Method and system for quickly distributing measurement and control resources of multiple spacecrafts

Abstract

The invention discloses a multi-spacecraft measurement and control resource rapid allocation method and system, and relates to the technical field of spaceflight measurement and control resource allocation. The method comprises the following steps: constructing a demand constraint-demand target distribution model according to a spacecraft demand; then, according to the measurement and control resources, constructing a demand-available resource allocation model; and using the demand constraint-demand target allocation model and the demand-available resource allocation model to carry out multi-spacecraft measurement and control resource rapid allocation so as to obtain a multi-spacecraft measurement and control resource allocation scheme satisfying multiple demands at the same time. According to the method, the double architecture of the demand constraint-target model and the demand-resource model is constructed, efficient and rapid distribution of multi-spacecraft measurement and control resources is realized by adopting the evolutionary algorithm, the problem of multi-constraint and multi-target combination optimization is solved, the solving efficiency and the resource utilization rate are remarkably improved, the rapid response demand is met, and the method is suitable for large-scale popularization and application. And the method has good engineering practicability and popularization value.

Description

Technical Field

[0001] This invention relates to the field of aerospace telemetry and control resource allocation technology, specifically a method and system for rapid allocation of telemetry and control resources for multiple spacecraft. Background Technology

[0002] With the rapid development of aerospace technology and the increasing demand for space applications, the number of spacecraft in orbit is showing a continuous growth trend. Various remote sensing satellites, communication satellites, navigation satellites, and deep space probes all rely on ground tracking and control stations to perform critical operations such as orbit determination, status monitoring, command uplink, and data downlink during their missions. However, the construction cycle of ground tracking and control stations is long, the investment scale is large, and the deployment conditions are limited. Their construction speed lags far behind the launch speed of spacecraft, leading to an increasingly prominent structural contradiction between the supply and demand of tracking and control resources, and a growing shortage of high-quality tracking and control resources.

[0003] In practical engineering applications, different spacecraft have diverse and personalized requirements for telemetry, tracking, and command (TT&C) resources based on their mission types, orbital characteristics, and payload operating modes. For example, low-Earth orbit (LEO) remote sensing satellites require multiple short-duration TT&C operations per day; high-Earth orbit (HEO) communication satellites require continuous TT&C operations over long periods; while deep space probes have strict requirements for TT&C operations during specific time periods. Meanwhile, different ground control and management agencies have different focuses on resource allocation schemes due to differences in management responsibilities and business priorities. Some prioritize key models, some emphasize balanced resource utilization, and some focus on time-period preferences. This complex application scenario of "multiple spacecraft, multiple constraints, and multiple objectives" makes the TT&C resource allocation problem a typical high-dimensional combinatorial optimization challenge.

[0004] Traditional telemetry, tracking, and command (TT&C) resource allocation mainly uses simple rule-matching algorithms. Although these algorithms can achieve basic allocation, they suffer from inherent defects such as low solution efficiency and poor optimization quality when faced with complex problems involving large scale, strong constraints, and multiple objectives. Therefore, this invention proposes a method and system for rapid allocation of TT&C resources for multiple spacecraft. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for rapid allocation of telemetry, tracking, and command (TT&C) resources for multiple spacecraft, thereby achieving a multi-spacecraft TT&C resource allocation scheme that simultaneously meets multiple requirements and improving the efficiency of solving the TT&C resource allocation problem.

[0006] According to a first aspect of the present invention, in order to achieve the above-mentioned objective, the present invention provides the following technical solution: a method for rapid allocation of telemetry, tracking, and command resources for multiple spacecraft, comprising the following steps: It receives all spacecraft telemetry and control requests and resources. The telemetry and control requests include the required time period, single arc segment duration requirements, arc segment quantity requirements, arc segment interval requirements, and priority for morning and afternoon time periods. The telemetry and control resources include spacecraft information, ground station information, and arc segment information. Based on the spacecraft's telemetry, tracking, and command (TT&C) requirements, the characteristics of each TT&C requirement are analyzed and identified. The identified characteristics are then generalized and a requirement constraint-requirement target allocation model is constructed. This model includes the requirement identifier, the spacecraft to which it belongs, the requirement time period, the single arc segment duration requirement, the arc segment quantity requirement, the arc segment interval requirement, and the priority of the morning and afternoon time periods. Based on spacecraft telemetry and control resources, an object-oriented modeling approach is adopted to construct a demand-available resource allocation model, which includes demand identifier, spacecraft name, available arc segment number, station name, station location, and start and end times. Based on the demand constraint-demand target allocation model and the demand-available resource allocation model, an evolutionary algorithm is used to quickly allocate telemetry, tracking, and command (TT&C) resources for multiple spacecraft, and output a TT&C resource allocation scheme that simultaneously satisfies multiple demand constraints and multiple optimization objectives.

[0007] Furthermore, the constraint-demand target allocation model includes constraint attributes and optimization target attributes. The constraint attributes include the shortest duration of a single arc segment, the longest duration of a single arc segment, the minimum number of arc segments, the maximum number of arc segments, the minimum arc segment interval, and the maximum arc segment interval. The optimization target attributes include maximizing the duration of a single arc segment, prioritizing the morning session, and prioritizing the afternoon session.

[0008] Furthermore, based on the demand constraint-demand target allocation model and the demand-available resource allocation model, an evolutionary algorithm is used for rapid allocation of telemetry, tracking, and command (TT&C) resources for multiple spacecraft, outputting a TT&C resource allocation scheme that simultaneously satisfies multiple demand constraints and multiple optimization objectives, as follows: (31) Define constraint functions and objective functions according to the demand constraint-demand target allocation model. The constraint function is used to determine whether the allocation scheme meets the various constraint requirements, and the objective function is used to evaluate the optimization degree of the allocation scheme. (32) Define individual coding strategies based on the demand-available resource allocation model and establish a mapping relationship between evolutionary algorithm individuals and measurement and control resource allocation schemes; (33) The evolutionary algorithm adopts the NSGA-II algorithm, which uses constraint functions, objective functions and individual coding strategies to perform algorithm calculations and obtain the optimal measurement and control resource allocation scheme.

[0009] Furthermore, the constraint functions are defined according to the demand constraint-demand target allocation model, as shown below: (41) Define constraint function c1 based on the shortest duration attribute of a single arc segment in the model; but (42) Define constraint function c2 based on the longest duration attribute of a single arc segment in the model; but (43) Define constraint function c3 based on the minimum number of arc segments attribute of the model; but (44) Define constraint function c4 based on the maximum number of arc segments in the model; but (45) Define constraint function c5 based on the minimum arc interval property of the model; but (46) Define constraint function c6 based on the maximum arc interval attribute of the model; but .

[0010] Furthermore, the objective function is defined according to the demand constraint-demand target allocation model, as follows: (51) Define the objective function o1 based on the single arc duration of the model as many attributes as possible; but (52) Based on the morning priority time attribute of the model, define the objective function o2; but (53) Based on the afternoon priority time period attribute of the model, define the objective function o3; but .

[0011] Furthermore, the individual encoding strategy defines three decision variables for each available arc segment: The first variable represents the selection duration of the available arc segment, and its value range is [0, available duration of the available arc segment]; The second variable represents the offset relative to the start time of the available arc segment, and its value range is [0, available arc segment duration]. The third variable is used to indicate whether the arc segment is selected. Its value is 0 or 1, where 1 indicates selection and 0 indicates non-selection. By combining and encoding three decision variables, the arc segment selected for each demand and its specific usage period are determined.

[0012] Furthermore, the evolutionary algorithm employs the NSGA-II algorithm, utilizing constraint functions, objective functions, and individual coding strategies to perform algorithmic calculations and obtain the optimal allocation scheme for measurement and control resources, as detailed below: (71) Collect the spacecraft telemetry and control requirements list and the telemetry and control resource list; (72) Preliminary screening: For each requirement, a list of available measurement and control arc segments is initially matched from global resources; (73) Iterate through the list of requirements: Population initialization: Using a problem-specific "individual encoding strategy", a set of initial solutions is generated, each solution representing an arc allocation scheme; Population evaluation: Calculate all constraint function values ​​and all objective function values ​​for each individual in the current population; Genetic iterative optimization: Enter a "selection-crossover-mutation" cycle until the preset maximum number of inheritances is reached. Selection: Based on non-dominated sorting and crowding distance calculation, select superior parent individuals from the current population; Genetic operations: Perform crossover and mutation operations on the selected parent individuals to generate offspring individuals; Merging and New Generation: Merge the parent and offspring populations and then select the new generation population again through non-dominated ranking and crowding comparison; Screening for feasible solutions: After the genetic iteration is completed, traverse and screen out all individuals in the obtained population whose constraint function values ​​are all 0; (74) Select the optimal solution: Among all feasible solutions, calculate the sum of all objective function values ​​for each solution, and select the individual with the smallest sum as the optimal allocation scheme for the demand; (75) Scheme decoding: The encoded variables of the optimal individual are converted into specific and executable "allocation arc" schemes through mapping rules; (76) Traversal and integration: Repeat the traversal operation until all requirements in the list have their respective allocation schemes; (77) Output: Summarize and output the final allocation scheme for all requirements.

[0013] According to a second aspect of the present invention, the present invention provides a rapid allocation system for multiple spacecraft telemetry, tracking, and command resources, used to implement a rapid allocation method for multiple spacecraft telemetry, tracking, and command resources described in the first aspect, comprising: The data receiving module is used to receive all spacecraft telemetry and control requirements and resources. The telemetry and control requirements include the required time period, single arc segment duration requirements, arc segment quantity requirements, arc segment interval requirements, and priority for morning and afternoon time periods. The telemetry and control resources include spacecraft information, ground station information, and arc segment information. The first construction module is used to analyze and identify the characteristics of each telemetry and control requirement based on the spacecraft's telemetry and control requirements, and to generalize the identified characteristics to construct a requirement constraint-requirement target allocation model. The requirement constraint-requirement target allocation model includes requirement identifier, spacecraft, requirement time period, single arc segment duration requirement, arc segment quantity requirement, arc segment interval requirement, and priority for morning and afternoon time periods. The second construction module is used to build a demand-available resource allocation model based on spacecraft telemetry and control resources and using object-oriented modeling. The demand-available resource allocation model includes demand identifier, spacecraft name, available arc segment number, station name, station location, and start and end time. The calculation output module is used to rapidly allocate telemetry, tracking, and command (TT&C) resources for multiple spacecraft based on the demand constraint-demand target allocation model and the demand-available resource allocation model, using an evolutionary algorithm to output a TT&C resource allocation scheme that simultaneously satisfies multiple demand constraints and multiple optimization objectives.

[0014] According to a third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor loads and executes the computer program, it employs a method for rapid allocation of multi-spacecraft telemetry and control resources as described in the first aspect.

[0015] According to a fourth aspect of the present invention, the present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for rapid allocation of multi-spacecraft telemetry and control resources as described in the first aspect.

[0016] This invention has at least the following beneficial effects: 1. This invention, through the construction of a dual-model architecture—a demand constraint-demand objective allocation model and a demand-available resource allocation model—achieves a systematic and standardized description of complex telemetry and control requirements for multiple spacecraft. It can comprehensively coordinate multi-dimensional constraints such as single arc segment duration limits, arc segment quantity requirements, arc segment interval constraints, and time period priorities, while also considering multi-objective optimizations such as maximizing arc segment duration and prioritizing specified time periods. This solves the bottleneck of traditional methods in handling high-dimensional combinatorial optimization problems within a reasonable time. This invention adopts the NSGA-II evolutionary algorithm based on non-dominated sorting, combined with an innovative three-variable individual encoding strategy, which can quickly converge to the optimal solution set for multiple objectives, greatly improving the solution efficiency and truly meeting the urgent need for rapid response in aerospace telemetry and control missions.

[0017] 2. This invention uses object-oriented modeling to abstractly describe the personalized needs of various spacecraft. Whether it is the need for multiple short-term tracking of low-orbit remote sensing satellites, the need for long-term continuous telemetry and control of high-orbit communication satellites, or the strict constraints of specific time periods of deep space probes, all can be uniformly incorporated into the model framework for automatic solution. There is no need to develop separate algorithms for each type of spacecraft, which significantly reduces the system maintenance cost.

[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the method described in this invention; Figure 2 This is a flowchart illustrating the NSGA-Ⅱ algorithm in this invention; Figure 3 This is a schematic diagram of the system described in this invention; Figure 4 This is a schematic diagram of the computer device in this invention. Detailed Implementation

[0020] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] Explanation of relevant terms: Measurement and control requirements: During the spacecraft control process, the ground control and management agency proposes requirements for spacecraft measurement and control based on mission and management needs. This invention specifically refers to the requirements for spacecraft measurement and control.

[0022] Telemetry, tracking, and command (TT&C) resources: During the spacecraft control process, the ground control and management agency tracks and controls the spacecraft through ground stations. The time (also known as arc) that the ground station can track and control the spacecraft is called the TT&C resources.

[0023] Example 1: Please see Figures 1-2 This invention provides a technical solution: a method for rapid allocation of telemetry, tracking, and command resources for multiple spacecraft, comprising the following steps: Step S1: Collect spacecraft telemetry and control requirements; In step S1 above, the spacecraft telemetry and control requirements are collected. The ground control and management agency has different requirements for the spacecraft it manages. For example, spacecraft A needs to be tracked for 2 hours every day for the next week, avoiding the period from 12:00 to 14:00, and preferably in the afternoon; spacecraft B needs to be tracked 3 times a day for the next week, with each tracking session lasting no less than 30 minutes; spacecraft C needs to be tracked for 1 hour every other day for the next week, and try to use only one arc segment, etc. All the telemetry and control requirements for the spacecraft are collected from the ground control and management agency to form a list of spacecraft telemetry and control requirements, which specifically includes the required time period, single arc segment duration requirement, arc segment quantity requirement, arc segment interval requirement, and priority for morning and afternoon time periods. Step S2: Based on the spacecraft's telemetry, tracking, and command requirements, construct a requirement constraint-requirement target allocation model, as follows: Based on the spacecraft telemetry, tracking, and command (TT&C) requirements, a requirement constraint-requirement target allocation model is constructed. Based on various spacecraft TT&C requirements collected by S1, the characteristics of each requirement are analyzed and identified. Using object-oriented modeling, the characteristics of the spacecraft TT&C requirements are generalized and a requirement constraint-requirement target allocation model is constructed. Table 1 Demand Constraint-Demand Target Allocation Model Step S3: Collect all spacecraft telemetry and control resources from ground control and management users to form a spacecraft telemetry and control resource list: Table 2 List of Spacecraft Telemetry, Tracking and Control Resources Step S4: Based on the spacecraft telemetry and control resources and the spacecraft telemetry and control resource list collected in S3, a demand-available resource allocation model is constructed using object-oriented modeling, as follows: Table 3 Demand-Available Resource Allocation Model Step S5: Define constraint functions and objective functions based on the demand constraint-demand target model. For constraint functions, a value of 0 indicates that the constraint is satisfied, and a value of non-zero indicates that the constraint is not satisfied. For objective functions, the one with the absolute value closest to 0 is the optimal one. S51. The constraint functions defined according to the demand constraint-demand target model are as follows: S51.1 Define constraint function c1 based on the shortest duration attribute of a single arc segment in the model; but S51.2 Based on the longest duration attribute of a single arc segment in the model, define constraint function c2; but S51.3 Define constraint function c3 based on the minimum number of arc segments attribute of the model; but S51.4 Define constraint function c4 based on the maximum number of arc segments in the model; but S51.5 Define constraint function c5 based on the minimum arc interval property of the model; but S51.6 Define constraint function c6 based on the maximum arc interval attribute of the model; but S52. The objective function defined according to the demand constraint-demand target model is shown in the table below: S52.1 Define the objective function o1 based on the single arc duration of the model as many attributes as possible; but S52.2 Based on the morning priority time period attribute of the model, define the objective function o2; but S52.3 Based on the afternoon priority time period attribute of the model, define the objective function o3; but ; Step S6: Define individual coding strategies based on the demand-available resource allocation model. The measurement and control resource allocation module defines individual coding strategies based on the demand-available resource allocation model, establishing a mapping relationship between variables and allocation arcs. In this embodiment, for the demand-available resource allocation model, three variables are defined for each available resource to control the selection of resources (arcs), namely: Variable 1: The duration of arc segment selection, the range of which is [0, available arc segment duration]; Variable 2: The offset relative to the start time of the available arc segment. The value range of this variable is [0, duration of available arc segment]. Variable 3: Whether to select this arc segment; the value of this variable is 0 or 1. The variables defined above can be used to determine whether the arc segment is selected by checking whether the value of variable 3 is 1. The start and end times of the arc segment can be obtained by using variables 1 and 2. Step S7: As Figure 2 As shown, the evolutionary algorithm uses the NSGA-II algorithm, which utilizes constraint functions, objective functions, and individual encoding strategies to perform rapid allocation, resulting in the following allocation scheme: Phase 1: Data Preparation and Preprocessing Input: Collect the "Spacecraft Telemetry and Control Requirement List" and the "Telemetry and Control Resource List".

[0024] Preliminary screening: For each requirement, a list of available measurement and control arc segments is initially matched from global resources to narrow down the search space for subsequent algorithms.

[0025] Phase Two: Perform independent multi-objective optimization for each requirement (core loop) This stage iterates through the list of requirements, executing a complete NSGA-II optimization process independently for each spacecraft telemetry and control requirement: Initialize the population: Using a problem-specific “individual encoding strategy”, a set of initial solutions (i.e., the initial population) is randomly generated, with each solution representing a possible arc allocation scheme.

[0026] Population evaluation: Calculate all constraint function values ​​(to measure the feasibility of the solution) and all objective function values ​​(to measure the quality of the solution) for each individual in the current population.

[0027] Genetic iterative optimization: Enter a "selection-crossover-mutation" cycle until the preset maximum number of inheritances is reached. Selection: Based on non-dominated sorting and crowding distance calculation, superior parent individuals are selected from the current population. This ensures convergence and population diversity.

[0028] Genetic operations: Perform crossover and mutation operations on the selected parent individuals to generate offspring individuals.

[0029] Merging and New Generation: The parent and offspring populations are merged, and a new generation population is selected again through non-dominated sorting and crowding comparison.

[0030] Phase 3: Determining the final solution from the optimization results Screening feasible solutions: After the genetic iteration is completed, in all the obtained populations, traverse and screen out all individuals whose constraint function values ​​are all 0 (i.e., feasible solutions that satisfy all hard constraints).

[0031] Selecting the optimal solution: Among all feasible solutions, calculate the sum of all objective function values ​​for each solution, and select the individual with the smallest sum as the optimal allocation scheme for the requirement.

[0032] Scheme Decoding: The encoded variables of the optimal individual are converted into specific, executable "assignment arc" schemes through mapping rules.

[0033] Phase Four: Integration and Output Traversal and Integration: Repeat phases two through three until all requirements in the list have their respective allocation schemes.

[0034] Output: Summarize and output the final allocation plan for all requirements, and the process ends.

[0035] It should be noted that during the entire iteration process, after the initial population is initialized and a set of initial solutions are randomly generated, a scheme decoding operation is required to map the encoded variables of the initial solutions to specific allocation arc information, which facilitates the subsequent calculation of constraint functions and objective functions.

[0036] In summary, this invention, through the construction of a dual-model architecture—a demand constraint-demand objective allocation model and a demand-available resource allocation model—achieves a systematic and standardized description of complex telemetry and control requirements for multiple spacecraft. It comprehensively addresses multi-dimensional constraints such as single-arc duration limits, arc number requirements, arc interval constraints, and time period priorities, while also considering multi-objective optimizations such as maximizing arc duration and prioritizing specified time periods. This solves the bottleneck of traditional methods struggling to handle high-dimensional combinatorial optimization problems within a reasonable timeframe. Furthermore, this invention employs the NSGA-II evolutionary algorithm based on non-dominated sorting, coupled with an innovative three-variable individual encoding strategy, enabling rapid convergence to the optimal solution set for multiple objectives, significantly improving solution efficiency and truly meeting the urgent need for rapid response in aerospace telemetry and control missions.

[0037] Example 2: like Figure 3 As shown, this embodiment provides a rapid allocation system for multi-spacecraft telemetry and control resources, used to implement a rapid allocation method for multi-spacecraft telemetry and control resources described in Embodiment 1, including: The data receiving module is used to receive all spacecraft telemetry and control requirements and resources. The telemetry and control requirements include the required time period, single arc segment duration requirements, arc segment quantity requirements, arc segment interval requirements, and priority for morning and afternoon time periods. The telemetry and control resources include spacecraft information, ground station information, and arc segment information. The first construction module is used to analyze and identify the characteristics of each telemetry and control requirement based on the spacecraft's telemetry and control requirements, and to generalize the identified characteristics to construct a requirement constraint-requirement target allocation model. The requirement constraint-requirement target allocation model includes requirement identifier, spacecraft, requirement time period, single arc segment duration requirement, arc segment quantity requirement, arc segment interval requirement, and priority for morning and afternoon time periods. The second construction module is used to build a demand-available resource allocation model based on spacecraft telemetry and control resources and using object-oriented modeling. The demand-available resource allocation model includes demand identifier, spacecraft name, available arc segment number, station name, station location, and start and end time. The calculation output module is used to rapidly allocate telemetry, tracking, and command (TT&C) resources for multiple spacecraft based on the demand constraint-demand target allocation model and the demand-available resource allocation model, using an evolutionary algorithm to output a TT&C resource allocation scheme that simultaneously satisfies multiple demand constraints and multiple optimization objectives.

[0038] Example 3: like Figure 4As shown, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it adopts a method for rapid allocation of multi-spacecraft telemetry and control resources as described in Embodiment 1.

[0039] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0040] Furthermore, the processor can be a central processing unit (CPU). Of course, depending on the actual use, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it in this regard.

[0041] Example 4: The present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for rapid allocation of multi-spacecraft telemetry and control resources as described in Embodiment 1.

[0042] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0043] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0044] For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on," "mounted on," "fixed to," or "set on" another element, it may be directly on the other element or there may be an intermediate element present. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible embodiments.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0046] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

Claims

1. A method for rapid allocation of telemetry, tracking, and command resources for multiple spacecraft, characterized in that, Includes the following steps: It receives all spacecraft telemetry and control requests and resources. The telemetry and control requests include the required time period, single arc segment duration requirements, arc segment quantity requirements, arc segment interval requirements, and priority for morning and afternoon time periods. The telemetry and control resources include spacecraft information, ground station information, and arc segment information. Based on the spacecraft's telemetry, tracking, and command (TT&C) requirements, the characteristics of each TT&C requirement are analyzed and identified. The identified characteristics are then generalized and a requirement constraint-requirement target allocation model is constructed. This model includes the requirement identifier, the spacecraft to which it belongs, the requirement time period, the single arc segment duration requirement, the arc segment quantity requirement, the arc segment interval requirement, and the priority of the morning and afternoon time periods. Based on spacecraft telemetry and control resources, an object-oriented modeling approach is adopted to construct a demand-available resource allocation model, which includes demand identifier, spacecraft name, available arc segment number, station name, station location, and start and end times. Based on the demand constraint-demand target allocation model and the demand-available resource allocation model, an evolutionary algorithm is used to quickly allocate telemetry, tracking, and command (TT&C) resources for multiple spacecraft, and output a TT&C resource allocation scheme that simultaneously satisfies multiple demand constraints and multiple optimization objectives.

2. The method for rapid allocation of telemetry, tracking, and command resources for multiple spacecraft according to claim 1, characterized in that: The constraint-demand target allocation model includes constraint attributes and optimization target attributes. The constraint attributes include the shortest duration of a single arc segment, the longest duration of a single arc segment, the minimum number of arc segments, the maximum number of arc segments, the minimum arc segment interval, and the maximum arc segment interval. The optimization target attributes include maximizing the duration of a single arc segment, prioritizing the morning session, and prioritizing the afternoon session.

3. The method for rapid allocation of telemetry, tracking, and command resources for multiple spacecraft according to claim 2, characterized in that: Based on the demand constraint-demand objective allocation model and the demand-available resource allocation model, an evolutionary algorithm is used for rapid allocation of telemetry, tracking, and command (TT&C) resources for multiple spacecraft, outputting a TT&C resource allocation scheme that simultaneously satisfies multiple demand constraints and multiple optimization objectives, as detailed below: (31) Define constraint functions and objective functions according to the demand constraint-demand target allocation model. The constraint function is used to determine whether the allocation scheme meets the various constraint requirements, and the objective function is used to evaluate the optimization degree of the allocation scheme. (32) Define individual coding strategies based on the demand-available resource allocation model and establish a mapping relationship between evolutionary algorithm individuals and measurement and control resource allocation schemes; (33) The evolutionary algorithm adopts the NSGA-II algorithm, which uses constraint functions, objective functions and individual coding strategies to perform algorithm calculations and obtain the optimal measurement and control resource allocation scheme.

4. The method for rapid allocation of multi-spacecraft telemetry and control resources according to claim 3, characterized in that: The constraint function is defined based on the demand constraint-demand target allocation model, as shown below: (41) Define constraint function c1 based on the shortest duration attribute of a single arc segment in the model; but (42) Define constraint function c2 based on the longest duration attribute of a single arc segment in the model; but (43) Define constraint function c3 based on the minimum number of arc segments attribute of the model; but (44) Define constraint function c4 based on the maximum number of arc segments in the model; but (45) Define constraint function c5 based on the minimum arc interval property of the model; but (46) Define constraint function c6 based on the maximum arc interval attribute of the model; but .

5. The method for rapid allocation of multi-spacecraft telemetry and control resources according to claim 4, characterized in that: The objective function is defined based on the demand constraint-demand target allocation model, as shown below: (51) Define constraint function o1 based on the single arc duration of the model as many attributes as possible; but (52) Define constraint function o2 based on the morning priority time attribute of the model; but (53) Define constraint function o3 based on the afternoon priority time period attribute of the model; but .

6. The method for rapid allocation of telemetry, tracking, and command resources for multiple spacecraft according to claim 5, characterized in that: The individual encoding strategy defines three decision variables for each available arc segment: The first variable represents the selection duration of the available arc segment, and its value range is [0, available duration of the available arc segment]; The second variable represents the offset relative to the start time of the available arc segment, and its value range is [0, available arc segment duration]. The third variable is used to indicate whether the arc segment is selected. Its value is 0 or 1, where 1 indicates selection and 0 indicates non-selection. By combining and encoding three decision variables, the arc segment selected for each demand and its specific usage period are determined.

7. A method for rapid allocation of telemetry, tracking, and command resources for multiple spacecraft according to claim 6, characterized in that: The evolutionary algorithm employs the NSGA-II algorithm, utilizing constraint functions, objective functions, and individual coding strategies to perform algorithmic calculations and obtain the optimal allocation scheme for measurement and control resources, as detailed below: (71) Collect the spacecraft telemetry and control requirements list and the telemetry and control resource list; (72) Preliminary screening: For each requirement, a list of available measurement and control arc segments is initially matched from global resources; (73) Iterate through the list of requirements: Population initialization: Using a problem-specific "individual encoding strategy", a set of initial solutions is generated, each solution representing an arc allocation scheme; Population evaluation: Calculate all constraint function values ​​and all objective function values ​​for each individual in the current population; Genetic iterative optimization: Enter a "selection-crossover-mutation" cycle until the preset maximum number of inheritances is reached. Selection: Based on non-dominated sorting and crowding distance calculation, select superior parent individuals from the current population; Genetic operations: Perform crossover and mutation operations on the selected parent individuals to generate offspring individuals; Merging and New Generation: Merge the parent and offspring populations and then select the new generation population again through non-dominated ranking and crowding comparison; Screening for feasible solutions: After the genetic iteration is completed, traverse and screen out all individuals in the obtained population whose constraint function values ​​are all 0; (74) Select the optimal solution: Among all feasible solutions, calculate the sum of all objective function values ​​for each solution, and select the individual with the smallest sum as the optimal allocation scheme for the demand; (75) Scheme decoding: The encoded variables of the optimal individual are converted into specific and executable "allocation arc" schemes through mapping rules; (76) Traversal and integration: Repeat the traversal operation until all requirements in the list have their respective allocation schemes; (77) Output: Summarize and output the final allocation scheme for all requirements.

8. A rapid allocation system for multi-spacecraft telemetry and control resources, used to implement the rapid allocation method for multi-spacecraft telemetry and control resources as described in any one of claims 1 to 7, characterized in that, include: The data receiving module is used to receive all spacecraft telemetry and control requirements and resources. The telemetry and control requirements include the required time period, single arc segment duration requirements, arc segment quantity requirements, arc segment interval requirements, and priority for morning and afternoon time periods. The telemetry and control resources include spacecraft information, ground station information, and arc segment information. The first construction module is used to analyze and identify the characteristics of each telemetry and control requirement based on the spacecraft's telemetry and control requirements, and to generalize the identified characteristics to construct a requirement constraint-requirement target allocation model. The requirement constraint-requirement target allocation model includes requirement identifier, spacecraft, requirement time period, single arc segment duration requirement, arc segment quantity requirement, arc segment interval requirement, and priority for morning and afternoon time periods. The second construction module is used to build a demand-available resource allocation model based on spacecraft telemetry and control resources and using object-oriented modeling. The demand-available resource allocation model includes demand identifier, spacecraft name, available arc segment number, station name, station location, and start and end time. The calculation output module is used to rapidly allocate telemetry, tracking, and command (TT&C) resources for multiple spacecraft based on the demand constraint-demand target allocation model and the demand-available resource allocation model, using an evolutionary algorithm to output a TT&C resource allocation scheme that simultaneously satisfies multiple demand constraints and multiple optimization objectives.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it employs a method for rapid allocation of multi-spacecraft telemetry and control resources as described in any one of claims 1 to 7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform a method for rapid allocation of multi-spacecraft telemetry and control resources as described in any one of claims 1 to 7.

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