A method and system for parsing satellite data packets

Abstract

The present invention provides a method and system for parsing satellite data packets, which relates to the technical field of data processing. The method includes: obtaining an original satellite data packet; performing segmentation processing on the original satellite data packet to obtain a plurality of data segments; aiming at improving the success rate of satellite data transmission and service balance, re-arranging each data segment through a bat optimization algorithm; parsing each data segment in parallel by a CPU and a GPU according to the re-arrangement order; re-organizing the parsed data; verifying the integrity of the re-organized data packet; and storing the re-organized data packet when the integrity verification is passed. The present invention can meet the real-time requirements of tasks and complex environments, and improve the data parsing efficiency by parsing each data segment in parallel by a CPU and a GPU.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for parsing satellite data packets. Background Art

[0002] The significance of satellite data packet parsing lies in ensuring that the massive data transmitted from satellites to ground stations can be processed and utilized efficiently and accurately. Through processes such as segmenting, reordering, parsing, checking, and recombining data packets, the data transmission success rate can be maximized, resource utilization optimized, service balance improved, and the integrity and reliability of data guaranteed. This process is crucial for application fields such as remote sensing images, scientific observations, communication navigation, etc., and is a key technical link for realizing rapid processing and efficient application of satellite information.

[0003] Currently, for satellite data packet parsing, mainly the First Observed, First Downlink (FOFD) method is adopted, and data transmission and parsing are carried out in the order of "first collected, first downlinked". This method is simple and easy to implement, and does not require complex segmentation and scheduling.

[0004] However, when facing limited transmission windows or unstable channels, the data transmission efficiency of the First Observed, First Downlink (FOFD) technology is low, and it is unable to dynamically adjust the data transmission order, making it difficult to meet the real-time requirements of tasks and complex environments. Summary of the Invention

[0005] In order to solve the technical problems that in the traditional First Observed, First Downlink (FOFD) technology, when facing limited transmission windows or unstable channels, the data transmission efficiency is low, it is unable to dynamically adjust the data transmission order, and it is difficult to meet the real-time requirements of tasks and complex environments, the present invention provides a method and system for parsing satellite data packets.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect:

[0008] A method for parsing satellite data packets provided by an embodiment of the present invention includes:

[0009] S1: Obtain the original satellite data packet;

[0010] S2: Segment the original satellite data packet to obtain a plurality of data segments;

[0011] S3: Reorder each data segment through the bat optimization algorithm with the goal of improving the satellite data transmission success rate and service balance;

[0012] S4: Parse each data segment in parallel by the CPU and GPU according to the reordered order;

[0013] S5: Reorganize the parsed data;

[0014] S6: Verify the integrity of the reorganized data packet;

[0015] S7: When passing the integrity verification, store the reorganized data packet.

[0016] Second aspect:

[0017] A satellite data packet parsing system provided by an embodiment of the present invention includes:

[0018] A processor;

[0019] A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the satellite data packet parsing method as described in the first aspect is implemented.

[0020] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0021] In the present invention, aiming at improving the success rate of satellite data transmission and service balance, the bat optimization algorithm is used to re-arrange each data segment, dynamically adjust the data transmission order, which can meet the real-time requirements of tasks and complex environments. The CPU and GPU are used in parallel to parse each data segment, improving the data parsing efficiency. Description of the Drawings

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 It is a schematic flowchart of a satellite data packet parsing method provided by an embodiment of the present invention;

[0024] Figure 2 It is a schematic structural diagram of a satellite data packet parsing system provided by an embodiment of the present invention. Detailed Embodiments

[0025] The following will describe the technical solutions in the present invention with reference to the drawings.

[0026] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0027] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0028] Refer to the attached Figure 1 which shows a schematic flowchart of a satellite data packet parsing method provided by an embodiment of the present invention.

[0029] The embodiments of the present invention provide a satellite data packet parsing method, which may include the following steps:

[0030] S1: Obtain the original satellite data packet.

[0031] S2: Perform segmentation processing on the original satellite data packet to obtain a plurality of data segments.

[0032] In a possible implementation manner, S2 specifically includes sub-steps S201 to S203:

[0033] S201: Set the minimum transmission unit limit condition of the data packet.

[0034] S202: Obtain the available transmission capacity of each transmission time window between the satellite and the ground station.

[0035] S203: Determine whether the available transmission capacity of the transmission time window is greater than the current data packet size. If so, no segmentation is required. Otherwise, on the premise of meeting the minimum transmission unit of the data packet and the limit condition of the available transmission capacity of the transmission time window, segment the data packet in the order from high to low priority.

[0036] In the present invention, the transmission mode of the data packet can be dynamically adjusted according to the actual transmission conditions, maximizing the use of the available capacity of the transmission window, while ensuring that the data packet segmentation meets the minimum transmission unit limit, avoiding low transmission efficiency caused by too small segmentation. Through priority sorting, high-priority data can be allocated resources first, improving the transmission success rate of key data, meeting real-time requirements, and thus realizing the efficient use of transmission resources and the accurate guarantee of task priorities.

[0037] S3: With the goal of improving the satellite data transmission success rate and service balance degree, re-arrange each data segment through the bat optimization algorithm.

[0038] Among them, the Bat Algorithm is a meta - heuristic optimization algorithm inspired by the echolocation behavior of bats. Its core idea is to achieve global optimization by simulating the process of bats emitting ultrasonic waves for target search. The algorithm controls the position, speed, frequency, loudness, and pulse emission rate of bats to dynamically balance the ability of global search and local exploitation, and gradually approaches the optimal solution. Due to its flexible search mechanism and simple parameter adjustment, the Bat Algorithm shows strong global optimization ability and fast convergence in multi - objective optimization, solving non - linear problems, and complex scheduling tasks, and is widely used in fields such as data scheduling and path planning.

[0039] In a possible implementation, S3 specifically includes sub - steps S301 to S303:

[0040] S301: Set the fitness function of the Bat Algorithm with the goal of improving the satellite data transmission success rate and service balance.

[0041] Optionally, the fitness function of the Bat Algorithm is specifically:

[0042] ;

[0043] where f represents the fitness function, x i represents whether the i - th data segment is scheduled for transmission. If , it means the i - th data segment is scheduled for transmission. If , it means the i - th data segment is not scheduled for transmission. represents the partial data of the i - th data segment allocated for transmission in the j - th transmission time window, bt j represents the start time of the downlink task for transmission in the j - th transmission time window, SR represents the satellite data transmission success rate, SBD represents the service balance, and λ 1 represents the weight coefficient of the satellite data transmission success rate, and λ 2 represents the weight coefficient of the service balance.

[0044] Among them, those skilled in the art can set the size of the weight coefficient λ 1 of the satellite data transmission success rate and the weight coefficient λ 2 of the service balance according to the actual situation, and the present invention does not make any limitations.

[0045] In the present invention, by setting a fitness function targeting the satellite data transmission success rate (SR) and service balance degree (SBD), the reliability of data transmission and the fair allocation of resources can be taken into account simultaneously during the optimization process. The flexible adjustment of the weight coefficients can balance the priorities of different objectives, improve the transmission success rate of high-priority data, and reduce the uneven resource load between satellites and ground stations. This method ensures the efficiency of task completion and the overall stability of system operation, and is particularly applicable to optimization problems in complex multi-task environments.

[0046] Optionally, the satellite data transmission success rate is specifically:

[0047] ;

[0048] where SR represents the satellite data transmission success rate, x i represents whether the i-th data segment is scheduled for transmission, odi.w represents the priority of the i-th data segment, and n represents the total number of data segments.

[0049] Optionally, the service balance degree is specifically:

[0050] ;

[0051] where SBD represents the service balance degree, UR s represents the transmission window utilization rate of the s-th satellite, S represents the total number of satellites, m s represents the number of transmission windows of the s-th satellite, dt j represents the duration of the downlink task associated with the j-th transmission window, et j represents the end time of the j-th transmission window, and st j represents the start time of the j-th transmission window.

[0052] S302: Set the constraint conditions for data segment rearrangement.

[0053] Optionally, the constraint conditions include:

[0054] Scheduling times constraint:

[0055] ;

[0056] where x i represents whether the i-th data segment is scheduled for transmission. If , it means the i-th data segment is scheduled for transmission. If , it means the i-th data segment is not scheduled for transmission.

[0057] It should be noted that the scheduling times constraint limits that each original image data can be scheduled at most once to avoid repeated transmission.

[0058] Minimum transmission constraint:

[0059] ;

[0060] Wherein, represents the partial data of the i-th data segment transmitted in the j-th transmission time window, and d 0 represents the minimum transmission limit, and m represents the total number of transmission windows.

[0061] It should be noted that the minimum transmission constraint ensures that the segment size meets the minimum transmission limit and ensures that all segments of an original image data must be completely transmitted.

[0062] Logical time constraint:

[0063] .

[0064] It should be noted that the logical time constraint ensures that the generation time, transmission time, and expiration time of the data are in logical order.

[0065] Variable legality constraint:

[0066] .

[0067] It should be noted that the variable legality constraint defines the feasible value range of the variable and ensures that the variable value conforms to the problem definition.

[0068] S303: Under the constraints of the constraint conditions, based on the fitness function, use the bat optimization algorithm to re-arrange each data segment.

[0069] Specifically, initialize the bat individuals. Each bat individual represents a feasible data segment re-arrangement scheme. Each bat individual consists of multiple dimensional components, and each component represents a data segment.

[0070] In the global search stage, update the flight speed and position of the bat individuals:

[0071] ;

[0072] Wherein, f i represents the pulse emission frequency of the i-th bat individual, f min represents the minimum pulse emission frequency, f max represents the maximum pulse emission frequency, β t represents the non-linear inverse cosine acceleration factor at the t-th iteration, represents the speed of the i-th bat individual at the (t + 1)-th iteration, r 1 、r 2Represents a random number between 0 and 1, ω t Represents the inertia weight factor at the t-th iteration, Represents the velocity of the i-th bat individual at the t-th iteration, Represents the individual optimal solution, c i Represents the learning factor, Represents the global optimal solution, Represents the position of the i-th bat individual at the t-th iteration, Represents the position of the i-th bat individual at the (t + 1)-th iteration, Levy represents the Levy flight step size.

[0073] In the present invention, in the global search stage, it can not only guide the individual to approach the high-quality solution region through the global optimal solution, but also jump out of the local optimal trap through randomness and the flight step size, expand the search range, enhance the diversity and globality of the search, thereby accelerating the convergence process and improving the ability of the algorithm to handle complex optimization problems.

[0074] Optionally, the inertia weight factor is specifically:

[0075] ;

[0076] where, ω t Represents the inertia weight factor at the t-th iteration, ω min Represents the minimum inertia weight factor, ω max Represents the maximum inertia weight factor, t represents the current iteration number, T represents the maximum iteration number.

[0077] In the present invention, by dynamically adjusting the inertia weight factor to gradually decrease from the maximum inertia weight to the minimum inertia weight, the balance between exploration and exploitation can be achieved at different stages of the algorithm. The larger inertia weight in the initial stage enhances the global search ability of the individual and helps to explore a wider solution space; as the number of iterations increases, the inertia weight gradually decreases, enhancing the refined local search ability and improving the convergence accuracy of the solution. This linear decreasing strategy can effectively avoid the premature convergence of the algorithm and improve the search efficiency of the global optimal solution.

[0078] Optionally, the non-linear inverse cosine acceleration factor is specifically:

[0079] ;

[0080] where, arccos represents the inverse cosine function, t represents the current iteration number, T represents the maximum iteration number.

[0081] In the present invention, a more flexible iterative process control can be achieved by introducing a non-linear arccosine acceleration factor. This factor grows rapidly in the initial stage, enabling the algorithm to explore the solution space extensively in the early stage and avoid falling into local optima. As the iteration progresses, the growth rate gradually slows down, prompting the algorithm to focus more on the fine search near the global optimum in the later stage. Compared with the linear acceleration strategy, the non-linear characteristic of the arccosine function can provide a more natural balance mechanism, which helps to improve the search efficiency and convergence accuracy of the algorithm and adapt to the complexity requirements of different problems.

[0082] Optionally, the learning factor is specifically:

[0083] ;

[0084] where c i represents the learning factor of the i-th bat individual, c max represents the maximum learning factor, and c min represents the minimum learning factor.

[0085] In the present invention, by setting the learning factor to vary dynamically with the non-linear arccosine acceleration factor, gradually decreasing from a relatively large learning factor in the initial stage, the process of exploration and exploitation can be effectively balanced. In the initial stage, the relatively large learning factor enhances the individual's response ability to the global optimum and improves the global search effect. As the iteration progresses, the learning factor gradually decreases, prompting the individual to focus more on local exploitation in the later stage, thereby improving the convergence accuracy of the solution. This dynamic adjustment strategy can adapt to the requirements of complex optimization problems and improve the overall performance of the algorithm.

[0086] Optionally, the Levy flight step size is specifically:

[0087] ;

[0088] where represents the standard Gamma function, λ represents the exponential parameter, a represents the size parameter.

[0089] In the present invention, by introducing the Levy flight step size and utilizing its long-tailed distribution characteristic, randomness and non-uniformity can be introduced into the global search process, enhancing the algorithm's ability to jump out of local optima. The exponential parameter in the Levy distribution controls the range and frequency of the step size distribution. Larger step sizes allow for long-distance searches, while smaller step sizes permit local fine searches. This characteristic is very important in optimization problems because it can balance the exploration and exploitation processes, promoting extensive exploration of the solution space in the early stage and improving the accuracy of the solution through more precise searches in the later stage. The Levy flight step size is particularly suitable for high-dimensional and non-convex optimization problems and can significantly improve the global optimization ability and convergence efficiency of the algorithm.

[0090] Randomly generate a random number r between 0 and 1 3 , and determine the random number r 3 Whether it is greater than the pulse rate. If so, enter the local search phase. Otherwise, enter the phase of updating the pulse rate and the average pulse loudness.

[0091] In the local search phase, select a solution from the optimal solution set to generate a new local solution:

[0092] ;

[0093] where represents the average pulse loudness emitted by the i-th bat individual at the t-th iteration, ε represents a random number between 0 and 1, represents the average pulse loudness emitted by the i-th bat individual at the t-th iteration.

[0094] In the present invention, by selecting a solution from the optimal solution set and using the local perturbation formula to generate a new local solution, the current search area can be refined and developed, effectively improving the accuracy of the solution, while avoiding overdevelopment and falling into local optimality, thereby enhancing the stability and optimization ability of the algorithm.

[0095] Calculate the fitness value of the new local solution, and generate a random number r between 0 and 1 4 , and determine whether it satisfies and . If so, accept the new local solution. Otherwise, do not accept the new local solution.

[0096] Update the pulse rate and the average pulse loudness:

[0097] ;

[0098] where represents the pulse rate emitted by the i-th bat individual at the (t + 1)-th iteration, represents the initial pulse rate emitted by the i-th bat individual, e represents the natural constant, γ represents the pulse rate enhancement coefficient, represents the average pulse loudness emitted by the i-th bat individual at the t-th iteration, and α represents the pulse loudness attenuation coefficient.

[0099] In the present invention, by dynamically updating the pulse rate and the average pulse loudness of the bat individuals, the global search and local development processes can be effectively balanced. The pulse rate gradually increases with the number of iterations, enhancing the sensitivity of the bat to high-quality solutions, thereby improving the search efficiency. And the average pulse loudness gradually decreases with the iteration, making the algorithm more focused on local optimization in the later stage and finely adjusting the quality of the solution. This design can ensure that the algorithm has a strong global search ability in the initial stage and gradually converges to the optimal solution in the later stage, improving the overall optimization performance and convergence speed.

[0100] Judge whether the current iteration count has reached the maximum iteration count; if so, output the data segment rearrangement scheme represented by the bat individual with the highest current fitness; otherwise, return to continue the iteration.

[0101] In the present invention, by rearranging data segments through the bat optimization algorithm, it is possible to balance the two key objectives of improving the data transmission success rate and service balance under constraint conditions. The design of the fitness function ensures a clear optimization direction, and the constraint conditions ensure the practical feasibility of the scheme. This method can not only give priority to meeting the transmission requirements of high-priority data and improve the reliability of task completion, but also balance the resource utilization of satellites and ground stations, avoid uneven distribution of transmission tasks, and ultimately achieve efficient utilization of transmission resources and comprehensive improvement of service quality.

[0102] S4: According to the rearrangement order, parse each data segment in parallel through the CPU and GPU.

[0103] In a possible implementation manner, S4 specifically includes sub-steps S401 to S404:

[0104] S401: Uniformly convert each data segment into a format recognizable by the GPU.

[0105] S402: Calculate the load factor between the current CPU and GPU:

[0106] ;

[0107] Among them, σ represents the load factor, s represents the speedup ratio, g represents the number of CPU cooperative computing cores, T CPU represents the execution time of the CPU serial algorithm, T GPU represents the execution time of the GPU serial algorithm.

[0108] It should be noted that the load factor is a parameter used to measure the balance degree of load distribution of each computing resource (such as CPU and GPU) in the system. It dynamically determines how to reasonably allocate the computing task volume between the two by comparing the computing capabilities and task execution times of different computing devices (such as CPU and GPU), so as to achieve the efficient operation of the system and the maximization of resource utilization.

[0109] S403: According to the load factor and in accordance with the rearrangement order, allocate computing tasks to the CPU and GPU:

[0110] ;

[0111] Among them, L CPU represents the computing task volume allocated to the CPU, L GPUrepresents the amount of computing tasks allocated to the GPU, and L represents the total amount of computing tasks.

[0112] It should be noted that by combining the hardware performance (the capabilities of the CPU and GPU) and the task priorities, the resource allocation is dynamically optimized, improving the overall performance, flexibility, and efficiency of the system, and is particularly suitable for multi-task parallel processing scenarios.

[0113] S404: Parallelly parse each data segment through the CPU and GPU.

[0114] In the present invention, it is possible to significantly shorten the parsing time of large-scale data, give full play to the parallel computing advantages of the GPU, while balancing the loads of the CPU and GPU, avoiding resource waste or bottlenecks, and improving the performance and real-time nature of the overall system.

[0115] S5: Reorganize the parsed data.

[0116] S6: Verify the integrity of the reorganized data packet.

[0117] In a possible implementation, S6 specifically includes sub-steps S601 to S606:

[0118] S601: Calculate the hash value of each data segment.

[0119] S602: Combine the hash values of all data segments to form a Merkle hash tree.

[0120] Among them, the Merkle Hash Tree is a binary tree-based data structure used to efficiently verify the integrity and consistency of large-scale data. The leaf nodes of the tree store the hash values of each data segment, and the non-leaf nodes store the combination of the hash values of their child nodes (such as through concatenation calculation). The root node of the tree (referred to as the root hash value) can uniquely identify the integrity of all data. The Merkle hash tree has the characteristic of high verification efficiency. By the root hash value and a small number of intermediate nodes, it is possible to verify whether a single data segment is complete without checking the entire data set. Therefore, it is widely used in blockchain, file systems, and distributed storage, and is particularly suitable for the integrity verification of large-scale sharded data.

[0121] S603: Use the private key to digitally sign the root hash value of the Merkle hash tree.

[0122] S604: When receiving the reorganized data packet, reconstruct the Merkle hash tree.

[0123] S605: Use the public key to verify the digital signature.

[0124] S606: Verify whether the root hash value of the reconstructed Merkel hash tree is consistent with the root hash value stored in the digital signature. If so, determine that the reconstructed data packet is complete. Otherwise, determine that the reconstructed data packet is incomplete.

[0125] In the present invention, the hash value of each data segment provides integrity verification at the shard level. The structure of the Merkel hash tree aggregates the integrity of all segments into the root hash value, greatly improving the verification efficiency. Using the private key to digitally sign the root hash value ensures the authenticity and immutability of the data source. Finally, by reconstructing the hash tree and verifying the consistency of the signature and the root hash value, it is possible to quickly detect whether the data packet is complete or tampered with. This design integrates efficiency and security and is suitable for the integrity verification requirements of large-scale, distributed data transmission.

[0126] S7: When passing the integrity check, store the reconstructed data packet.

[0127] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include:

[0128] In the present invention, aiming to improve the success rate of satellite data transmission and service balance, the bat optimization algorithm is used to rearrange each data segment, dynamically adjusting the data transmission order, which can meet the real-time requirements of the task and complex environments. By parallelizing the parsing of each data segment by the CPU and GPU, the data parsing efficiency is improved.

[0129] Refer to the attached Figure 2 illustrates the structural schematic diagram of a satellite data packet parsing system provided by the present invention.

[0130] The present invention also provides a satellite data packet parsing system 20, including:

[0131] A processor 201;

[0132] A memory 202, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor 201, the satellite data packet parsing method as described in the method embodiment is implemented.

[0133] The satellite data packet parsing system 20 provided by the present invention can execute the above satellite data packet parsing method and achieve the same or similar technical effects. To avoid repetition, the present invention will not be elaborated herein.

[0134] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include:

[0135] In the present invention, aiming at improving the success rate of satellite data transmission and service balance, the bat optimization algorithm is used to re-arrange each data segment, dynamically adjust the data transmission order, so as to meet the real-time requirements of tasks and complex environments. The CPU and GPU are used in parallel to parse each data segment, improving the data parsing efficiency.

[0136] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0137] The following points need to be explained:

[0138] (1) The attached drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the general design.

[0139] (2) For the sake of clarity, in the attached drawings used to describe the embodiments of the present invention, the thickness of the film, region or substrate is enlarged or reduced, that is, these drawings are not drawn according to the actual ratio. It can be understood that when an element such as a film, region or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be an intermediate element.

[0140] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0141] As above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A satellite data packet parsing method, characterized in that: include: S1: Get the original satellite data packet; S2: Segment the original satellite data packet to obtain multiple data segments; S3: With the goal of improving the satellite data transmission success rate and service balance, each data fragment is rearranged through the bat optimization algorithm; S4: Parsing each data segment in parallel by CPU and GPU according to the rearranged order; S5: reorganize the parsed data; S6: Verify the integrity of the reassembled data packet; S7: When the integrity check is passed, the reassembled data packet is stored; Among them, S3 specifically includes: S301: with the goal of improving the satellite data transmission success rate and service balance, setting the fitness function of the bat optimization algorithm; S302: Setting constraints for rearrangement of data segments; S303: Under the constraint of the constraint condition, based on the fitness function, rearrange each data segment by using a bat optimization algorithm; Among them, S303 specifically includes: Initialize bat individuals, each bat individual represents a feasible data segment rearrangement scheme, each bat individual is composed of multiple dimensional components, each component represents a data segment; In the global search phase, update the flight speed and position of individual bats: ； Among them, f i represents the pulse emission frequency of the i-th bat individual, f min Indicates the minimum pulse transmission frequency, f max Indicates the maximum pulse emission frequency, β t represents the nonlinear arccosine acceleration factor at t iterations, represents the speed of the i-th bat individual at the t+1th iteration, r1 and r2 represent random numbers between 0 and 1, ω t represents the inertia weight factor at t iterations, represents the speed of the i-th bat individual at the t-th iteration, represents the individual optimal solution, c i represents the learning factor, represents the global optimal solution, represents the position of the i-th bat individual at the t-th iteration, represents the position of the i-th bat individual at the t+1th iteration, and Levy represents the Levy flight step length; Randomly generate a random number r3 between 0 and 1, and determine whether the random number r3 is greater than the pulse rate; if so, enter the local search phase; otherwise, enter the update pulse rate and pulse average loudness; In the local search phase, a solution is selected from the optimal solution set to generate a new local solution: ； in, represents the average loudness of the pulses emitted by the i-th bat individual at the tth iteration, and ε represents a random number between 0 and 1; Calculate the fitness value of the new local solution, generate a random number r4 between 0 and 1, and determine whether it satisfies and ; If yes, accept the new local solution; otherwise, do not accept the new local solution; where f represents the fitness function; Update the pulse rate and average loudness of the pulses: ； in, represents the pulse rate emitted by the i-th bat individual at the t+1th iteration, represents the initial pulse rate emitted by the i-th bat individual, e represents the natural constant, γ represents the pulse rate enhancement coefficient, represents the average loudness of the pulse emitted by the i-th bat individual at the t+1 iteration, and α represents the pulse loudness attenuation coefficient; Determine whether the current number of iterations has reached the maximum number of iterations; if so, output the data segment rearrangement plan representing the bat individual with the highest current fitness; otherwise, return to continue iterating.

2. The satellite data packet parsing method according to claim 1, wherein: S2 specifically includes: S201: Setting a minimum transmission unit restriction condition for a data packet; S202: Obtaining available transmission capacity of each transmission time window between the satellite and the ground station; S203: Determine whether the available transmission capacity of the transmission time window is greater than the current data packet size; if so, no segmentation is required; otherwise, on the premise of satisfying the minimum transmission unit of the data packet and the available transmission capacity constraint conditions of the transmission time window, segment the data packet in descending order of priority.

3. The satellite data packet parsing method according to claim 1, wherein: The fitness function of the bat optimization algorithm is specifically: ； Among them, f represents the fitness function, x i Indicates whether the i-th data fragment is scheduled for transmission. If , indicating that the i-th data fragment is scheduled for transmission, if , indicating that the i-th data fragment is not scheduled for transmission, represents the part of the data that the i-th data fragment is allocated to be transmitted in the j-th transmission time window, bt j represents the transmission start time of the downlink task in the jth transmission time window, SR represents the satellite data transmission success rate, SBD represents the service balance, λ1 represents the weight coefficient of the satellite data transmission success rate, and λ2 represents the weight coefficient of the service balance.

4. The satellite data packet parsing method according to claim 3, characterized in that: The satellite data transmission success rate is specifically: ； Among them, SR represents the satellite data transmission success rate, x i Indicates whether the i-th data fragment is scheduled for transmission, odi.w indicates the priority of the i-th data fragment, and n indicates the total number of data fragments.

5. The satellite data packet parsing method according to claim 3, characterized in that: The service balance is specifically: ； Among them, SBD represents service balance, UR s represents the transmission window utilization of the sth satellite, S represents the total number of satellites, and m s represents the number of transmission windows for the sth satellite, dt j represents the duration of the downlink task associated with the jth transmission window, et j represents the end time of the jth transmission window, st j Indicates the start time of the jth transmission window.

6. The satellite data packet parsing method according to claim 1, characterized in that: The constraints include: Scheduling frequency constraints: ； Among them, x i Indicates whether the i-th data fragment is scheduled for transmission. If , indicating that the i-th data fragment is scheduled for transmission, if , indicating that the i-th data fragment is not scheduled for transmission; Minimum transmission constraints: ； in, It represents the part of data that the i-th data fragment is allocated to be transmitted in the j-th transmission time window, d0 represents the minimum transmission limit, and m represents the total number of transmission windows; Logical time constraints: ； Variable validity constraints: 。 7. The satellite data packet parsing method according to claim 1, characterized in that: The S4 specifically includes: S401: Convert all data fragments into a format recognizable by the GPU; S402: Calculate the current load factor between the CPU and GPU; S403: Allocating computing tasks to the CPU and GPU according to the load factor and in the re-arrangement order; S404: parse each data segment in parallel using the CPU and GPU.

8. The satellite data packet parsing method according to claim 1, characterized in that: The S6 specifically includes: S601: Calculate the hash value of each data fragment; S602: Combining hash values ​​of all data fragments to form a Merkle hash tree; S603: Digitally sign the root hash value of the Merkle hash tree using a private key; S604: When the reassembled data packet is received, rebuild the Merkle hash tree; S605: Use the public key to verify the digital signature; S606: Verify whether the root hash value of the reconstructed Merkle hash tree is consistent with the root hash value stored in the digital signature; if so, determine that the reconstructed data packet is complete; otherwise, determine that the reconstructed data packet is incomplete.

9. A satellite data packet analysis system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the satellite data packet parsing method according to any one of claims 1 to 8 is implemented.

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