Spaceflight test, operation and control resource scheduling method oriented to fragment optimization

Abstract

The invention discloses a spaceflight test, operation and control resource scheduling method oriented to fragment optimization, and the method comprises the steps: firstly, carrying out the data preprocessing of a task demand and a feasible solution, constructing a demand-feasible solution mapping, carrying out the time standardization, and building a time slice index; secondly, fusing a task demand satisfaction rate, an equipment load balancing rate and a resource fragmentation rate, and constructing a comprehensive optimization objective function; then, on the basis of the target function, a greedy iterative algorithm is adopted, and an initial resource use plan is generated through iterative conflict block establishment and resolution; and finally, performing global resource arrangement on the initial plan, and combining resource fragments by detecting and transferring adjacent tasks to generate a final scheduling plan. Through a mode of combining active optimization and scheduling post-arrangement in the scheduling process, the fragmentation degree of the test, operation and control resources is effectively reduced, and the overall utilization rate of the system resources is remarkably improved while the task demand satisfaction rate and the equipment load balance are ensured.

Description

Technical Field

[0001] This application relates to the field of aerospace telemetry, tracking, and command (TT&C) technology, and more specifically, to a method for scheduling aerospace TT&C resources optimized for debris. Background Technology

[0002] With the rapid development of the space industry and the continuous expansion of the scale of spacecraft in orbit, mission types are becoming increasingly diverse and complex, placing higher demands on the scheduling capabilities of telemetry, tracking, and command (TT&C) resources. Efficient and reliable resource scheduling is the fundamental guarantee for the successful execution of space missions. Currently, research and practice in this field both domestically and internationally focus on improving mission completion rates, shortening total scheduling time, and optimizing resource load balancing, and have achieved a series of results in these areas.

[0003] However, during long-term operation and the alternating scheduling of multiple tasks, resource fragmentation has gradually become prominent. This problem manifests as a large number of scattered and discontinuous available segments of telemetry and control resources in terms of time, frequency band, and antenna, resulting in a decrease in the overall utilization rate of resources. Although local load balancing may meet the standards, the overall system efficiency is limited and it is difficult to adapt to the high-density and high-dynamic requirements of modern aerospace missions.

[0004] Therefore, in view of the shortcomings of existing scheduling methods in dealing with resource fragmentation, there is an urgent need for a space telemetry, tracking, and command (TT&C) resource scheduling method that can systematically reduce fragmentation and improve the efficiency of resource integration and utilization, so as to enhance the overall service capability and robustness of the TT&C network. Summary of the Invention

[0005] The purpose of this application is to overcome the shortcomings of existing technologies and provide a fragmentation-oriented aerospace telemetry, tracking, and command (TT&C) resource scheduling method. By constructing a comprehensive optimization objective function that integrates resource fragmentation rate and adopting a combination of greedy iterative plan generation and global fragmentation management techniques, this method solves the technical problem of low overall utilization caused by severe resource fragmentation in aerospace TT&C resource scheduling.

[0006] The objective of this application is achieved through the following technical solution:

[0007] Firstly, this application proposes a space tracking, telemetry, and command (TT&C) resource scheduling method optimized for debris, the method comprising: For multiple task requirements and corresponding feasible solutions in the scheduling scenario, data preprocessing operations are performed. The preprocessing operations include building an association mapping between requirements and feasible solutions, converting absolute time to relative time, and building a time-slice-based index. Based on task requirement fulfillment rate, equipment load balancing rate, and resource fragmentation rate, a comprehensive optimization objective function is designed. Based on the comprehensive optimization objective function, a greedy iterative algorithm is adopted to generate an initial measurement, operation and control resource utilization plan by iteratively establishing conflict blocks, resolving conflicts and updating the feasible solution space. The initial measurement, operation, and control resource utilization plan is reorganized globally. This reorganization includes detecting fragmented idle time periods and merging fragments to generate the final plan by judging and executing the transfer of surrounding task plans.

[0008] In one possible implementation, the step of constructing the association mapping between requirements and feasible solutions includes: Construct the first mapping structure ,in The task requirement is used as the key, and the list of all feasible solutions corresponding to the requirement is used as the value. Construct the second mapping structure ,in Use the feasible solution as the key and the corresponding task requirement as the value.

[0009] In one possible implementation, the step of converting absolute time to relative time includes: Set time baseline ; For any absolute time Calculate relative time - And store all time parameters as integers in seconds.

[0010] In one possible implementation, the step of building a time-slice-based index includes: With a fixed duration Divide the timeline into time slice units, the first The time interval corresponding to each time slice is , ,in , The end time of the scheduling cycle. It is the floor function; For each feasible solution's time interval Judgment and time slice Does it overlap? Then the feasible solution will be associated with the time slice. The condition for determining overlap is: and , This indicates the relative time point at which a feasible solution begins to occupy resource devices. This indicates the relative time point at which a feasible solution ends the use of resource-intensive equipment. Construct a hash table index where the key is the time slice index. The value is a list of all feasible solutions associated within the time slice.

[0011] In one possible implementation, the comprehensive optimization objective function is: , , , These are the weighting coefficients. To improve the user demand satisfaction rate For equipment load balancing rate, Resource fragmentation rate; The task requirement fulfillment rate is: , To use the total number of task requirements that the plan actually fulfills. This represents the total number of tasks required. The equipment load balancing rate is: , This represents the total number of resource devices. Indicates resource equipment Total available time period duration This indicates the total duration of tasks allocated on this resource device. Indicates resource equipment The number of tasks already assigned; Resource fragmentation rate: ,in This indicates the idle time window of the resource device. This indicates a fragmented window that has been determined to be unusable. This is a hyperparameter.

[0012] In one possible implementation, the process of establishing a conflict block is as follows: The feasible solution space is grouped by device, and the feasible solutions under each device group are sorted in ascending order of start time; Obtain the feasible solution with the earliest start time among the globally feasible solutions; By using the associated index, other feasible solutions that intersect with the earliest start time in time are found, forming a conflict block.

[0013] In one possible implementation, the conflict resolution step includes: Calculate the acceptability of each feasible solution in the conflicting block; The feasible solution with the highest acceptance is selected as the chosen plan for the current iteration, where the acceptance is calculated based on the optimization objective function.

[0014] In one possible implementation, the step of updating the feasible solution space includes: Remove feasible solutions from the remaining unscheduled tasks that conflict with current and historical selected plans; Remove the remaining feasible solutions that were not selected from the currently assigned requirements.

[0015] In one possible implementation, the step of detecting fragmented idle time periods includes: After generating the usage plan, calculate the idle time period for each resource device; If the duration of the idle period is less than a preset threshold, the idle period is determined to be a fragment unit.

[0016] In one possible implementation, fragmentation expansion includes: For idle time periods identified as fragmented units, determine whether adjacent assigned task plans can be transferred to other resource devices for execution; If the task plan If the task is transferred from device A to device B for execution, then the time window originally scheduled for this task on device A will be extended. free space before and after and Merge to form new consecutive idle windows Continuous idle windows length , Indicates the length of the interval.

[0017] The main solution and its various further alternatives described above can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed in this application; furthermore, the (non-conflicting alternatives) can also be freely combined with each other and with other alternatives. Those skilled in the art, after understanding the solution of this application, will realize from the prior art and common general knowledge that there are many combinations, all of which are technical solutions to be protected in this application, and will not be exhaustively listed here.

[0018] This application discloses a fragmentation-optimized aerospace telemetry, tracking, and command (TT&C) resource scheduling method. First, the task requirements and their feasible solutions are preprocessed to construct a requirement-feasible solution mapping, perform time standardization, and establish a time slice index. Second, a comprehensive optimization objective function is constructed by integrating task requirement fulfillment rate, equipment load balancing rate, and resource fragmentation rate. Then, based on this objective function, a greedy iterative algorithm is used to generate an initial resource usage plan through the iterative establishment and resolution of conflict blocks. Finally, the initial plan undergoes global resource consolidation, merging resource fragments by detecting and transferring adjacent tasks to generate the final scheduling plan. By combining proactive optimization during scheduling with post-scheduling consolidation, the fragmentation level of TT&C resources is effectively reduced, significantly improving the overall utilization rate of system resources while ensuring task requirement fulfillment rate and equipment load balancing. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The diagram shows a flowchart of a space tracking, telemetry, and command resource scheduling method for debris optimization proposed in an embodiment of this application.

[0021] Figure 2 Another flowchart of aerospace telemetry, tracking, and command (TT&C) resource scheduling method is shown; Figure 3 A schematic diagram of the feasible solution space distribution according to an embodiment of this application is shown; Figure 4 A schematic diagram of a greedy iteration-based algorithm for generating measurement, operation, and control resource usage plans according to an embodiment of this application is shown. Figure 5 A schematic diagram of the establishment of conflict blocks in the first iteration of the algorithm according to an embodiment of this application is shown; Figure 6 A schematic diagram of the distribution of the feasible solution space after the first round update of the algorithm according to an embodiment of this application is shown; Figure 7 A schematic diagram of the second round of the algorithm according to an embodiment of this application is shown; Figure 8 A schematic diagram of the third round of the algorithm according to an embodiment of this application is shown; Figure 9 A schematic diagram of the fourth round of the algorithm according to an embodiment of this application is shown; Figure 10 A schematic diagram of a measurement, operation, and control plan generated according to an embodiment of this application is shown; Figure 11 A schematic diagram of a fragment detection transfer scheme according to an embodiment of this application is shown; Figure 12 A schematic diagram of the final plan generated according to an embodiment of this application is shown. Detailed Implementation

[0022] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0023] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Please refer to Figure 1 , Figure 1 This paper presents a flowchart illustrating a space tracking, telemetry, and command (TT&C) resource scheduling method optimized for debris optimization, as proposed in an embodiment of this application. The method includes: Step S1: For multiple task requirements and corresponding feasible solutions in the scheduling scenario, perform data preprocessing operations. The preprocessing operations include building an association mapping between requirements and feasible solutions, converting absolute time to relative time, and establishing an index based on time slices.

[0025] The system receives multiple task requirements from a scheduling scenario, along with multiple feasible solutions for each requirement. Preprocessing operations include: First, constructing a mapping between requirements and feasible solutions. This involves systematically establishing the correspondence between each task requirement and all its possible candidate solutions, forming a structured set of matching pairs to ensure that subsequent algorithms can search and evaluate within a defined solution space. Second, converting absolute time to relative time. Considering that scheduling decisions focus more on the relative temporal relationships between tasks, the system uniformly converts absolute timestamps such as task requirement time and deadline time into relative times relative to the scheduling start point or a certain baseline time. This simplifies the calculation of time constraints and improves the model's generalization ability. Finally, establishing a time-slice-based index. To efficiently handle resource conflicts and allocation issues in the time dimension, the system divides the entire scheduling timeline into continuous time slices and establishes a fast index structure based on these time slices for information such as task requirements and resource occupancy status. This supports efficient querying of resource availability and conflict detection within the time window.

[0026] Step S2: Based on the task requirement fulfillment rate, equipment load balancing rate, and resource fragmentation rate, design a comprehensive optimization objective function.

[0027] To achieve comprehensive optimization of the scheduling scheme, a multi-dimensional comprehensive optimization objective function was designed. This function aims to synergistically optimize three key performance indicators: task requirement fulfillment rate, device load balancing rate, and resource fragmentation rate. The task requirement fulfillment rate measures the core efficiency of the system in completing business requests; the device load balancing rate assesses the dispersion of workload among execution units to improve the overall stability and resource utilization of the system; and the resource fragmentation rate quantifies the continuity of resource availability in time or space, aiming to reduce resource idleness and waste.

[0028] Step S3: Based on the comprehensive optimization objective function, a greedy iterative algorithm is adopted to generate an initial measurement, operation and control resource utilization plan by iteratively establishing conflict blocks, resolving conflicts and updating the feasible solution space.

[0029] Based on the comprehensive optimization objective function F(x), a greedy iterative algorithm is used to generate an initial measurement, operation, and control (MEC) resource utilization plan. This algorithm optimizes iteratively: First, in the conflict block establishment phase, the earliest start time feasible solution is obtained from the feasible solution space, and other feasible solutions with time overlap with this feasible solution are quickly found using a pre-processed time slice association index, forming a conflict block. Next, in the conflict resolution phase, the acceptability of each feasible solution in the conflict block is calculated, and the feasible solution with the highest acceptability is selected as the current iteration's chosen plan. Then, in the feasible solution space update phase, feasible solutions that conflict with the selected plan in the remaining scheduled tasks, as well as other feasible solutions in the currently allocated requirements besides the selected feasible solution, are deleted to ensure effective reduction of the solution space. The iterative process continues until the feasible solution space is empty, ultimately outputting an initial MEC resource utilization plan.

[0030] Step S4: Perform global resource organization on the initial measurement, operation and control resource usage plan. Global resource organization includes detecting fragmented idle time periods and merging fragments to generate the final plan by judging and executing the transfer of surrounding task plans.

[0031] First, the system detects fragmented idle time slots on the resource timeline, i.e., short and scattered available resource windows generated by task intervals. Then, the system intelligently judges and executes the transfer of surrounding task plans, dynamically adjusting the execution time or resource usage of adjacent or nearby tasks to merge and reorganize these scattered idle time slots. Finally, by eliminating or reducing resource fragmentation, a final measurement, operation, and control resource usage plan with higher continuous resource utilization and a more compact overall arrangement is generated.

[0032] The steps for constructing the mapping between requirements and feasible solutions include: Construct the first mapping structure : ,in The task requirement is used as the key, and the list of all feasible solutions corresponding to the requirement is used as the value. Construct the second mapping structure : ,in Use the feasible solution as the key and the corresponding task requirement as the value.

[0033] In terms of demand feasibility demapping, the core is to construct the following two data structures: and ,in, It is a mapping where the requirement is the key and the list of all feasible solutions corresponding to that requirement is the value. It is a mapping where feasible solutions are keys and the corresponding requirements are values. Constructing these two types of mappings involves traversing the space of feasible solutions. For each feasible solution, a mapping of the corresponding requirement is created and stored. In the middle, at the same time add the feasible solution to The list of feasible solutions corresponds to the requirements. The establishment of these two mapping structures is for fast retrieval during subsequent algorithm iterations. The mappings constructed through a single traversal in the preprocessing stage reduce the time consumption caused by multiple traversals in subsequent algorithm iterations, thereby improving the algorithm's time performance.

[0034] In one possible implementation, the step of converting absolute time to relative time includes: Set time baseline ; For any absolute time Calculate relative time - And store all time parameters as integers in seconds.

[0035] In feasible solution space optimization, the primary objective is to reduce the memory footprint of the feasible solution space. Since time windows are typically represented as absolute time, direct storage and processing would consume significant memory. Therefore, feasible solution space optimization aims to reduce memory consumption through time transformation, specifically by converting absolute time into accumulated seconds relative to a certain time baseline. Let the time baseline be... Then for any absolute time The relative time is: - In this way, all times are converted into an integer, thus saving storage space. Furthermore, this conversion is beneficial for subsequent scheduling algorithms, as integer operations are more efficient than date-time operations.

[0036] In one possible implementation, the step of building a time-slice-based index includes: With a fixed duration Divide the timeline into time slice units, the first The time interval corresponding to each time slice is , ,in , This is the end time of the scheduling cycle; For each feasible solution's time interval Judgment and time slice Does it overlap? Then the feasible solution will be associated with the time slice. The condition for determining overlap is: and ; Construct a hash table index where the key is the time slice index. The value is a list of all feasible solutions associated within the time slice.

[0037] In terms of constructing the association index, the main focus is on establishing a mapping relationship between feasible solutions and time points, enabling the scheduling algorithm to quickly find resources or demands available at specific time points. This index structure can significantly improve the efficiency of the scheduling algorithm and reduce time consumption in subsequent algorithm iterations. In this invention, time slices are employed. As a unit (setting) If the unit is 1 second, and the division is too fine, index building will also consume a huge amount of space. The time intervals corresponding to each time slice are: ,in,

[0038] For each feasible solution's time interval We associate it with all time slices that overlap with this time interval. For each time slice ,if If so, the feasible solution is added to the list of that time slice. Formally, the conditions for determining the time interval of a feasible solution and its associated time slice are: and This allows us to construct a hash table index, where the key is the time slice index. The value is a list of feasible solutions available within that time slice.

[0039] The overall optimization objective function is: , , , These are the weighting coefficients. To improve the user demand satisfaction rate For equipment load balancing rate, Resource fragmentation rate; The task requirement fulfillment rate is: , To use the total number of task requirements that the plan actually fulfills. This represents the total number of tasks required. The equipment load balancing rate is: ,in Indicates resource equipment Total available time period duration This indicates the total duration of tasks allocated on this resource device; Resource fragmentation rate: ,in This indicates the idle time window of the resource device. This indicates a fragmented window that has been determined to be unusable. This is a hyperparameter.

[0040] In the mission optimization objective setting step, not only were the demand satisfaction rate and equipment load balancing rate of the aerospace telemetry, tracking, and command (TT&C) system considered, but the fragmentation rate was also proposed to formulate optimization objectives for the subsequent greedy iterative generation of the TT&C plan. The optimization objectives are as follows:

[0041] To improve the user demand satisfaction rate For the load balancing rate of the equipment, Fragmentation rate of pre-allocated resources. , , These represent the corresponding weights. The following formula indicates the degree to which the solution meets user needs. The total number of representative solutions that meet the requirements. This represents the total number of tasks required.

[0042] The objective function for measuring the rationality of resource allocation is shown below. Wherein, Representing resources Total available time period duration This indicates the duration of tasks for which resources have already been allocated. This formula shows that during the search for feasible solutions in the resource pre-allocation scheme, the weight of the measurement and control resources for already allocated tasks will gradually decrease to ensure load balancing in resource allocation.

[0043] The resource fragmentation rate is represented by the following formula: Represents a fragmented window. This indicates a fragment window that is unusable (resource fragments with a window duration of less than 10 minutes are considered unusable fragments). This is a hyperparameter used to prevent the optimization function from tending to infinity when all fragmented windows are available.

[0044] In one possible implementation, the process of establishing a conflict block is as follows: The feasible solution space is grouped by device, and the feasible solutions under each device group are sorted in ascending order of start time; Obtain the feasible solution with the earliest start time among the globally feasible solutions; By using the associated index, other feasible solutions that intersect with the earliest start time in time are found, forming a conflict block.

[0045] The planning process for the utilization of measurement and control resources is primarily implemented using an iterative algorithm. Each iteration includes conflict block creation, conflict resolution, and updates to the feasible solution space.

[0046] The process of establishing conflict blocks mainly includes three steps. First, the feasible solution space is grouped by device, and the feasible solutions within each device group are sorted in ascending order of start time. Second, the arc segment with the earliest start time in each device is compared to obtain the feasible solution with the earliest start time in the global feasible solution. Third, by using the index that associates feasible solutions with time points, the set of arc segments that have a time intersection with that arc segment is found, and that arc segment and the set of arc segments that have a time intersection with it are designated as conflict blocks.

[0047] In one possible implementation, the conflict resolution step includes: Calculate the acceptability of each feasible solution in the conflicting block; The feasible solution with the highest acceptance level is selected as the chosen plan for the current iteration, where the acceptance level is based on the optimization objective function. calculate.

[0048] In terms of conflict resolution, there are mainly four steps. The first step is to associate the feasible solution with the requirement index. The first step is to quickly find the corresponding requirement for each feasible solution in the conflicting block; the second step is to associate the requirement with the feasible solution using an index. The first step is to find each feasible solution to the requirement. The second step is to calculate the acceptability of each feasible solution under all requirements for each feasible solution in the conflict block. The third step is to select the feasible solution with the highest acceptability as the result of the current iteration. Acceptability is determined according to... The calculation method is as follows.

[0049] In one possible implementation, the step of updating the feasible solution space includes: Remove feasible solutions from the remaining unscheduled tasks that conflict with current and historical selected plans; Remove other feasible solutions that are not selected from the currently assigned requirements.

[0050] In terms of updating the feasible solution space, it mainly includes the deletion of two types of feasible solutions: feasible solutions that conflict with the selected plan in the remaining tasks to be scheduled, and other feasible solutions in the currently allocated requirements besides the selected feasible solutions. After the feasible solution space is updated, a new round of conflict block creation and conflict resolution is restarted, iterating until the feasible solution space is empty.

[0051] In one possible implementation, the step of detecting fragmented idle time periods includes: After generating the usage plan, calculate the idle time period for each resource device; If the duration of an idle time period is less than a preset threshold, then this idle time period is determined to be a fragment unit.

[0052] The global resource integration process mainly includes two parts: fragmentation detection and fragmentation expansion. Fragmentation detection primarily involves calculating the idle time period for each device after the allocation plan is finalized. If the idle time period is less than 10 minutes, it is considered a fragmented unit.

[0053] In one possible implementation, fragmentation expansion includes: For idle time periods identified as fragmented units, determine whether adjacent assigned task plans can be transferred to other resource devices for execution; If the task plan If the task is transferred from device A to device B for execution, then on device A, the time window Wi originally occupied by the task and the free intervals before and after it will be adjusted accordingly. and Merge to form new consecutive idle windows Its length , Indicates the length of the interval.

[0054] Regarding fragment expansion, it involves determining whether plans surrounding the fragment can be transferred. Formalized, the original plan sequence for device A is... ,Task The time window is If If the execution is transferred to device B, then the original device A... The occupied time period becomes free, and it merges with the free periods before and after it. and It may be merged into a larger contiguous free window. , Indicates the length of the interval.

[0055] In one possible embodiment, Figure 2 Another flowchart of aerospace telemetry, tracking, and command (TT&C) resource scheduling method is shown, including: Step S1: Load the scheduling scenario. The scenario contains four task requirements. Table 1 lists the task requirements and the feasible solution space. A feasible solution mainly consists of several key elements such as the name of the occupying device, the time period occupied, and the priority planning function. In this embodiment, only the name of the occupying device and the time period occupied are listed. Table 1 shows the task requirements and the feasible solution space: Table 1

[0056] Step S2: Figure 3 A schematic diagram of the feasible solution space distribution according to an embodiment of this application is shown, illustrating the feasible solutions for each task requirement on each device over time. Simultaneously, an index is established and the feasible solution space is optimized; the established index is as follows: ; ; ; In optimizing the feasible solution space, the types of occupancy start time and occupancy end time are optimized. Based on the time baseline of 2025-10-01 00:00:00, the occupancy time is converted into accumulated seconds relative to that baseline time. The feasible solutions after space optimization are shown in Table 2: Table 2

[0057] Step S3: Figure 4 A schematic diagram of a greedy iterative algorithm for generating measurement, operation, and control resource usage plans according to an embodiment of this application is shown. The algorithm begins iteration, with the iteration steps as follows: Figure 4 As shown, a parallelizable and accelerated greedy iterative algorithm for generating measurement, operation, and control resource usage plans is presented. Both the conflict block establishment and conflict resolution stages can be accelerated through parallel strategies. Figure 3 In the feasible solution space distribution, there exists , Figure 5 A schematic diagram of the establishment of conflict blocks in the first iteration of the algorithm according to an embodiment of this application is shown.

[0058] Step S4: Calculation Figure 5 The acceptable level of each feasible solution in the conflicting blocks. Choosing C1 would result in a loss of B1, which is more costly; choosing C2 would not reduce the feasible solution space for the remaining requirements. Therefore, C2 has the highest acceptable level.

[0059] Step S5: After selecting C2 as the optimal choice for requirement C, update the feasible solution space. Remove feasible solutions that conflict with C2 (in this embodiment, there are no feasible solutions that conflict with C2) and the remaining feasible solutions for requirement C (delete C1). After updating the feasible solution space, the space distribution is as follows: Figure 6 As shown, Figure 6 A schematic diagram of the distribution of the feasible solution space after the first round of algorithm update according to an embodiment of this application is shown, wherein C2 is marked in green and does not participate in subsequent iteration calculations, but is only used to identify the generated measurement, operation and control plan.

[0060] Step S6: Continue iterating. In each iteration, sort by start time, select a new conflict block, calculate the acceptance degree of feasible solutions in the conflict block, select the optimal feasible solution to complete the conflict resolution, and update the feasible solution space. Figure 7 A schematic diagram of the second round of the algorithm according to an embodiment of this application is shown. Figure 8 A schematic diagram of the third round of the algorithm according to an embodiment of this application is shown. Figure 9 A schematic diagram of the fourth round of the algorithm according to an embodiment of this application is shown; Based on the results of the algorithm iteration Figure 10 A schematic diagram of a measurement, operation, and control plan generated according to an embodiment of this application is shown. Figure 10 The table lists all feasible solutions and the final selection results, which constitute the generated measurement, operation, and control plan.

[0061] Figure 11 A schematic diagram of a fragmentation detection and transfer scheme according to an embodiment of this application is shown; the diagram focuses on the resource fragmentation optimization process on device 3. There is an idle time period F1 (lasting less than 10 minutes, identified as a fragmentation unit) between planned C2 and B3 on device 3. The diagram illustrates three transfer schemes to eliminate this fragmentation: Scheme 1 transfers C2 to position C1 on device 1, Scheme 2 transfers B3 to position B1 on device 1, and Scheme 3 transfers B3 to position B2 on device 2. By comparison, Scheme 3 is preferred because it avoids overloading device 1, achieves load balancing among devices 1, 2, 3, and 4, and merges the fragmentation window with surrounding idle intervals to form a larger continuous idle window, thereby effectively reducing the resource fragmentation rate.

[0062] Step S7: Traverse the generated measurement and control plan and determine whether the idle time period meets the fragmentation condition. Among them, device 3 has an idle window F1=[2025-10-01 08:01:22, 2025-10-01 08:08:38], the duration of which is less than the fragmentation threshold of 10 minutes. This idle window belongs to the fragmentation.

[0063] Step S8: For the idle window F1, determine whether the surrounding plans C2 and B3 can be transferred. There are 3 transfer options.

[0064] Method 1: C2 can be transferred to C1 and run on device 1.

[0065] Option 2: B3 can be transferred to B1 and run on device 1.

[0066] Option 3: B3 can be transferred to B2 and run on device 2.

[0067] Option 1 would result in a higher load on device 1, while device 2 would be idle, potentially leading to an imbalance in device load.

[0068] Option 2 would result in a higher load on device 1 while device 2 would be idle, potentially leading to an imbalance in device load.

[0069] For scheme 3, load balancing can be achieved by allocating one measurement and control plan to each of devices 1, 2, 3, and 4.

[0070] Therefore, option 3 is selected to achieve the final objective of this embodiment of the invention. Figure 12 A schematic diagram of the final plan generated according to an embodiment of this application is shown.

[0071] Compared with the prior art, the embodiments of this application have the following beneficial effects: First, by incorporating resource fragmentation rate as the core optimization objective into the scheduling process, and by specifically performing fragmentation detection and expansion after plan generation, the number of unusable small idle windows is effectively reduced, thereby merging scattered resource fragments into usable resources and realizing a qualitative change in measurement, operation and control resources from "usable" to "easy to use".

[0072] Secondly, through data preprocessing steps (such as constructing demand-demapping, time relativization conversion, and establishing time slice indexes), an efficient retrieval foundation is provided for the subsequent greedy iterative algorithm, which greatly reduces the computational overhead of the algorithm in steps such as conflict detection and ensures the performance of the method in large-scale scheduling scenarios.

[0073] Third, the constructed optimization objective function comprehensively considers the task requirement satisfaction rate, equipment load balancing rate, and resource fragmentation rate, so that the generated scheduling plan can not only meet more task requirements, but also make the resource load more balanced, while actively reducing the fragmentation rate, and obtain a better overall scheduling scheme.

[0074] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A space tracking, telemetry, and command resource scheduling method optimized for debris, characterized in that, The method includes: For multiple task requirements and corresponding feasible solutions in the scheduling scenario, data preprocessing operations are performed. The preprocessing operations include building an association mapping between requirements and feasible solutions, converting absolute time to relative time, and building a time-slice-based index. Based on task requirement fulfillment rate, equipment load balancing rate, and resource fragmentation rate, a comprehensive optimization objective function is designed. Based on the comprehensive optimization objective function, a greedy iterative algorithm is adopted to generate an initial measurement, operation and control resource utilization plan by iteratively establishing conflict blocks, resolving conflicts and updating the feasible solution space. The initial measurement, operation, and control resource utilization plan is reorganized globally. This reorganization includes detecting fragmented idle time periods and merging fragments to generate the final plan by judging and executing the transfer of surrounding task plans.

2. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method as described in claim 1, characterized in that, The steps for constructing the mapping between requirements and feasible solutions include: Construct the first mapping structure ,in The task requirement is used as the key, and the list of all feasible solutions corresponding to the requirement is used as the value. Construct the second mapping structure ,in Use the feasible solution as the key and the corresponding task requirement as the value.

3. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method as described in claim 1, characterized in that, The steps to convert absolute time to relative time include: Set time baseline ; For any absolute time Calculate relative time - And store all time parameters as integers in seconds.

4. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method as described in claim 3, characterized in that, The steps to build a time-slice-based index include: With a fixed duration Divide the timeline into time slice units, the first The time interval corresponding to each time slice is: , ,in , The end time of the scheduling cycle. It is the floor function; For each feasible solution's time interval Judgment and time slice Does it overlap? Then the feasible solution will be associated with the time slice. The condition for determining overlap is: and , This indicates the relative time point at which a feasible solution begins to occupy resource devices. This indicates the relative time point at which a feasible solution ends the use of resource-intensive equipment. Construct a hash table index where the key is the time slice index. The value is a list of all feasible solutions associated within the time slice.

5. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method according to claim 1, characterized in that, The overall optimization objective function is: , , , These are the weighting coefficients. To improve the user demand satisfaction rate For equipment load balancing rate, Resource fragmentation rate; The task requirement fulfillment rate is: , To use the total number of task requirements that the plan actually fulfills. This represents the total number of tasks required. The equipment load balancing rate is: , This represents the total number of resource devices. Indicates resource equipment Total available time period duration This indicates the total duration of tasks allocated on this resource device. Indicates resource equipment The number of tasks already assigned; Resource fragmentation rate: ,in This indicates the idle time window of the resource device. This indicates a fragmented window that has been determined to be unusable. This is a hyperparameter.

6. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method according to claim 1, characterized in that, The process of creating a conflicting block is as follows: The feasible solution space is grouped by device, and the feasible solutions under each device group are sorted in ascending order of start time; Obtain the feasible solution with the earliest start time among the globally feasible solutions; By using the associated index, other feasible solutions that intersect with the earliest start time in time are found, forming a conflict block.

7. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method according to claim 6, characterized in that, The steps to resolve conflicts include: Calculate the acceptability of each feasible solution in the conflicting block; The feasible solution with the highest acceptance is selected as the chosen plan for the current iteration, where the acceptance is calculated based on the optimization objective function.

8. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method as described in claim 1, characterized in that, The steps for updating the feasible solution space include: Remove feasible solutions from the remaining unscheduled tasks that conflict with current and historical selected plans; Remove the remaining feasible solutions that were not selected from the currently assigned requirements.

9. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method as described in claim 1, characterized in that, The steps for detecting fragmented idle time periods include: After generating the usage plan, calculate the idle time period for each resource device; If the duration of the idle period is less than a preset threshold, the idle period is determined to be a fragment unit.

10. The aerospace telemetry, tracking, and command (TT&C) resource scheduling method as described in claim 1, characterized in that, Fragmentation expansion includes: For idle time periods identified as fragmented units, determine whether adjacent assigned task plans can be transferred to other resource devices for execution; If the task plan If the task is transferred from device A to device B for execution, then the time window originally scheduled for this task on device A will be extended. free space before and after and Merge to form new consecutive idle windows Continuous idle windows length , Indicates the length of the interval.

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