5, and s6, and k' It is reachable to s1, s5, and s6 in two hops.

[0114] The state of the low-orbit satellite is transferred according to the action space to obtain the state space of other low-orbit satellites at the next moment, including: selecting a computing task unloading destination (other low-orbit satellite) of the other low-orbit satellite from the action space, then unloading the computing task to the corresponding destination, and then observing the state of the low-orbit satellite to obtain the state space of the other low-orbit satellite at the next moment; wherein, selecting the computing task unloading destination of the other low-orbit satellite from the action space includes: randomly selecting a low-orbit satellite from the action space as the computing task unloading destination.

[0115] Finally, the state space, action space, reward value of other low-orbit satellites at the current moment and the state space of other low-orbit satellites at the next moment are determined as empirical data

[0116] When a low-orbit satellite receives a computing task, the low-orbit satellite may send the model parameters of the low-orbit satellite to other low-orbit satellites, where the other low-orbit satellites here may be all low-orbit satellites in the communication network except the low-orbit satellite.

[0117] After receiving the model parameters sent by the low-orbit satellite, other low-orbit satellites may vote for the low-orbit satellite based on the model parameters. Optionally, other low-orbit satellites vote for the low-orbit satellite based on the empirical data of other low-orbit satellites and the model parameters of the low-orbit satellite, including: calculating the loss rate between the model of other low-orbit satellites and the model of the low-orbit satellite based on the model parameters and empirical data of other low-orbit satellites and the model parameters of the low-orbit satellite; if the loss rate is greater than the loss tolerance threshold, determining that the vote result of the other low-orbit satellites on the low-orbit satellite is in favor; if the loss rate is less than or equal to the loss tolerance threshold, determining that the vote result of the other low-orbit satellites on the low-orbit satellite is against.

[0118] Among them, the loss rate between the models of other low-orbit satellites and the model of the low-orbit satellite is calculated based on the model parameters and empirical data of other low-orbit satellites, as well as the model parameters of the low-orbit satellite, including: calculating the arrival target time difference of the models of other low-orbit satellites based on the empirical data of other low-orbit satellites, and calculating the arrival target time difference of the model of the low-orbit satellite based on the model parameters of the low-orbit satellite; and determining the ratio of the arrival target time difference of the model of other low-orbit satellites to the arrival target time difference of the model of the low-orbit satellite as the loss rate between the models of other low-orbit satellites and the model of the low-orbit satellite.

[0119] Optionally, the time difference to the target is calculated as:

[0120]

[0121] Where γ is the discount factor, are model parameters, It is a low-orbit satellite k In the state space Next execution action space The model parameters are Q value when ; Q value represents the model parameter In the state space Execution Action Space The expected value of the reward obtained.

[0122] Among them, the action space There may be multiple uninstall results, It represents the Q value corresponding to the maximum Q value of a certain offloading result in the execution action space.

[0123] It should be noted that after receiving the model parameters sent by the low-orbit satellite, other low-orbit satellites can calculate the action space and reward value in the state space through the model parameters of the low-orbit satellite, and determine the arrival target time difference of the low-orbit satellite model based on the action space and reward value calculated by the model parameters of the low-orbit satellite.

[0124] The low-orbit satellite s can be calculated according to the above formulas (12)-(14) k The arrival time difference of the model is calculated by the same principle for other low-orbit satellites s k' The arrival time difference of the model.

[0125] In addition, other low-orbit satellites Model and low-orbit satellite The loss rate between the models Its calculation expression is as follows:

[0126]

[0127] S103: Determine the pre-commitment status of the low-orbit satellite according to the voting results of other low-orbit satellites on the low-orbit satellite.

[0128] Preferably, the pre-submission status of the low-orbit satellite is determined based on the voting results of other low-orbit satellites on the low-orbit satellite, including: obtaining the number of other low-orbit satellites that vote in favor of the low-orbit satellite; determining the ratio of the number of votes in favor to the total number of other low-orbit satellites as the verification ratio; if the verification ratio is greater than a preset verification threshold, determining that the status of the low-orbit satellite is a pre-submission status.

[0129] Low Earth Orbit Satellites k Verification ratio The calculation formula can be:

[0130]

[0131] Among them, Number(V T ) is the number of votes received that are in favor, Number(V F ) is the number of votes received with a negative result, and the sum of the number of votes in favor and against is the total number of other low-orbit satellites.

[0132] If the verification ratio is greater than a preset verification threshold, the state of the low-orbit satellite is determined to be set to the pre-submission state, otherwise, the low-orbit satellite does not participate in the global aggregation process. The preset verification threshold may be 50%.

[0133] S104: When the ratio between the number of low-orbit satellites in the pre-submission state in the communication network and the total number of low-orbit satellites in the communication network is greater than a preset ratio threshold, the model parameters of the low-orbit satellites in the pre-submission state are corrected through a voting mechanism and a cold start mechanism, and global aggregation is performed based on the corrected model parameters to obtain global model parameters.

[0134] The voting results of LEO satellites stored in the blockchain are obtained, and the ratio of LEO satellites currently in the pre-submitted state to all LEO satellites is calculated to determine whether to enter the global parameter aggregation phase. Based on the voting results of LEO satellites on the remaining satellites, the CCVM mechanism is used to correct the model parameters of the local model. A CSRA scheme is established to consider the historical accumulated reputation of LEO satellites and correct the model parameters of their local models.

[0135] When the number of low-orbit satellites in the communication network that are in the pre-committed state is less than or equal to a preset ratio threshold, the model parameters of the local models of all low-orbit satellites are backtracked to the global model parameters trained in the previous round. When the number of low-orbit satellites in the communication network that are in the pre-committed state is greater than the preset ratio threshold, the model parameters of the low-orbit satellites in the pre-committed state are corrected, and global aggregation is performed based on the corrected model parameters to obtain the global model parameters.

[0136] Optionally, the model parameters of the low-orbit satellite in the pre-submission state are corrected through a voting mechanism and a cold start mechanism, including the following steps: for any low-orbit satellite in the pre-submission state, obtain a first number of objections from other low-orbit satellites that voted against the low-orbit satellite, and a second number of objections from low-orbit satellites that voted against other low-orbit satellites; determine constraint correction voting mechanism parameters based on the first number of objections and the second number of objections; determine cold start reputation aggregation parameters based on the accumulated historical reputation and reputation threshold parameters of the low-orbit satellite; the accumulated historical reputation represents the number of consecutive rounds of global aggregation in which the low-orbit satellite successfully participated; and correct the model parameters of the low-orbit satellite based on the constraint correction voting mechanism parameters and the cold start reputation aggregation parameters to obtain corrected model parameters.

[0137] The local model parameters can be corrected according to the Constrained Correction Voting Mechanism (CCVM) and Cold Start Reputation Aggregation (CSRA) scheme, and the federated learning model can be globally aggregated to adaptively make intelligent offloading decisions for computing tasks in low-orbit satellite networks.

[0138] Specifically, malicious voting attack satellites can continuously vote against other satellites, hindering model convergence and interfering with consensus. In order to limit the impact of malicious voting attack satellites on the global model, by calculating any low-orbit satellite s k The voting results of other low-orbit satellites are used to obtain the constraint correction voting mechanism parameters of the low-orbit satellite. Its calculation expression is as follows:

[0139]

[0140] Where K is the total number of low-orbit satellites, The first number of objections to other low-orbit satellites whose voting results are against the low-orbit satellites, The second number of objections to other low-orbit satellites whose voting results are against them, Indicates that except low-orbit satellites s k For other low-orbit satellites other than , sigmoid is a function that maps values ​​to [0,1], and its calculation expression is:

[0141]

[0142] In the constraint correction voting mechanism, the penalty for satellites that cannot pre-submit is to prevent them from participating in the current round of global aggregation. However, the low-orbit satellite may recover in the future. In order to avoid long-term impact in the future, the CSRA mechanism is established to use low-orbit satellites to k Historical voting records to obtain the cold start reputation aggregation parameters of the low-orbit satellite Its calculation expression is as follows:

[0143]

[0144] in, It is a low-orbit satellite k The cumulative historical reputation of the satellite represents the number of consecutive rounds of global aggregation that the satellite successfully participated in, H Rep is the reputation threshold parameter.

[0145] Get CCVM correction parameters and CSRA correction parameters After that, the low-orbit satellite s k Model parameters of the model The calculation expression is as follows:

[0146]

[0147] in, Indicates low-orbit satellite s k The model parameters of the model, represents the modified model parameters.

[0148] Performing global aggregation based on the modified model parameters to obtain global model parameters includes: performing weighted average on each modified model parameter to obtain global model parameters. Among them, the global model parameter θ K The calculation expression is as follows:

[0149]

[0150] Where M is the number of low-orbit satellites in the pre-commitment state.

[0151] After obtaining the global model parameters, the global model parameters are recorded in the blockchain.

[0152] S105 , using the global model parameters as the model parameters of each low-orbit satellite model, and calculating the task offloading strategy for the corresponding low-orbit satellite using the model with updated parameters.

[0153] In an exemplary embodiment, the present invention further provides a method for offloading tasks in a zero-trust satellite network, the method comprising the following steps:

[0154] S301: Use satellite orbit simulation software to simulate the spatial position of the LEO satellite and construct a network topology map of the LEO satellite based on the coordinate parameters obtained from the simulation. The blockchain-based smart contract mechanism proposed in this invention is used to complete the synchronization consensus step of the LEO satellite.

[0155] After obtaining the LEO satellite network topology, initialize the LEO satellite s k ∈{s1,…,s k ,…,s K} and global model parameters; initialize the position-related parameters of the LEO satellite; the system time node is recorded as t∈{1,...,t,...,T}, and the state space at time t is recorded as The action space is denoted as The reward function is recorded as The verification experience buffer is denoted as The task set is represented as C = {c1,...,c i ,...,c I Before each round of algorithm operation, the voting result graph V is initialized. The graph marks whether the LEO satellite has voted against or in favor of any LEO satellite. If no LEO satellite has voted, the voting result is recorded as empty. For each LEO satellite, a verification request is sent to all other LEO satellites, and then the verification experience buffer is used to check the LEO satellites. Get verification data Calculate the time difference (TD) of the verification data in the local model of the current LEO satellite and use it as the evaluation index of the verification; the current satellite uses the verification data Calculate the arrival time difference TD of the local model of the LEO satellite to be voted; calculate the loss rate of the two local models, and vote in favor or against based on the loss tolerance threshold E; record the voting results of each LEO satellite and upload them to the blockchain for storage.

[0156] Specifically, the operation steps of the LEO satellite network topology simulation and blockchain-based synchronous consensus in S301 are as follows:

[0157] S1, initialize the global time t = 0, model all LEO satellites in the satellite orbit simulation software STK, and obtain the LEO satellite s at each time k coordinate And calculate the distance between each LEO satellite based on the coordinates

[0158] S2, initialize voting result graph Initialize LEO satellite state space Action Space Reward Function and verification experience buffer

[0159] S3, select a LEO satellite that has not yet voted k , and vote for all remaining satellites in turn, and record the voting results in the set V of the blockchain, such as Figure 3 As shown, Figure 3 Flowchart for initiating synchronization request verification for a LEO satellite.

[0160] Specifically, LEO satellites k Select a satellite that has not yet been verified k' , from the verification experience buffer Get a batch of verification data in Pointing to the state Select Action to enter the state; LEO satellites k Calculate the time difference of arrival TD of the local model, LEO satellites k' The arrival time difference TD of the local model is calculated in the same way to obtain the loss rate like If the threshold E is exceeded, the LEO satellite s k For LEO satellites k' Vote Yes T , otherwise vote against V F ; Determine the current LEO satellites k Have all remaining satellites been voted for? If so, go to S4; otherwise, continue to execute S3.

[0161] S4, judging whether the current voting result map records the voting results of all LEO satellites, if so, proceeding to S5, otherwise proceeding to S3.

[0162] S5, upload the voting result graph V to the blockchain for storage. Each LEO satellite obtains the voting result record graph V. If other LEO satellites k The verification success rate exceeds 50%, and LEO satellites k The status of is set to pre-submit status, otherwise it does not participate in the global parameter aggregation process.

[0163] S301 uses CCVM and CSRA to correct local model parameters, performs global aggregation on the federated reinforcement learning model, and adaptively makes intelligent offloading decisions on computing tasks in the LEO satellite network through global model parameters.

[0164] Specifically, the satellite network voting results stored in the blockchain are obtained, and the ratio of the current pre-submitted LEO satellites to all LEO satellites is calculated to determine whether to enter the global parameter aggregation stage. k Based on the voting results of the remaining satellites, the CCVM mechanism is used to modify the model parameters of the local model. k The task offloading method for zero-trust satellite network of the present invention includes a cold start model aggregation task offloading scheme based on federated reinforcement learning (Blockchain-enabled ColdStart Aggregation Federated Reinforcement Learning, BCSA-FRL), such as Figure 4 As shown, Figure 4 This is a flowchart of a single-round training of the BCSA-FRL model. Using the BCSA-FRL framework, the model parameters are globally aggregated and recorded in the blockchain. Specifically, it includes the following steps:

[0165] S401, determining whether all LEO satellites have completed verification. If all LEO satellites have completed verification, executing step S402, otherwise executing step S405.

[0166] S402: Determine whether the proportion of LEO satellites in the pre-submission state is greater than 50%. If so, correct the model parameters of the local model of each LEO satellite in the pre-submission state, perform global parameter distance, and calculate the global model parameters. Otherwise, return the parameters of all local models to the global model parameters of the previous round of training.

[0167] S403: The LEO satellite initiates a model synchronization request and broadcasts the local model.

[0168] S404: The remaining LEO satellites perform local verification and broadcast their verification results.

[0169] S405 , determining whether the LEO satellite has received more than half of the successful verification results. If so, the LEO satellite enters a pre-submission state. Otherwise, the LEO satellite is marked as an abnormal node.

[0170] Specifically, the specific steps of determining whether to perform global parameter aggregation in S302 are:

[0171] S6 calculates the ratio of the current pre-submitted LEO satellites to all LEO satellites. If the ratio exceeds 50%, proceed to S7. Otherwise, all local model parameters are backtracked to the global model parameters of the previous round of training and proceed to S3.

[0172] Specifically, the specific process of global parameter aggregation in S302 is as follows:

[0173] S7, calculate LEO satellites k Constraints modify voting mechanism parameters and cold start reputation aggregation parameters and correct LEO satellites k Model parameters like Figure 5 As shown, Figure 5 Schematic diagram of the CCVM and CSRA mechanisms.

[0174] S8, based on the corrected model parameters of all LEO satellites, perform global aggregation to obtain the global model parameters And submit the global model parameters to the blockchain.

[0175] S9, determine whether the training is completed, if it is completed, end the process, if not, go to S3.

[0176] In an exemplary embodiment, without loss of generality, a simulation system for a task offloading method for a zero-trust satellite network is assigned in this embodiment. This embodiment considers the existence of multiple areas on the ground, each area containing multiple terminals as task creators. After a ground terminal creates a task, it will send a task calculation request to any LEO satellite based on its location. At any time point t, the number of calculation tasks is [200, 400], with a step size of 100, and their probability follows a Poisson distribution with a parameter of 100. The size of the calculation task file follows a standard normal distribution with a mean of 15MB. In the space layer, there is a total of 1 LEO satellite orbit, each orbit has 15 LEO satellites, its altitude is 800km, its computing power is [1, 4]GB, its channel bandwidth is 2MHz, its transmit power is 150W, the environmental correlation coefficient is 2, the transmit antenna gain and the receive antenna gain are both 1, the system loss coefficient is 1, and the spatial noise power is 1W. The learning rate in the neural network is 0.001, the exploration decay rate is 0.95, the batch size of the experience replay pool is 32, and the batch size of the verification experience pool is 512.

[0177] The present invention provides a task offloading method for zero-trust satellite networks. First, it reaches a consensus in distributed LEO satellites through smart contracts of blockchain, and realizes the transition of the network from one consistency state to another consistency state under the premise of ensuring that all files are globally accessible in the LEO satellite network. Secondly, after reaching a consensus between satellites, the present invention further proposes CCVM and CSRA schemes, which not only consider the existence of malicious attack satellites in the satellite network, and correct the influence of the local model of the satellite on the global model by the success or failure of the verification of a certain satellite by other satellites, but also consider the process of the malicious attack satellite gradually changing from the attack state to the normal state, and dynamically adjust the influence of any satellite on the global model by evaluating its historical reputation, thereby improving the robustness of the global model and accelerating the speed of model convergence. Finally, the present invention not only considers the collaborative work between heterogeneous LEO satellites, but also flexibly offloads tasks to LEO satellites with different computing capabilities according to the needs of ground users, thereby reducing network packet loss rate and network delay, and providing customized QoS for ground users. Figure 6 As shown, Figure 6 This is a comparison chart of rewards when using or not using the CCVM mechanism, Figure 6 It can be seen that the reward value of the BCSA-FRL algorithm of the present invention can converge to the optimal value of 25 when the CCVM mechanism is in place, and its defense performance is significantly better than the reward value of 10 without the CCVM mechanism. Figure 7 and Figure 8 As shown, Figure 7 Comparison of rewards under replay buffer poisoning under different federated reinforcement learning methods. Figure 8Comparison of rewards under different federated reinforcement learning methods with model parameter poisoning, Figure 7 and Figure 8 It can be concluded that the task offloading method for zero-trust satellite networks of the present invention can improve the reward value by 100% to reach the optimal value of 26 in the replay buffer poisoning attack compared with the conventional federated reinforcement learning algorithm (FedAvg-FRL); in the model poisoning attack, its reward value can be improved by 50.29% to reach the optimal value of 26, and converges faster. Figure 9 and Figure 10 As shown, Figure 9 This is a performance comparison chart of packet loss rate as the proportion of malicious satellites changes under different federated reinforcement learning methods. Figure 10 The performance comparison chart of average task processing delay under different federated reinforcement learning methods as the proportion of malicious satellites changes. Figure 9 and Figure 10 It can be concluded that when the proportion of malicious satellites is less than 50%, the packet loss rate can be reduced by 8.75% to an average of 5%, and the average processing delay can be reduced by 2.8ms to an average of 6ms. Figure 11 and Figure 12 As shown, Figure 11 This is a performance comparison chart of packet loss rate as the number of task loads changes under different task offloading decisions. Figure 12 The performance comparison chart of average task processing delay as the number of task loads changes under different task offloading decisions is shown in the figure. Figure 11 and Figure 12 It can be concluded that the task offloading method for zero-trust satellite networks of the present invention can reduce the packet loss rate by an average of 13.89% and 34.18% compared with the traditional task offloading algorithms, the average task burden algorithm (Avg Task Burden) and the random algorithm (Random), when the task load is low, and the average processing delay can be reduced by 1.45ms and 3.35ms, reaching an average level below 5.95ms; when the task load is high, the packet loss rate and the average task processing delay do not increase significantly, and the packet loss rate can be reduced by an average of 13.29% and 38.81%, reaching an average level below 8.29%, and the average processing delay can be reduced by 2.27ms and 3.88ms, reaching an average level below 6.08ms.

[0178] The present invention provides a task offloading method for zero-trust satellite networks. First, voting consensus is achieved through smart contracts on the blockchain to ensure the transparency and traceability of historical behaviors. Secondly, the present invention further proposes a CCVM scheme, which can correct the impact of malicious voting satellites and evaluate the credibility of uploaded sub-models, thereby ensuring the trust between distributed satellites. In addition, the present invention proposes a CSRA scheme, which can severely reduce the reputation of the attacking participant when an attack is detected, and gradually restore the malicious attacking satellite to a normal state by taking into account the cleanup time of the memory replay buffer, thereby ensuring the normal convergence of the model. Finally, the BCSA-FRL scheme of the present invention takes into account the synergy between distributed satellites and flexibly offloads tasks to different LEO satellites according to the needs of ground users, thereby ensuring the efficiency and security of the task offloading strategy.

[0179] When applying the task offloading method for zero-trust satellite network provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0180] The above is a task offloading method for a zero-trust satellite network provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding task offloading device for a zero-trust satellite network, such as Figure 13 shown.

[0181] Figure 13 A schematic diagram of a task offloading device for a zero-trust satellite network provided by the present invention, the device 1300 includes:

[0182] The acquisition module 1301 is configured to, when a low-orbit satellite receives a computing task, acquire model parameters of a model used by the low-orbit satellite at the current moment to determine a computing task offloading strategy; the low-orbit satellite is any low-orbit satellite in a communication network; and the communication network is a network formed by a low-orbit satellite and other low-orbit satellites that are accessible within the communication range of the low-orbit satellite.

[0183] Voting module 1302 is configured to send model parameters to other low-orbit satellites in the communication network, so that the other low-orbit satellites vote for the low-orbit satellite based on the empirical data of the other low-orbit satellites and the model parameters of the low-orbit satellite; the empirical data is data exchanged between the other low-orbit satellites and the low-orbit satellites in the communication network;

[0184] A determination module 1303 is configured to determine a pre-commitment status of the low-orbit satellite based on voting results of other low-orbit satellites on the low-orbit satellite;

[0185] an obtaining module 1304 for correcting the model parameters of each low-orbit satellite using a voting mechanism and a cold start mechanism when a ratio between the number of low-orbit satellites in a pre-committed state in the communication network and the total number of low-orbit satellites in the communication network is greater than a preset ratio threshold, and performing global aggregation based on the corrected model parameters to obtain global model parameters;

[0186] The calculation module 1305 is configured to use the global model parameters as the model parameters of each low-orbit satellite model, and calculate the task offloading strategy for the corresponding low-orbit satellite using the model with updated parameters.

[0187] For the specific definition of the task offloading device for zero-trust satellite networks, please refer to the definition of the task offloading method for zero-trust satellite networks above, which will not be repeated here. The various modules in the above-mentioned task offloading device for zero-trust satellite networks can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0188] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 Provided is a task offloading method for zero-trust satellite networks.

[0189] The present invention also provides Figure 14 The structural diagram of the computer equipment shown in FIG. Figure 14 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Provided is a task offloading method for zero-trust satellite networks.

[0190] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0191] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A task offloading method for zero-trust satellite networks, characterized in that: include: When a low-orbit satellite receives a computing task, obtaining model parameters of a model used by the low-orbit satellite at the current moment to determine a computing task offloading strategy; the low-orbit satellite is any low-orbit satellite in a communication network; and the communication network is a network formed by the low-orbit satellite and other low-orbit satellites accessible within a communication range of the low-orbit satellite. sending the model parameters to other low-orbit satellites in the communication network, so that the other low-orbit satellites vote for the low-orbit satellite based on the empirical data of the other low-orbit satellites and the model parameters of the low-orbit satellite; the empirical data is data exchanged between the other low-orbit satellites and the low-orbit satellites in the communication network; Acquiring the experience data of the other low-orbit satellites at the current moment, including: obtaining a state space of the other low-orbit satellites at the current moment based on the positions of the other low-orbit satellites at the current moment, and calculating a reward value of the other low-orbit satellites at the current moment; analyzing the state space based on a local model of the other low-orbit satellite to determine an action space of the other low-orbit satellite at a next moment; performing a state transfer on the other low-orbit satellite based on the action space to obtain a state space of the other low-orbit satellite at the next moment; and determining the state space of the other low-orbit satellites at the current moment, the action space, the reward value, and the state space of the other low-orbit satellites at the next moment as the experience data; The other low-orbit satellites exist t The expression of the state space at time is: ； in, D The distance between each two low-orbit satellites A collection of For other low-orbit satellites At the moment t The computational load, For other low-orbit satellites To low-orbit satellite The transmission rate, For other low-orbit satellites The required computing resources, is the delay threshold; Determining a pre-commitment status of the low-orbit satellite according to voting results of the other low-orbit satellites on the low-orbit satellite; When a ratio between the number of low-orbit satellites in a pre-submission state in the communication network and the total number of low-orbit satellites in the communication network is greater than a preset ratio threshold, the model parameters of the low-orbit satellites in the pre-submission state are modified through a voting mechanism and a cold start mechanism, and global aggregation is performed based on the modified model parameters to obtain global model parameters; The global model parameters are used as model parameters of the models of the low-orbit satellites respectively, and the task offloading strategy of the corresponding low-orbit satellite is calculated through the model with updated parameters.

2. The method according to claim 1, characterized in that Calculating the reward value of the other low-orbit satellites at the current moment includes: Obtaining the number of successful missions and the number of failed missions processed successfully by the other low-orbit satellites within a preset time period; Determining an active reward value and a penalty value based on the number of successful tasks and the number of failed tasks; A difference between the active reward value and the penalty value is determined as a reward value for the other low-orbit satellite.

3. The method according to claim 1, characterized in that The voting for the low-orbit satellite according to the empirical data of the other low-orbit satellites and the model parameters of the low-orbit satellite comprises: Calculating a loss rate between the model of the other low-orbit satellite and the model of the low-orbit satellite based on the model parameters and empirical data of the other low-orbit satellites and the model parameters of the low-orbit satellite; If the loss rate is greater than a loss tolerance threshold, determining that the voting result of the other low-orbit satellites on the low-orbit satellite is in favor; If the loss rate is less than or equal to the loss tolerance threshold, it is determined that the voting result of the other low-orbit satellites on the low-orbit satellite is against.

4. The method according to claim 3, characterized in that Calculating a loss rate between the model of the other low-orbit satellite and the model of the low-orbit satellite based on the model parameters and empirical data of the other low-orbit satellites and the model parameters of the low-orbit satellite includes: Calculating the arrival time difference of the local model of the other low-orbit satellite based on the model parameters and empirical data of the other low-orbit satellite, and calculating the arrival time difference of the model of the low-orbit satellite based on the model parameters of the low-orbit satellite; The ratio of the arrival target time difference of the model of the other low-orbit satellite to the arrival target time difference of the model of the low-orbit satellite is determined as the loss rate between the model of the other low-orbit satellite and the model of the low-orbit satellite.

5. The method according to claim 4, characterized in that The calculation formula for the time difference to the target is: ； ； ； in, is the discount factor, are model parameters, It is a low-orbit satellite In the state space Next execution action space , the model parameters are Time Q value; Q Values ​​represent model parameters In the state space Execution Action Space The expected value of the reward obtained.

6. The method according to claim 3, characterized in that Determining a pre-commitment status of the low-orbit satellite according to voting results of the other low-orbit satellites on the low-orbit satellite includes: Obtaining the number of other low-orbit satellites that vote in favor of the low-orbit satellite; Determine a ratio of the number of approvals to the total number of other low-orbit satellites as a verification ratio; If the verification ratio is greater than a preset verification threshold, it is determined that the state of the low-orbit satellite is a pre-submission state.

7. The method according to claim 3, characterized in that The model parameters of low-orbit satellites in the pre-submission state are modified through voting and cold start mechanisms, including: For any low-orbit satellite in a pre-commitment state, obtain a first number of objections from other low-orbit satellites that voted against the low-orbit satellite, and a second number of objections from other low-orbit satellites that voted against the low-orbit satellite; Determining parameters of a constrained modified voting mechanism based on the first number of objections and the second number of objections; Determining a cold start reputation aggregation parameter based on the accumulated historical reputation of the low-orbit satellite and a reputation threshold parameter; the accumulated historical reputation represents the number of consecutive rounds of global aggregation in which the low-orbit satellite successfully participated; The model parameters of the low-orbit satellite are corrected according to the constraint correction voting mechanism parameters and the cold start reputation aggregation parameters to obtain corrected model parameters.

8. The method according to claim 1, characterized in that The global aggregation is performed based on the corrected model parameters to obtain the global model parameters, including: A weighted average is performed on each of the modified model parameters to obtain the global model parameters.

Images