Transmission link scheduling method for low-orbit satellite internet of things data
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANQI SATELLITE INTERNET OF THINGS TECHNOLOGY CO LTD
- Filing Date
- 2026-05-18
- Publication Date
- 2026-06-19
AI Technical Summary
Existing technologies are insufficient for scientifically constructing a low-orbit satellite IoT data transmission link scheduling model. They cannot accurately and efficiently find the optimal scheduling scheme, and the algorithm is prone to getting trapped in local optima. The iterative process has a slow convergence speed, low solution accuracy, poor stability, and cannot adapt to complex and ever-changing transmission environments.
The Asian jackal group cooperative optimization algorithm is used for transmission link scheduling. By simulating the dispersed exploration and synchronous shrinking of the encirclement behavior of scout jackals, and combining the adaptive hunting coefficient and the optimal solution fluctuation coefficient, a balance between global search and local convergence is achieved. A multi-objective balanced numerical optimization problem is constructed for resource allocation and command issuance.
It achieves precise allocation of link resources, improves link utilization efficiency and overall operational efficiency, ensures the stability and reliability of low-orbit satellite IoT data transmission, and provides a high-quality optimal transmission solution.
Smart Images
Figure CN122247494A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to satellite Internet of Things (IoT), and more specifically to a method for scheduling transmission links for low-Earth orbit (LEO) satellite IoT data. Background Technology
[0002] With the rapid development of low-Earth orbit (LEO) satellite Internet of Things (IoT), a large number of IoT terminals are transmitting data via LEO satellites, which places extremely high demands on the scheduling of transmission links. LEO satellites are characterized by their low orbit, wide coverage, and high speed, while IoT terminals are numerous and widely distributed, and their data transmission needs are diverse and dynamic.
[0003] Traditional transmission link scheduling methods often face several problems. On the one hand, the search space is difficult to determine precisely, which may prevent the scheduling scheme from covering all possible link combinations, thus affecting the comprehensiveness and effectiveness of the scheduling. On the other hand, the objective function and constraints set when constructing the transmission link scheduling model are not scientifically reasonable enough, making it difficult to accurately reflect the actual scheduling needs and limitations.
[0004] Meanwhile, when solving the transmission link scheduling model, commonly used algorithms are prone to getting trapped in local optima, failing to find the globally optimal transmission link scheduling scheme, and suffer from slow convergence speed and low solution accuracy during the iterative process. In addition, existing algorithms have poor stability and are prone to premature stalling, making them unsuitable for the complex and ever-changing transmission environment of low-Earth orbit satellite IoT.
[0005] Therefore, in order to achieve efficient and stable transmission of low-Earth orbit satellite IoT data, a transmission link scheduling method for low-Earth orbit satellite IoT data is needed. This method should be able to scientifically construct a transmission link scheduling model, possess powerful global search capabilities, quickly converge to a high-precision optimal solution, and have good stability. Summary of the Invention
[0006] (a) Technical problems to be solved
[0007] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for scheduling transmission links of low-orbit satellite Internet of Things data, which can effectively overcome the shortcomings of the existing technology, such as the difficulty in scientifically constructing a transmission link scheduling model and the inability to accurately and efficiently find the optimal transmission link scheduling scheme.
[0008] (II) Technical Solution
[0009] To achieve the above objectives, the present invention provides the following technical solution: The method for scheduling transmission links for low-Earth orbit satellite IoT data includes the following steps: S1. Determine the search space for transmission link scheduling; S2. Determine the objective function and constraints for transmission link scheduling, and construct a transmission link scheduling model; S3. The Asian jackal group cooperative optimization algorithm is used to solve the transmission link scheduling model to obtain the optimal transmission link scheduling scheme. S4. Analyze the optimal transmission link scheduling scheme, allocate link resources, and finally issue scheduling instructions to low-orbit satellites and IoT terminals; Among them, in the Asian jackal group cooperative optimization algorithm: During the global exploration phase, the behavior of scout jackals independently and dispersedly exploring without a fixed leader and randomly expanding the search domain is simulated to broaden the global search range and avoid the algorithm getting stuck in local optima. In the local development phase, by simulating the behavior of all jackals synchronously shrinking their encirclement from their current position toward the prey, gradually approaching and capturing the prey, a fine local search is performed, which quickly converges to the optimal solution and improves the accuracy of the solution. At the same time, on the one hand, by introducing an adaptive hunting coefficient, global exploration is enhanced in the early stage of iteration, and local development is enhanced in the later stage of iteration, resulting in more stable nonlinear descent. On the other hand, by introducing an optimal solution fluctuation coefficient, the prey position is randomly perturbed, preventing the algorithm from stagnating too early.
[0010] Preferably, determining the search space for transmission link scheduling in S1 includes: The transmission link scheduling scheme includes the transmission resources allocated to each IoT terminal, including carrier frequency, time slot number, spreading code channel, and transmit power. That is, each solution vector in the search space includes the transmission resources allocated to all IoT terminals, including carrier frequency, time slot number, spreading code channel, and transmit power.
[0011] Preferably, in S2, the objective function and constraints for transmission link scheduling are determined, and a transmission link scheduling model is constructed, including: S21. Determine the objective function for transmission link scheduling: Establish an objective function that balances multiple objectives, such as reducing transmission delay, alleviating link congestion, and increasing throughput, and transform transmission link scheduling into a constrained numerical optimization problem. S22. Determine the constraints for transmission link scheduling; S23. Combine the objective function and constraints of transmission link scheduling to construct a transmission link scheduling model.
[0012] Preferably, in S21, the objective function for transmission link scheduling is determined: an objective function that balances multiple objectives—reducing transmission delay, alleviating link congestion, and increasing throughput—is established, transforming transmission link scheduling into a constrained numerical optimization problem, including: The objective function F(X) for transmission link scheduling is expressed by the following formula: ; Where X is the solution vector, containing transmission resources including the carrier frequency, time slot number, spreading code channel, and transmit power allocated to all IoT terminals, and f delay (X) is the total transmission delay function, f congest (X) is the link congestion penalty function, f throughput (X) is the total throughput function. , , All are weighting coefficients, and ; The constraints for determining transmission link scheduling in S22 include: For the i-th solution vector X i =[f i1 ,f i2 ,…,f in ,…,f iN ], that is, the position of the i-th jackal in the jackal pack, where f in The transmission resources allocated to the nth IoT terminal, including carrier frequency, time slot number, spreading code channel, and transmit power, where N is the number of IoT terminals, are subject to the following constraints: 1) The i-th solution vector X i Each element represents a single IoT terminal, and the transmission resources allocated to each dimension are all within the preset range of the corresponding dimension's transmission resources. 2) The bandwidth occupied by all IoT terminals is less than the total available bandwidth of the satellite.
[0013] Preferably, in S3, the Asian jackal group cooperative optimization algorithm is used to solve the transmission link scheduling model to obtain the optimal transmission link scheduling scheme, including: S31. Randomly generate an initial population in the search space. The position of each jackal in the population corresponds to a solution vector, and initialize the algorithm parameters. S32. Implement a nonlinear adaptive stage switching mechanism to automatically switch between the global exploration stage and the local development stage according to the iteration progress, balancing global search and local convergence. S33. In the global exploration phase, by simulating the behavior of reconnaissance jackals independently and dispersedly exploring without a fixed leader and randomly expanding the search domain, the global search range is expanded to avoid the algorithm getting stuck in local optima, and then proceeds to S35. S34. In the local development phase, by simulating the behavior of all jackals synchronously shrinking their encirclement from their current positions toward the prey, gradually approaching and capturing the prey, a fine local search is performed to quickly converge to the optimal solution, improving the accuracy of the solution. At the same time, on the one hand, by introducing an adaptive hunting coefficient, global exploration is enhanced in the early stage of iteration, and local development is enhanced in the later stage of iteration, resulting in more stable nonlinear descent. On the other hand, by introducing an optimal solution fluctuation coefficient, the prey position is randomly disturbed to prevent the algorithm from stagnating too early and proceeding to S35. S35. Use the objective function of transmission link scheduling to evaluate all jackals in the current population, calculate the corresponding fitness value, and record and update the historical best solution; S36. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S32. Otherwise, take the historical best solution as the optimal transmission link scheduling scheme.
[0014] Preferably, S32 implements a nonlinear adaptive stage switching mechanism, automatically switching between the global exploration stage and the local development stage according to the iteration progress, balancing global search and local convergence, including: S321. Calculate the nonlinear adaptive stage switching probability P based on the iterative progress: ; Where t is the current iteration number and T is the maximum iteration number; S322. Compare the nonlinear adaptive phase switching probability P with the random number r, and switch between the global exploration phase and the local development phase based on the comparison result: ; Where r is a uniform random number in the range of 0 to 1.
[0015] Preferably, in S33, during the global exploration phase, the global search range is expanded by simulating the behavior of reconnaissance jackals independently and dispersedly exploring without a fixed leader, randomly expanding the search domain, thus avoiding the algorithm from getting trapped in local optima. This includes: Based on the simulated behavior of reconnaissance jackals independently and dispersedly exploring, without a fixed leader, and randomly expanding their search domain, the positions of all jackals in the current population are updated: ; in, , Let ∠UB and ∠LB be the positions of the i-th jackal in the t-th and t+1-th iterations, respectively. Let UB and LB be the upper and lower bounds of the search space, respectively. Let rand(1,N) be an N-dimensional random vector with the same dimension as the solution vector. Each element in the random vector rand(1,N) follows a uniform distribution and takes values in the range [0,1]. This represents the Hadamard product, which is the element-wise product, and A is the exploration coefficient.
[0016] Preferably, in S34, during the local development phase, a refined local search is performed by simulating the behavior of all jackals synchronously shrinking their encirclement from their current positions towards the prey, gradually approaching and capturing the prey. This rapidly converges to the optimal solution, improving the accuracy of the solution. Simultaneously, on the one hand, an adaptive hunting coefficient is introduced to enhance global exploration in the early stages of iteration and local development in the later stages, resulting in more stable nonlinear descent. On the other hand, an optimal solution fluctuation coefficient is introduced to randomly perturb the prey's position, preventing the algorithm from stalling prematurely. This includes: S341. Based on the fitness values of all jackals in the current population, determine the current global optimal solution. And use it as the location of prey; S342. Based on the simulation of all jackals synchronously shrinking their encirclement towards the prey from their current positions, gradually approaching and capturing the prey, and introducing an adaptive hunting coefficient B and an optimal solution fluctuation coefficient C, the positions of all jackals in the current population are updated: ; in, This represents calculating the position of the i-th jackal at the t-th iteration. Location of prey The distance vector between the two vectors is calculated by subtracting each element from the other and taking the absolute value. B is the adaptive hunting coefficient. The nonlinearity decreases from 2 to 0 to enhance global exploration in the early stages of iteration and local development in the later stages. The decrease in nonlinearity leads to greater stability. C is the fluctuation coefficient of the optimal solution. Location of the prey Apply random perturbations to prevent the algorithm from stalling prematurely.
[0017] Preferably, in S4, the optimal transmission link scheduling scheme is analyzed, link resources are allocated, and finally scheduling instructions are issued to low-Earth orbit satellites and IoT terminals, including: S41. Parse the optimal transmission link scheduling scheme into executable satellite IoT physical layer resource configuration instructions to complete the formal allocation of IoT terminal-satellite link; S42. Encapsulate resource configuration instructions into standard signaling and send them to ground stations, low-orbit satellites, and IoT terminals.
[0018] Preferably, in S42, the resource configuration command is encapsulated into standard signaling and sent to ground stations, low-Earth orbit satellites, and IoT terminals, including: The ground station will send the parsed resource configuration instructions to the low-Earth orbit satellite via the uplink. The resource configuration instructions include the transmission resources, including the carrier frequency, time slot number, spreading code channel, and transmit power allocated to all IoT terminals. Low-Earth orbit satellites use onboard processing units to receive and store resource configuration instructions, and configure corresponding transponders, transmission beams and time slot resources according to the instructions; Low-Earth orbit satellites send resource allocation instructions to corresponding IoT terminals via downlink and provide matching transmission channels for the IoT terminals, enabling the IoT terminals to establish communication links with the satellite according to the allocated transmission resources and complete data transmission.
[0019] (III) Beneficial Effects
[0020] Compared with existing technologies, the low-orbit satellite IoT data transmission link scheduling method provided by this invention has the following advantages:
[0021] 1) Precise resource allocation to improve link utilization efficiency When determining the search space for transmission link scheduling, each solution vector is specified to include all transmission resources such as carrier frequency, time slot number, spreading code channel, and transmit power allocated to all IoT terminals. In this way, various resource combinations can be considered comprehensively and meticulously, and the link resources can be accurately allocated. For example, according to the actual needs and data transmission characteristics of different IoT terminals, the corresponding transmission resources can be reasonably allocated to them, avoiding over-allocation or under-allocation of resources, effectively improving the utilization efficiency of link resources, and ensuring the stability and reliability of low-orbit satellite IoT data transmission. 2) Optimize the scheduling process to improve overall operational efficiency. From constructing the transmission link scheduling model to solving it using the Asian jackal group collaborative optimization algorithm, and then to analyzing the optimal transmission link scheduling scheme, allocating resources, and issuing scheduling instructions, the entire process is rationally designed and the steps are clearly defined. When constructing the transmission link scheduling model, a multi-objective balance was comprehensively considered, including reducing transmission latency, alleviating link congestion, and increasing throughput. Transmission link scheduling was transformed into a constrained numerical optimization problem, making the scheduling objectives more explicit. During the model solving process, a nonlinear adaptive stage switching mechanism automatically switched between the global exploration stage and the local development stage according to the iteration progress, quickly finding the optimal solution. Finally, timely resource allocation and instruction issuance reduced the waiting time in intermediate links, significantly improving the overall operational efficiency of low-Earth orbit satellite IoT data transmission link scheduling. 3) Enhance algorithm performance and ensure the quality of scheduling schemes. The Asian jackal pack collaborative optimization algorithm simulates the independent and dispersed exploration behavior of scout jackals in the global exploration phase, expanding the global search range and avoiding getting trapped in local optima. In the local development phase, it simulates the behavior of all jackals synchronously shrinking their encirclement to capture prey, performing a refined local search to improve the accuracy of the solution. At the same time, it introduces an adaptive hunting coefficient and an optimal solution fluctuation coefficient to enhance global exploration in the early stage of iteration and local development in the later stage of iteration, while preventing the algorithm from stagnating prematurely. These characteristics enable the algorithm to accurately and efficiently solve the transmission link scheduling model and obtain a high-quality optimal transmission link scheduling scheme, providing a strong guarantee for the efficient and stable transmission of low-orbit satellite IoT data. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram illustrating the process of solving the transmission link scheduling model using the Asian jackal group cooperative optimization algorithm in this invention. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0025] The core of this invention lies in addressing the challenge of balancing multiple objectives in low-Earth orbit satellite IoT data transmission link scheduling, such as reducing transmission latency, alleviating link congestion, and increasing throughput. Traditional optimization algorithms suffer from limitations such as insufficient global search capability, susceptibility to local optima, and limited convergence speed and accuracy. Therefore, an Asian jackal pack cooperative optimization algorithm is designed to solve the transmission link scheduling model. In this algorithm: During the global exploration phase, the behavior of scout jackals independently and dispersedly exploring without a fixed leader and randomly expanding the search domain is simulated to broaden the global search range and avoid the algorithm getting stuck in local optima. In the local development phase, by simulating the behavior of all jackals synchronously shrinking their encirclement from their current position toward the prey, gradually approaching and capturing the prey, a fine local search is performed, which quickly converges to the optimal solution and improves the accuracy of the solution. At the same time, on the one hand, by introducing an adaptive hunting coefficient, global exploration is enhanced in the early stage of iteration, and local development is enhanced in the later stage of iteration, resulting in more stable nonlinear descent. On the other hand, by introducing an optimal solution fluctuation coefficient, the prey position is randomly perturbed, preventing the algorithm from stagnating too early.
[0026] In the technical solution of this application, the Asian jackal pack collaborative optimization algorithm simulates the independent and dispersed exploration behavior of scout jackals in the global exploration phase, expanding the global search range and avoiding getting trapped in local optima. In the local development phase, it simulates the behavior of all jackals synchronously shrinking their encirclement to capture prey, performing a refined local search to improve the accuracy of the solution. At the same time, it introduces an adaptive hunting coefficient and an optimal solution fluctuation coefficient to enhance global exploration in the early stage of iteration and enhance local development in the later stage of iteration, while preventing the algorithm from stagnating too early. These characteristics enable the algorithm to accurately and efficiently solve the transmission link scheduling model, ultimately obtaining a high-quality optimal transmission link scheduling scheme, and accurately completing the link allocation and command issuance between low-orbit satellites and IoT terminals.
[0027] The following describes the specific process of the low-orbit satellite IoT data transmission link scheduling method provided by this invention, using a specific example (e.g.) Figure 1 (as shown) and technical effects.
[0028] S1. Determine the search space for transmission link scheduling, including: The transmission link scheduling scheme includes the transmission resources allocated to each IoT terminal, including carrier frequency, time slot number, spreading code channel, and transmit power. That is, each solution vector in the search space includes the transmission resources allocated to all IoT terminals, including carrier frequency, time slot number, spreading code channel, and transmit power.
[0029] The above technical solution, when determining the search space for transmission link scheduling, explicitly includes the carrier frequency, time slot number, spreading code channel, transmit power, and other transmission resources allocated to all IoT terminals in each solution vector. In this way, various resource combinations can be comprehensively and meticulously considered, enabling precise allocation of link resources. For example, based on the actual needs and data transmission characteristics of different IoT terminals, corresponding transmission resources can be reasonably allocated to them, avoiding over-allocation or under-allocation of resources, effectively improving the utilization efficiency of link resources, and ensuring the stability and reliability of low-orbit satellite IoT data transmission.
[0030] S2. Determine the objective function and constraints for transmission link scheduling, and construct a transmission link scheduling model, including: S21. Determine the objective function for transmission link scheduling: Establish an objective function that balances multiple objectives, such as reducing transmission delay, alleviating link congestion, and increasing throughput. Transform transmission link scheduling into a constrained numerical optimization problem, specifically including: The objective function F(X) for transmission link scheduling is expressed by the following formula: ; Where X is the solution vector, containing transmission resources including the carrier frequency, time slot number, spreading code channel, and transmit power allocated to all IoT terminals, and f delay (X) is the total transmission delay function, f congest (X) is the link congestion penalty function, f throughput (X) is the total throughput function. , , All are weighting coefficients, and ; S22. Determine the constraints for transmission link scheduling, specifically including: For the i-th solution vector X i =[f i1 ,f i2 ,…,f in ,…,f iN ], that is, the position of the i-th jackal in the jackal pack, where f in The transmission resources allocated to the nth IoT terminal, including carrier frequency, time slot number, spreading code channel, and transmit power, where N is the number of IoT terminals, are subject to the following constraints: 1) The i-th solution vector X i Each element represents a single IoT terminal, and the transmission resources allocated to each dimension are all within the preset range of the corresponding dimension's transmission resources. 2) The bandwidth occupied by all IoT terminals is less than the total available bandwidth of the satellite; S23. Combine the objective function and constraints of transmission link scheduling to construct a transmission link scheduling model.
[0031] S3. The Asian jackal group cooperative optimization algorithm is used to solve the transmission link scheduling model to obtain the optimal transmission link scheduling scheme, such as... Figure 2 As shown, it includes: S31. Randomly generate an initial population in the search space. The position of each jackal in the population corresponds to a solution vector, and initialize the algorithm parameters. S32. Implement a nonlinear adaptive stage switching mechanism to automatically switch between the global exploration stage and the local development stage according to the iteration progress, balancing global search and local convergence. Specifically, this includes: S321. Calculate the nonlinear adaptive stage switching probability P based on the iterative progress: ; Where t is the current iteration number and T is the maximum iteration number; S322. Compare the nonlinear adaptive phase switching probability P with the random number r, and switch between the global exploration phase and the local development phase based on the comparison result: ; Where r is a uniformly random number in the range of 0 to 1; S33. In the global exploration phase, by simulating the behavior of scout jackals independently and dispersedly exploring without a fixed leader, and randomly expanding the search domain, the global search range is expanded to avoid the algorithm getting trapped in local optima (after completing this step, proceed directly to S35), specifically including: Based on the simulated behavior of reconnaissance jackals independently and dispersedly exploring, without a fixed leader, and randomly expanding their search domain, the positions of all jackals in the current population are updated: ; in, , Let ∠UB and ∠LB be the positions of the i-th jackal in the t-th and t+1-th iterations, respectively. Let UB and LB be the upper and lower bounds of the search space, respectively. Let rand(1,N) be an N-dimensional random vector with the same dimension as the solution vector. Each element in the random vector rand(1,N) follows a uniform distribution and takes values in the range [0,1]. This represents the Hadamard product, i.e., the element-wise product, where A is the exploration coefficient; S34. In the local development phase, by simulating the behavior of all jackals synchronously shrinking their encirclement from their current positions towards the prey, gradually approaching and capturing the prey, a refined local search is performed to quickly converge to the optimal solution, improving the accuracy of the solution. Simultaneously, on the one hand, by introducing an adaptive hunting coefficient, global exploration is enhanced in the early stages of iteration, and local development is enhanced in the later stages of iteration, resulting in more stable nonlinear descent. On the other hand, by introducing an optimal solution fluctuation coefficient to randomly perturb the prey's position, the algorithm is prevented from stalling prematurely (after this step, proceed directly to S35), which specifically includes: S341. Based on the fitness values of all jackals in the current population, determine the current global optimal solution. And use it as the location of prey; S342. Based on the simulation of all jackals synchronously shrinking their encirclement towards the prey from their current positions, gradually approaching and capturing the prey, and introducing an adaptive hunting coefficient B and an optimal solution fluctuation coefficient C, the positions of all jackals in the current population are updated: ; in, This represents calculating the position of the i-th jackal at the t-th iteration. Location of prey The distance vector between the two vectors is calculated by subtracting each element from the other and taking the absolute value. B is the adaptive hunting coefficient. The nonlinearity decreases from 2 to 0 to enhance global exploration in the early stages of iteration and local development in the later stages. The decrease in nonlinearity leads to greater stability. C is the fluctuation coefficient of the optimal solution. Location of the prey Apply random perturbations to prevent the algorithm from stalling prematurely; S35. Use the objective function of transmission link scheduling to evaluate all jackals in the current population, calculate the corresponding fitness value, and record and update the historical best solution; S36. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S32. Otherwise, take the historical best solution as the optimal transmission link scheduling scheme.
[0032] S4. Analyze the optimal transmission link scheduling scheme, allocate link resources, and finally issue scheduling instructions to low-Earth orbit satellites and IoT terminals, including: S41. Parse the optimal transmission link scheduling scheme into executable satellite IoT physical layer resource configuration instructions to complete the formal allocation of IoT terminal-satellite link; S42. Encapsulate resource allocation instructions into standard signaling and distribute them to ground stations, low-Earth orbit satellites, and IoT terminals, specifically including: The ground station will send the parsed resource configuration instructions to the low-Earth orbit satellite via the uplink. The resource configuration instructions include the transmission resources, including the carrier frequency, time slot number, spreading code channel, and transmit power allocated to all IoT terminals. Low-Earth orbit satellites use onboard processing units to receive and store resource configuration instructions, and configure corresponding transponders, transmission beams and time slot resources according to the instructions; Low-Earth orbit satellites send resource allocation instructions to corresponding IoT terminals via downlink and provide matching transmission channels for the IoT terminals, enabling the IoT terminals to establish communication links with the satellite according to the allocated transmission resources and complete data transmission.
[0033] In this application's technical solution, the entire process—from constructing the transmission link scheduling model, to solving it using the Asian jackal group collaborative optimization algorithm, to analyzing the optimal transmission link scheduling scheme, allocating resources, and issuing scheduling instructions—is rationally designed and clearly structured. When constructing the transmission link scheduling model, a comprehensive consideration is given to balancing multiple objectives, such as reducing transmission latency, alleviating link congestion, and increasing throughput. This transforms transmission link scheduling into a constrained numerical optimization problem, making the scheduling objectives more explicit. During the model solving process, a nonlinear adaptive stage switching mechanism automatically switches between the global exploration stage and the local development stage based on the iteration progress, quickly finding the optimal solution. Finally, timely resource allocation and instruction issuance reduce waiting time in intermediate stages, significantly improving the overall operational efficiency of low-Earth orbit satellite IoT data transmission link scheduling.
[0034] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for scheduling transmission links of low-Earth orbit satellite Internet of Things (IoT) data, characterized in that: Includes the following steps: S1. Determine the search space for transmission link scheduling; S2. Determine the objective function and constraints for transmission link scheduling, and construct a transmission link scheduling model; S3. The Asian jackal group cooperative optimization algorithm is used to solve the transmission link scheduling model to obtain the optimal transmission link scheduling scheme. S4. Analyze the optimal transmission link scheduling scheme, allocate link resources, and finally issue scheduling instructions to low-orbit satellites and IoT terminals; Among them, in the Asian jackal group cooperative optimization algorithm: During the global exploration phase, the behavior of scout jackals independently and dispersedly exploring without a fixed leader and randomly expanding the search domain is simulated to broaden the global search range and avoid the algorithm getting stuck in local optima. In the local development phase, by simulating the behavior of all jackals synchronously shrinking their encirclement from their current position toward the prey, gradually approaching and capturing the prey, a fine local search is performed, which quickly converges to the optimal solution and improves the accuracy of the solution. At the same time, on the one hand, by introducing an adaptive hunting coefficient, global exploration is enhanced in the early stage of iteration, and local development is enhanced in the later stage of iteration, resulting in more stable nonlinear descent. On the other hand, by introducing an optimal solution fluctuation coefficient, the prey position is randomly perturbed, preventing the algorithm from stagnating too early.
2. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 1, characterized in that: The search space for determining transmission link scheduling in S1 includes: The transmission link scheduling scheme includes the transmission resources allocated to each IoT terminal, including carrier frequency, time slot number, spreading code channel, and transmit power. That is, each solution vector in the search space includes the transmission resources allocated to all IoT terminals, including carrier frequency, time slot number, spreading code channel, and transmit power.
3. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 1, characterized in that: S2 determines the objective function and constraints for transmission link scheduling and constructs a transmission link scheduling model, including: S21. Determine the objective function for transmission link scheduling: Establish an objective function that balances multiple objectives, such as reducing transmission delay, alleviating link congestion, and increasing throughput, and transform transmission link scheduling into a constrained numerical optimization problem. S22. Determine the constraints for transmission link scheduling; S23. Combine the objective function and constraints of transmission link scheduling to construct a transmission link scheduling model.
4. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 3, characterized in that: S21 defines the objective function for transmission link scheduling: This objective function aims to achieve a balance between reducing transmission delay, alleviating link congestion, and increasing throughput, transforming transmission link scheduling into a constrained numerical optimization problem, including: The objective function F(X) for transmission link scheduling is expressed by the following formula: ； Where X is the solution vector, containing transmission resources including the carrier frequency, time slot number, spreading code channel, and transmit power allocated to all IoT terminals, and f delay (X) is the total transmission delay function, f congest (X) is the link congestion penalty function, f throughput (X) is the total throughput function. , , All are weighting coefficients, and ; The constraints for determining transmission link scheduling in S22 include: For the i-th solution vector X i =[f i1 ,f i2 ,…,f in ,…,f iN ], that is, the position of the i-th jackal in the jackal pack, where f in The transmission resources allocated to the nth IoT terminal, including carrier frequency, time slot number, spreading code channel, and transmit power, where N is the number of IoT terminals, are subject to the following constraints: 1) The i-th solution vector X i Each element represents a single IoT terminal, and the transmission resources allocated to each dimension are all within the preset range of the corresponding dimension's transmission resources. 2) The bandwidth occupied by all IoT terminals is less than the total available bandwidth of the satellite.
5. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 1, characterized in that: In S3, the Asian jackal group cooperative optimization algorithm is used to solve the transmission link scheduling model to obtain the optimal transmission link scheduling scheme, including: S31. Randomly generate an initial population in the search space. The position of each jackal in the population corresponds to a solution vector, and initialize the algorithm parameters. S32. Implement a nonlinear adaptive stage switching mechanism to automatically switch between the global exploration stage and the local development stage according to the iteration progress, balancing global search and local convergence. S33. In the global exploration phase, by simulating the behavior of reconnaissance jackals independently and dispersedly exploring without a fixed leader and randomly expanding the search domain, the global search range is expanded to avoid the algorithm getting stuck in local optima, and then proceeds to S35. S34. In the local development phase, by simulating the behavior of all jackals synchronously shrinking their encirclement from their current positions toward the prey, gradually approaching and capturing the prey, a fine local search is performed to quickly converge to the optimal solution, improving the accuracy of the solution. At the same time, on the one hand, by introducing an adaptive hunting coefficient, global exploration is enhanced in the early stage of iteration, and local development is enhanced in the later stage of iteration, resulting in more stable nonlinear descent. On the other hand, by introducing an optimal solution fluctuation coefficient, the prey position is randomly disturbed to prevent the algorithm from stagnating too early and proceeding to S35. S35. Use the objective function of transmission link scheduling to evaluate all jackals in the current population, calculate the corresponding fitness value, and record and update the historical best solution; S36. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S32. Otherwise, take the historical best solution as the optimal transmission link scheduling scheme.
6. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 5, characterized in that: S32 implements a nonlinear adaptive stage switching mechanism, automatically switching between the global exploration stage and the local development stage according to the iteration progress, balancing global search and local convergence, including: S321. Calculate the nonlinear adaptive stage switching probability P based on the iterative progress: ； Where t is the current iteration number and T is the maximum iteration number; S322. Compare the nonlinear adaptive phase switching probability P with the random number r, and switch between the global exploration phase and the local development phase based on the comparison result: ； Where r is a uniform random number in the range of 0 to 1.
7. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 5, characterized in that: In S33, during the global exploration phase, the algorithm simulates the behavior of scout jackals independently and dispersedly exploring without a fixed leader, randomly expanding the search domain to broaden the global search range and avoid the algorithm getting trapped in local optima. This includes: Based on the simulated behavior of reconnaissance jackals independently and dispersedly exploring, without a fixed leader, and randomly expanding their search domain, the positions of all jackals in the current population are updated: ； in, , Let ∠UB and ∠LB be the positions of the i-th jackal in the t-th and t+1-th iterations, respectively. Let UB and LB be the upper and lower bounds of the search space, respectively. Let rand(1,N) be an N-dimensional random vector with the same dimension as the solution vector. Each element in the random vector rand(1,N) follows a uniform distribution and takes values in the range [0,1]. This represents the Hadamard product, which is the element-wise product, and A is the exploration coefficient.
8. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 7, characterized in that: In S34, during the local development phase, a refined local search is performed by simulating the behavior of all jackals synchronously shrinking their encirclement towards the prey from their current positions, gradually approaching and capturing the prey. This rapidly converges to the optimal solution, improving the accuracy of the solution. Simultaneously, an adaptive hunting coefficient is introduced to enhance global exploration in the early stages of iteration and local development in the later stages, resulting in more stable nonlinear descent. Furthermore, an optimal solution fluctuation coefficient is introduced to introduce random perturbations to the prey's position, preventing the algorithm from stalling prematurely. This includes: S341. Based on the fitness values of all jackals in the current population, determine the current global optimal solution. And use it as the location of prey; S342. Based on the simulation of all jackals synchronously shrinking their encirclement towards the prey from their current positions, gradually approaching and capturing the prey, and introducing an adaptive hunting coefficient B and an optimal solution fluctuation coefficient C, the positions of all jackals in the current population are updated: ； in, This represents calculating the position of the i-th jackal at the t-th iteration. Location of prey The distance vector between the two vectors is calculated by subtracting each element from the other and taking the absolute value. B is the adaptive hunting coefficient. The nonlinearity decreases from 2 to 0 to enhance global exploration in the early stages of iteration and local development in the later stages. The decrease in nonlinearity leads to greater stability. C is the fluctuation coefficient of the optimal solution. Location of the prey Apply random perturbations to prevent the algorithm from stalling prematurely.
9. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 1, characterized in that: S4 analyzes the optimal transmission link scheduling scheme, allocates link resources, and finally issues scheduling instructions to low-Earth orbit satellites and IoT terminals, including: S41. Parse the optimal transmission link scheduling scheme into executable satellite IoT physical layer resource configuration instructions to complete the formal allocation of IoT terminal-satellite link; S42. Encapsulate resource configuration instructions into standard signaling and send them to ground stations, low-orbit satellites, and IoT terminals.
10. The method for scheduling transmission links of low-orbit satellite IoT data according to claim 9, characterized in that: In S42, resource allocation instructions are encapsulated into standard signaling and distributed to ground stations, low-Earth orbit satellites, and IoT terminals, including: The ground station will send the parsed resource configuration instructions to the low-Earth orbit satellite via the uplink. The resource configuration instructions include the transmission resources, including the carrier frequency, time slot number, spreading code channel, and transmit power allocated to all IoT terminals. Low-Earth orbit satellites use onboard processing units to receive and store resource configuration instructions, and configure corresponding transponders, transmission beams and time slot resources according to the instructions; Low-Earth orbit satellites send resource allocation instructions to corresponding IoT terminals via downlink and provide matching transmission channels for the IoT terminals, enabling the IoT terminals to establish communication links with the satellite according to the allocated transmission resources and complete data transmission.