Satellite task scheduling solution space expansion method based on double-layer granularity optimization

Abstract

The invention relates to a satellite task scheduling solution space expansion method based on double-layer granularity optimization. The method comprises the following steps: acquiring an initial task scheduling scheme; preprocessing the initial task scheduling scheme to obtain a potential selectable arc segment set, an uncompleted task set and a completed task set of each task; dynamically adjusting the uncompleted task set based on a double-layer granularity optimization method, and generating a new scheduling solution; and obtaining a task scheduling result based on the new scheduling solution, and evaluating the task scheduling result. Through a double-layer granularity replacement strategy, on the basis of an original scheduling solution, through layer-by-layer replacement optimization, on the premise that resources are not increased or task time is not prolonged, a solution space is effectively expanded, so that an appropriate scheduling time period is found for more tasks, and the task completion rate is improved.

Description

Technical Field

[0001] This invention relates to the field of satellite mission planning and resource scheduling technology, and in particular to a satellite mission scheduling solution space expansion method based on two-layer granularity optimization. Background Technology

[0002] Today, Earth satellites with diverse missions, such as communication, navigation, and remote sensing, have brought about significant changes to various industries. When a satellite enters the visible space of a ground station along its orbit, the ground station uses its telemetry and control (TT&C) antennas to send TT&C commands to the satellite. Based on these commands, the satellite performs tasks in different fields, such as communication, navigation, and remote sensing, to provide services to humanity. In practical scenarios, ground stations can only be established within a certain geographical area, leading to a sharp narrowing of TT&C bandwidth. Especially when a ground station malfunctions, the contradiction between the massive TT&C tasks and the scarcity of ground stations inevitably intensifies.

[0003] Satellite telemetry, tracking, and command (TT&C) mission scheduling is based on the satellite's operation in orbit, within the visible range of ground stations. This involves establishing communication links with the satellite, completing telemetry tracking, acquiring position information, and measuring and analyzing the operational status of onboard ground equipment. The specific process is as follows: First, the user transmits the satellite TT&C mission requirements to the ground control center. The control center analyzes and calculates the satellite mission, plans the satellite's TT&C scheme, and then transmits the generated scheme to the corresponding ground TT&C equipment. Finally, the ground TT&C equipment executes the satellite TT&C mission, simultaneously uploading and injecting satellite mission commands and feeding back data information to the control center.

[0004] In complex task scheduling problems, especially in the field of task scheduling for ground control and measurement equipment, task scheduling systems typically use heuristic algorithms, rule-based algorithms, or algorithms based on global optimization to generate initial scheduling schemes. However, due to various constraints such as ground equipment resources, time windows, and task priorities, scheduling systems often cannot find ideal scheduling time periods for all tasks, resulting in the overall task completion rate falling short of the theoretically optimal level.

[0005] Current scheduling systems often get stuck in local optima when faced with highly complex scheduling problems. This means they optimize the scheduling of current tasks only under limited resources and time constraints, while neglecting the overall optimization of the task pool. The generation of such local optima often leads to some tasks in the system failing to complete on time due to resource conflicts, tight time windows, or other reasons, thus affecting the system's task execution efficiency.

[0006] The limitations of existing optimization methods are mainly reflected in two aspects: First, they rely on increasing ground equipment resources or adjusting the time window for task execution to improve task completion rates. While these methods can address resource constraints to some extent, their applicability is poor when ground equipment resources are limited or the system has strict time limits for task completion, failing to meet the needs of efficient scheduling. Second, most existing optimization methods are limited to local adjustments to the initial scheduling scheme, such as attempting to improve resource utilization by reordering tasks or filling task gaps. This method only has an effect on local tasks and cannot fundamentally solve the problem of low global task completion rates. Furthermore, because the solution space is limited by existing resources, it cannot overcome the limitations of the initial solution.

[0007] Task scheduling is a typical NP-hard problem. As the scale of the task increases, the solution space grows exponentially, making it difficult for optimization algorithms to efficiently search for the globally optimal solution in the complex solution space. To address this problem, how to optimize the scheduling solution, expand the system's task solution space, and improve the overall task scheduling completion rate without increasing resources or extending task time has become a key challenge that urgently needs to be solved in the field of task scheduling. Summary of the Invention

[0008] The purpose of this invention is to provide a satellite mission scheduling solution space expansion method based on two-layer granularity optimization, which can expand the system's mission solution space without increasing resources or extending mission time, solve the problem of initial solution optimization difficulties in mission scheduling, and improve the overall mission completion rate.

[0009] The technical solution adopted in this invention is a satellite mission scheduling solution space expansion method based on two-layer granularity optimization, comprising the following steps: S1. Obtain the initial task scheduling scheme; S2. Preprocess the initial task scheduling scheme to obtain the potential optional arc set, the incomplete task set, and the completed task set for each task. S3. Dynamically adjust the set of unfinished tasks based on the two-layer granularity optimization method to generate a new scheduling solution; specifically including: S31. Set the first-level granularity parameter x, and randomly select x unfinished tasks from the unfinished task set; for each selected unfinished task, perform a 1:1 replacement mechanism to replace it with the completed tasks in the completed task set, and use the successfully replaced completed tasks as the new unfinished tasks to generate a new unfinished task set. S32, Set the particle size parameters for the second layer. For each new unfinished task in the new set of unfinished tasks, the 1:1 replacement mechanism is executed to recursively replace it with the completed tasks in the set of completed tasks, until the replacement depth is reached or the new set of unfinished tasks is empty. The replacement depth is the second-level granularity parameter. ; S33. Iterate through steps S31 to S32 until the maximum number of iterations is reached or the set of unfinished tasks is empty; S4. Based on the new scheduling solution, obtain the task scheduling result and evaluate the task scheduling result.

[0010] The beneficial effects of this invention are as follows: By employing the above-mentioned satellite mission scheduling solution space expansion method based on dual-layer granularity optimization, and through a dual-layer granularity replacement strategy, the solution space is effectively expanded without increasing resources or extending mission time, based on the original scheduling solution and through layer-by-layer replacement optimization. This allows for finding suitable scheduling time periods for more missions, improving mission completion rates. The mission replacement optimization strategy enables more efficient utilization of idle time periods of ground equipment, reducing resource waste. Furthermore, the dual-layer granularity optimization design allows the method to adaptively adjust according to mission requirements, making it suitable for mission scheduling problems of different scales and possessing broad application prospects.

[0011] Preferably, in step S3, the 1:1 replacement mechanism is used to replace the completed tasks in the completed task set, and the successfully replaced completed tasks are used as new incomplete tasks. The specific process is as follows: For the current unfinished task, iterate through its set of potential optional arc segments, and sort all potential optional arc segments in the set of potential optional arc segments in ascending order of arc segment length to obtain the sorted set of potential optional arc segments; The system sequentially examines each potential optional arc in the sorted set of potential optional arcs. Once a potential optional arc is found to be occupied by a completed task, the completed task occupying that arc is removed, and it is verified whether the current incomplete task can be scheduled to that arc. If it is feasible, the current incomplete task is scheduled to that arc, and the status of the removed completed task is set to incomplete, adding it as a new incomplete task to the incomplete task set. If it is not feasible, the completed task occupying that arc is not removed, and the system checks the next potential optional arc.

[0012] Preferably, the specific process for verifying whether the current unfinished task can be scheduled in the arc segment is as follows: if the arc segment does not conflict with any completed task in terms of satellite circle number, and the arc segment does not conflict with any completed task in terms of ground equipment occupancy time, then the current unfinished task can be scheduled in the arc segment; if the arc segment conflicts with any completed task in terms of satellite circle number or ground equipment occupancy time, then the current unfinished task cannot be scheduled in the arc segment; the satellite circle number conflict is defined as not allowing multiple tasks of the same type to be executed under the same satellite and the same circle number.

[0013] Preferably, before step S32, the method further includes: S311. For the new set of unfinished tasks, firstly, determine whether there are any unoccupied potential optional arc segments in the set of potential optional arc segments corresponding to each unfinished task in the new set of unfinished tasks. If so, sort the unoccupied potential optional arc segments of the unfinished task in ascending order of arc segment length, select the shortest unoccupied potential optional arc segment based on the principle of minimum arc segment, assign it to the unfinished task, and delete the unfinished task from the new set of unfinished tasks. Repeat this process until all unfinished tasks have been traversed and a new set of unfinished tasks is generated.

[0014] Preferably, in step S32, for each layer of the multi-layer recursive replacement, the newly replaced unfinished task is determined according to the method in step S311 to determine whether there are any unoccupied potential optional arc segments in its corresponding potential optional arc segment set. If there are, the unoccupied potential optional arc segments of the unfinished task are sorted in ascending order of arc segment length, and the shortest unoccupied potential optional arc segment is selected based on the minimum arc segment principle and assigned to the unfinished task. At the same time, the new unfinished task is deleted from the current new unfinished task set. If there are no, the new unfinished task is replaced at the next layer.

[0015] Preferably, step S1 further includes: reading satellite visibility forecast, mission requirements, and ground equipment constraint information; The satellite visibility forecast includes M visibility forecasts for X satellites over N days. Each visibility forecast includes the satellite's arc segment and its highest elevation angle. The satellite visibility forecasts are stored in a multidimensional array Acc. The task requirements include SN total tasks, each total task contains N days of tasks, each day contains K tasks, and each task contains a satellite number, a task number, an extension link time before the forecast start time, and a link dismantling time after the forecast end time; the task requirements are stored in a multidimensional array req; The ground equipment constraint information includes the ground equipment codes of all available ground equipment and the ground equipment disable time period corresponding to the ground equipment. The ground equipment constraint information is stored in a multidimensional array FB.

[0016] Preferably, in step S2, the preprocessing of the initial task scheduling scheme specifically includes: S21: Based on the obtained satellite visibility forecast, mission requirements, and ground equipment constraints, select a set of potential optional arc segments for each mission; S22: According to the initial task scheduling scheme, mark each task in the initial task scheduling scheme, marking completed tasks and incomplete tasks. Completed tasks are those that have been assigned to an arc segment, and incomplete tasks are those that have not been assigned to an arc segment. Obtain the set of completed tasks and the set of incomplete tasks, and calculate the task completion rate.

[0017] Preferably, in step S21, the specific process of filtering the set of potential optional arc segments for each task includes the following steps: S211. Based on the satellite number and mission number of each mission, extract all visible forecasts for the corresponding satellite for the day from the multidimensional array Acc; S212. Select the forecasts from all visible forecasts for the day whose maximum elevation angle of the satellite is greater than or equal to the maximum elevation angle required by the mission, and form a set of visible forecasts that meet the maximum elevation angle requirement; S213. Select forecasts whose start time is within the allowable offset range from the set of visible forecasts with the highest elevation angle requirement to form a set of visible forecasts that meets the time window constraint. S214. For each visible forecast in the set of visible forecasts constrained by the time window, check whether the ground equipment occupancy time of each visible forecast overlaps with the ground equipment disabled time period. If there is an overlap, the visible forecast is unavailable. If there is no overlap, the arc segment of the visible forecast is marked as a potential optional arc segment for the task. The ground equipment occupancy time = the chain dismantling time after the forecast end time - the chain extension time before the forecast start time.

[0018] Preferably, step S4 includes the following specific process: S41. Based on the new scheduling solution generated in step S3, establish a task scheduling matrix. Each row of the task scheduling matrix represents a successfully scheduled task, and records the arc segment information corresponding to the task. S42. Use the tinyxml2 library function to write the task scheduling matrix into an XML file and output it in a formatted manner to obtain the task scheduling result; S43. Analyze the task completion rate and calculation time of the task scheduling results. Attached Figure Description

[0019] Figure 1 This is a flowchart of a satellite mission scheduling solution space expansion method based on two-layer granularity optimization according to the present invention; Figure 2 This is a flowchart illustrating the process of dynamically adjusting the set of incomplete tasks to generate a new scheduling solution based on a two-layer granularity optimization method in this invention. Figure 3 This is a simulation diagram showing the success rate of task scheduling for measurement and control and data transmission based on the initial scheduling scheme in the experiment of this invention; Figure 4 The figure shows the simulation results of the task scheduling success rate of measurement and control and data transmission obtained by the scheduling scheme proposed in this invention during the experiment. Figure 5 This is a Gantt chart showing the final task scheduling results obtained in the experiment of this invention using the scheduling scheme proposed in this invention. Detailed Implementation

[0020] The invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can implement it based on the description. The scope of protection of the invention is not limited to these specific embodiments.

[0021] This invention provides a satellite mission scheduling solution space expansion method based on two-layer granularity optimization, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain the initial task scheduling plan; In a specific embodiment, the initial mission scheduling scheme is a ground equipment work plan XML file, which contains arc allocation information for all completed missions. Each completed mission corresponds to a PLAN node, and each PLAN node (plan node) contains the spacecraft number, ground equipment number, mission preparation start time, ground equipment tracking start time, ground equipment tracking end time, mission end time, mission code number corresponding to the plan, day number corresponding to the plan, and mission number corresponding to the plan.

[0022] In addition, it also includes reading satellite visibility forecasts, mission requirements, and ground equipment constraints. The satellite visibility forecast is a satellite orbit forecast XML file containing M visibility forecasts for X satellites over N days, where X, N, and M are defined by the data given in the file. Each satellite visibility forecast includes the ground equipment code, satellite total circle number, transit circle number, total number of transit circles, satellite approach time, approach elevation angle, satellite maximum elevation time, maximum elevation angle, satellite departure time, and departure time elevation angle. All forecast information is written into a multidimensional array Acc. The total orbit number refers to the total number of complete orbital cycles that the satellite has completed since its launch. The transit circle number is a unique identifier for a satellite's single transit over a specific ground station, used to constrain mission allocation conflicts within the same transit circle; Satellite arrival time refers to the starting time when the satellite enters the effective coverage area of ​​the ground station's telemetry and control antenna; Satellite departure time refers to the time at which the satellite leaves the effective coverage area of ​​the ground station's telemetry and control antenna. The time interval between the satellite's arrival time and its departure time is called an arc. The highest elevation angle refers to the elevation angle relative to the highest point of the ground station observation point during the satellite's transit. The mission requirements are a weekly mission requirements XML file for high, medium and low orbits, containing SN total missions, each total mission contains N days of missions, each day contains K missions, and each mission contains mission number, mission type (telemetry and control or data transmission), satellite parameters, arc segment selection constraints and a set of available ground equipment. All requirement information is written into a multidimensional array req. The satellite parameters include satellite code, extension link time before the forecast start time (e.g., 10 minutes in advance), and extension link time after the forecast end time (e.g., 5 minutes in advance). The arc selection constraint includes the maximum tracking elevation angle requirement and the task window constraint. The task window constraint includes the constraint method definition, allowable downward offset, the most desired time / lap number, and allowable upward offset. The set of available ground equipment includes ground equipment codes and ground equipment supported functions. Ground equipment supported functions include data transmission only, measurement and control only, or dual modes of data transmission and measurement and control.

[0023] The ground equipment constraint information is a ground equipment function XML file, which contains the ground equipment codes of all available ground equipment and multiple ground equipment disabling time periods corresponding to each ground equipment. Each ground equipment disabling time period includes the ground equipment disabling start time and the ground equipment disabling end time. All ground equipment disabling information is written into a multidimensional array FB.

[0024] The first step described above imports information using the XML read / write module, and the imported information is stored in the corresponding matrix to await subsequent algorithm calls.

[0025] Step 2: Preprocess the initial task scheduling scheme to obtain the set of potential optional arc segments, the set of incomplete tasks, and the set of completed tasks for each task; The specific process of preprocessing the initial task scheduling scheme includes the following steps: S21: Based on the obtained satellite visibility forecast, mission requirements, and ground equipment constraints, select a set of potential optional arc segments for each mission; S22: According to the initial task scheduling scheme, mark each task in the initial task scheduling scheme, marking completed tasks and incomplete tasks. Completed tasks are those that have been assigned to an arc segment, and incomplete tasks are those that have not been assigned to an arc segment. Obtain the set of completed tasks and the set of incomplete tasks, and calculate the task completion rate.

[0026] The specific process for selecting the set of potential optional arc segments for each task is as follows: S211. Based on the satellite number and mission number of each mission, extract all visible forecasts for the corresponding satellite for the day from the multidimensional array Acc; S212. Select the forecasts from all visible forecasts for the day whose maximum elevation angle of the satellite is greater than or equal to the maximum elevation angle required by the mission, and form a set of visible forecasts that meet the maximum elevation angle requirement; S213. Select forecasts whose start time is within the allowable offset range from the set of visible forecasts with the highest elevation angle requirement to form a set of visible forecasts that meets the time window constraint. S214. For each visible forecast in the set of visible forecasts constrained by the time window, check whether the ground equipment occupancy time of each visible forecast overlaps with the ground equipment disabled time period. If there is an overlap, the visible forecast is unavailable. If there is no overlap, the arc segment of the visible forecast is marked as a potential optional arc segment for the task. The ground equipment occupancy time = the chain dismantling time after the forecast end time - the chain extension time before the forecast start time.

[0027] The specific process of marking each task in the initial task scheduling scheme is as follows: Define a vector <int>An array of type Iteration_path is initialized with all elements set to 0. The number of vectors contained in the array Iteration_path is equal to the total number of tasks. This vector is used to store the completion status of each task. For example, Iteration_path[0] represents the completion status of the first task, and Iteration_path[1] represents the completion status of the second task. Based on the initial task scheduling scheme data read in step S1, the task number, the day number, and the task number corresponding to the plan in each PLAN node are used to calculate which requirement (i.e., the requirement index) the task is. The requirement index is calculated as: task number x total number of task codes + day number corresponding to the plan x total number of days + subtask number corresponding to the plan. In each PLAN node The spacecraft number (i.e., satellite number) and the day number corresponding to the plan are used to find all visible forecasts corresponding to the satellite on that day in the visible forecast. The arc start time, arc end time, and equipment number of the visible forecast are compared with the equipment tracking start time, equipment tracking end time, and equipment number in the PLAN node. It is determined whether the three are the same. If they are the same, the value corresponding to the index of the requirement (which requirement) in the array Iteration_path is changed to the value corresponding to the index of the visible forecast of the satellite on that day during the traversal process. For example, Iteration_path[8]=216 indicates that the 9th requirement has been completed (index starts from 0), and the arc segment allocated to the requirement is the 217th visible forecast of the satellite on that day (index starts from 0) corresponding to the satellite day number constraint of the mission. Traverse all PLAN nodes in the task scheduling scheme data. Record the corresponding completed requirement number and the arc number that completed the requirement in the array Iteration_path using the above method. The number of elements with a value of 0 in the array Iteration_path is the number of incomplete tasks, and the number of non-zero elements is the number of completed tasks. Calculate the current scheduling solution by the ratio of the number of completed tasks to the total number of tasks, calculate the task completion rate, and record the set of incomplete tasks. The set of incomplete tasks is the index set of all elements with a value of 0 in the array Iteration_path.

[0028] Step 3: Dynamically adjust the set of unfinished tasks based on the two-layer granularity optimization method to generate a new scheduling solution; by dynamically adjusting the task scheduling scheme through two-layer granularity optimization, more feasible execution time windows are found for unfinished tasks, ultimately improving the overall task completion rate; such as Figure 2 As shown, it specifically includes: S31. Set the first-level granularity parameter x, and randomly select x unfinished tasks from the unfinished task set; for each selected unfinished task, perform a 1:1 replacement mechanism to replace it with the completed tasks in the completed task set, and use the successfully replaced completed tasks as the new unfinished tasks to generate a new unfinished task set. S311. For the new set of unfinished tasks, firstly determine whether there are any unoccupied potential optional arc segments in the set of potential optional arc segments corresponding to each unfinished task in the new set of unfinished tasks. If so, sort the unoccupied potential optional arc segments of the unfinished task in ascending order of arc segment length, select the shortest unoccupied potential optional arc segment based on the principle of minimum arc segment, assign it to the unfinished task, and delete the unfinished task from the current new set of unfinished tasks. Continue until all unfinished tasks have been traversed and a new set of unfinished tasks is generated. S32, Set the particle size parameters for the second layer. For each new unfinished task in the new set of unfinished tasks, the 1:1 replacement mechanism is executed to recursively replace it with the completed tasks in the set of completed tasks, until the replacement depth is reached or the new set of unfinished tasks is empty. The replacement depth is the second-level granularity parameter. ; S33. Iterate through steps S31 to S32 until the maximum number of iterations is reached or the set of unfinished tasks is empty.

[0029] The specific principle of the 1:1 replacement mechanism is as follows: For the current unfinished task, iterate through its set of potential optional arc segments, and sort all potential optional arc segments in the set of potential optional arc segments in ascending order of arc segment length to obtain the sorted set of potential optional arc segments; The process involves checking each potential optional arc in the sorted set of potential optional arcs in sequence, or checking each potential optional arc in a random order. Once a potential optional arc is found to be occupied by a completed task, the completed task occupying that arc is removed, and it is verified whether the current incomplete task can be arranged in that arc. If it is verified to be feasible, the current incomplete task is arranged in that arc, the status of the removed completed task is set to incomplete, and the successfully replaced completed task is added to the incomplete task set as a new incomplete task. If it is verified to be infeasible, the completed task occupying that arc is not removed, and the next potential optional arc is checked.

[0030] In the 1:1 replacement mechanism, the specific principle for verifying whether the currently unfinished task can be scheduled in the arc segment is as follows: If the arc segment does not conflict with any completed tasks in terms of satellite circle number and equipment occupancy time, then the current unfinished task can be scheduled in that arc segment; if the arc segment conflicts with any completed task in terms of satellite circle number or equipment occupancy time, then the current unfinished task cannot be scheduled in that arc segment; the satellite circle number conflict is defined as not allowing multiple tasks of the same type to be executed under the same satellite and circle number.

[0031] In step S32, for each layer of the multi-layer recursive replacement, the newly replaced unfinished task is determined according to the method in step S311 to determine whether there are any unoccupied potential optional arc segments in its corresponding potential optional arc segment set. If there are, the unoccupied potential optional arc segments of the unfinished task are sorted in ascending order of arc segment length, and the shortest unoccupied potential optional arc segment is selected based on the principle of minimum arc segment and assigned to the unfinished task. At the same time, the new unfinished task is deleted from the current set of new unfinished tasks. If there are no unoccupied arc segments, the new unfinished task is replaced at the next layer.

[0032] In this invention, before executing each layer of the 1:1 replacement mechanism, it is necessary to first determine whether there are any unoccupied potential optional arc segments in the potential optional arc segment set corresponding to each unoccupied task in the new unoccupied task set. If so, the unoccupied potential optional arc segments of the unoccupied task are sorted in ascending order of arc segment length. Based on the principle of minimum arc segment, the shortest unoccupied potential optional arc segment is selected and assigned to the unoccupied task. At the same time, the unoccupied task is deleted from the current new unoccupied task set. This process continues until all unoccupied tasks have been traversed and a new unoccupied task set is generated.

[0033] In this invention, by using the 1:1 replacement mechanism between incomplete and completed tasks in step S32, the potential optional arc segments of the newly replaced incomplete tasks are used as a new solution space, thereby expanding the solution space and increasing the probability of task completion without reducing the task completion rate.

[0034] By performing a multi-layered recursive 1:1 replacement between incomplete and completed tasks in step S33, the understanding space is further expanded during each 1:1 replacement process. The replacement depth can be adjusted by controlling the size of the second layer granularity, thereby controlling the computational efficiency. The size of the second layer granularity can be adaptively adjusted according to the task scale.

[0035] Step 4: Based on the new scheduling solution generated in step S3, obtain the task scheduling result and evaluate the task scheduling result. The specific process includes: S41. Based on the new scheduling solution generated in step S3, establish a task scheduling matrix. Each row of the task scheduling matrix represents a successfully scheduled task and records the arc segment corresponding to the task. It also records information such as satellite number, equipment number, satellite circle number, preparation start time, tracking start time, tracking end time, chain break end time, and demand index number. S42. Use the tinyxml2 library function to write the task scheduling matrix into an XML file and output it in a formatted manner to obtain the task scheduling result; S43. Analyze the task completion rate and calculation time of the task scheduling results.

[0036] To evaluate the effectiveness of the algorithm, let S represent the satisfaction of the measurement and control task and D represent the satisfaction of the data transmission task. The evaluation index for the satisfaction of the measurement and control task is as follows: S43. Analyze the task completion rate and calculation time of the task scheduling results.

[0037] To evaluate the effectiveness of the algorithm, let S represent the satisfaction of the measurement and control task and D represent the satisfaction of the data transmission task. The evaluation index for the satisfaction of the measurement and control task is as follows: ;in, The number of telemetry and control tasks completed by the scheduling department. This represents the total number of measurement and control tasks.

[0038] The evaluation index for data transmission task satisfaction is in the following form: ;in, The number of telemetry and control tasks completed by the scheduling department. This represents the total number of measurement and control tasks.

[0039] Based on the above technical solutions, the satellite mission scheduling solution space expansion method based on dual-layer granularity optimization provided by this invention evaluates the planning results after mission planning is completed. This method dynamically adjusts the scheduling scheme and expands the solution space through a dual-layer mission replacement strategy (granularity optimization), rather than relying solely on local adjustments. Without increasing resources, it improves equipment idle time utilization, reduces task conflicts, and enhances the overall mission completion rate. It is applicable to resource-constrained scheduling scenarios such as satellite telemetry, tracking, and command (TT&C), and is particularly suitable for highly complex and multi-constrained mission planning problems.

[0040] The following experiments demonstrate the superiority of the satellite mission scheduling solution space expansion method based on dual-layer granularity optimization provided in this invention.

[0041] This example illustrates the 8-day tracking and control mission for 400 low-Earth orbit satellites using 47 tracking and control devices. The simulation dataset used is from Dataset 1 of the Large-Scale Tracking and Control Data Transmission Mission Station Network Scheduling in Subject 2 of the 4th "Tianzhi Cup" Artificial Intelligence Challenge in the field of spacecraft management. Basic information on the tracking and control equipment and mission requirements is shown in Table 1. Table 1 Basic Information on Measurement and Control Scenarios .

[0042] The experimental environment was a Win11 64-bit system with an R5-7530 CPU (2.4GHz six-core, 16GB RAM) and VS2019. To more objectively compare algorithm performance, task scheduling success rate was used to measure the algorithm's solution quality. Based on the above scenario experiments, the initial scheduling scheme's control and data transmission task scheduling success rates were as follows: Figure 3 As shown.

[0043] A two-layer granularity-based algorithm for expanding the solution space is employed. Based on the initial scheduling scheme and plan, satellite visibility forecasts, task requirements, and equipment constraints, the first-layer granularity (number of incomplete replacement requirements) is set to 10, the second-layer granularity (maximum replacement depth) to 50, and the maximum number of iterations is 100. The resulting task scheduling success rate is as follows: Figure 4 As shown, Figure 3 and Figure 4 As can be seen, the satellite mission scheduling solution space expansion method based on dual-layer granularity optimization proposed in this invention can improve both data transmission completion rate and telemetry and control completion rate to 100%. Figure 5 As shown, a Gantt chart of the final scheduling results is presented, which more intuitively demonstrates the scheduling of ground station resources.< / int>

Claims

1. A satellite mission scheduling solution space expansion method based on two-layer granularity optimization, characterized in that: Includes the following steps: S1. Obtain the initial task scheduling scheme; S2. Preprocess the initial task scheduling scheme to obtain the potential optional arc set, the incomplete task set, and the completed task set for each task. S3. Dynamically adjust the set of unfinished tasks based on the two-layer granularity optimization method to generate a new scheduling solution; specifically including: S31. Set the first-level granularity parameter x, and randomly select x unfinished tasks from the unfinished task set; for each selected unfinished task, perform a 1:1 replacement mechanism to replace it with the completed tasks in the completed task set, and use the successfully replaced completed tasks as the new unfinished tasks to generate a new unfinished task set. S32, Set the particle size parameters for the second layer. For each new unfinished task in the new set of unfinished tasks, the 1:1 replacement mechanism is executed to recursively replace it with the completed tasks in the set of completed tasks, until the replacement depth is reached or the new set of unfinished tasks is empty. The replacement depth is the second-level granularity parameter. ; S33. Iterate through steps S31 to S32 until the maximum number of iterations is reached or the set of unfinished tasks is empty; S4. Based on the new scheduling solution, obtain the task scheduling result and evaluate the task scheduling result.

2. The satellite mission scheduling solution space expansion method based on dual-layer granularity optimization according to claim 1, characterized in that: In step S3, the specific process of replacing the completed tasks in the completed task set with the 1:1 replacement mechanism and taking the successfully replaced completed tasks as new unfinished tasks is as follows: For the current unfinished task, traverse its potential optional arc segment set, sort all potential optional arc segments in the potential optional arc segment set according to the arc segment length from smallest to largest, and obtain the sorted potential optional arc segment set. The system sequentially examines each potential optional arc in the sorted set of potential optional arcs. Once a potential optional arc is found to be occupied by a completed task, the completed task occupying that arc is removed, and the current incomplete task is verified to be able to be scheduled in that arc. If the verification is feasible, the current incomplete task is scheduled to that arc, and the status of the removed completed task is set to incomplete, and it is added to the incomplete task set as a new incomplete task. If the verification is not feasible, the system traverses the next potential optional arc.

3. The satellite mission scheduling solution space expansion method based on dual-layer granularity optimization according to claim 2, characterized in that: The specific process for verifying whether a current unfinished task can be scheduled in the arc segment is as follows: if the arc segment does not conflict with any completed tasks in terms of satellite circle number, and the arc segment does not conflict with any completed tasks in terms of ground equipment occupancy time, then the current unfinished task can be scheduled in the arc segment; if the arc segment conflicts with any completed task in terms of satellite circle number or ground equipment occupancy time, then the current unfinished task cannot be scheduled in the arc segment. The satellite circle number conflict is defined as not allowing multiple tasks of the same type to be executed under the same satellite and circle number.

4. A satellite mission scheduling solution space expansion method based on dual-layer granularity optimization according to claim 1 or 3, characterized in that: Before step S32, the following is also included: S311. For the new set of unfinished tasks, firstly determine whether there are any unoccupied potential optional arc segments in the set of potential optional arc segments corresponding to each unfinished task in the new set of unfinished tasks. If so, sort the unoccupied potential optional arc segments of the unfinished task in ascending order of arc segment length, select the shortest unoccupied potential optional arc segment based on the principle of minimum arc segment, assign it to the unfinished task, and delete the unfinished task from the current new set of unfinished tasks. Repeat this process until all unfinished tasks have been traversed and a new set of unfinished tasks is generated.

5. The satellite mission scheduling solution space expansion method based on dual-layer granularity optimization according to claim 4, characterized in that: In step S32, for each layer of the multi-layer recursive replacement, the newly replaced unfinished task is checked to see if there are any unoccupied potential optional arc segments in its corresponding set of potential optional arc segments. If so, the unoccupied potential optional arc segments of the unfinished task are sorted in ascending order of arc segment length, and the shortest unoccupied potential optional arc segment is selected based on the principle of minimum arc segment and assigned to the unfinished task. At the same time, the new unfinished task is deleted from the current set of new unfinished tasks. If not, the new unfinished task is replaced at the next layer.

6. The satellite mission scheduling solution space expansion method based on dual-layer granularity optimization according to claim 1, characterized in that: Step S1 further includes: reading satellite visibility forecasts, mission requirements, and ground equipment constraint information; The satellite visibility forecast includes M visibility forecasts for X satellites over N days. Each visibility forecast includes the satellite's arc segment and its highest elevation angle. The satellite visibility forecasts are stored in a multidimensional array Acc. The task requirements include SN total tasks, each total task contains N days of tasks, each day contains K tasks, and each task contains a satellite number, a task number, an extension link time before the forecast start time, and a link dismantling time after the forecast end time; the task requirements are stored in a multidimensional array req; The ground equipment constraint information includes the equipment codes of all available ground equipment and the corresponding equipment disable time period. The equipment constraint information is stored in a multidimensional array FB.

7. The satellite mission scheduling solution space expansion method based on dual-layer granularity optimization according to claim 6, characterized in that: In step S2, the preprocessing of the initial task scheduling scheme specifically includes: S21: Based on the obtained satellite visibility forecast, mission requirements, and ground equipment constraints, select a set of potential optional arc segments for each mission; S22: According to the initial task scheduling scheme, mark each task in the initial task scheduling scheme, marking completed tasks and incomplete tasks. Completed tasks are those that have been assigned to an arc segment, and incomplete tasks are those that have not been assigned to an arc segment. Obtain the set of completed tasks and the set of incomplete tasks, and calculate the task completion rate.

8. The satellite mission scheduling solution space expansion method based on two-layer granularity optimization according to claim 7, characterized in that: In step S21, the specific process of filtering out the set of potential optional arc segments for each task includes the following steps: S211. Based on the satellite number and mission number of each mission, extract all visible forecasts for the corresponding satellite for the day from the multidimensional array Acc; S212. Select the forecasts from all visible forecasts for the day whose maximum elevation angle of the satellite is greater than or equal to the maximum elevation angle required by the mission, and form a set of visible forecasts that meet the maximum elevation angle requirement; S213. Select forecasts whose start time is within the allowable offset range from the set of visible forecasts with the highest elevation angle requirement to form a set of visible forecasts that meets the time window constraint. S214. For each visible forecast in the set of visible forecasts constrained by the time window, check whether the ground equipment occupancy time of each visible forecast overlaps with the ground equipment disabled time period. If there is an overlap, the visible forecast is unavailable. If there is no overlap, the arc segment of the visible forecast is marked as a potential optional arc segment for the task. The ground equipment occupancy time = the chain dismantling time after the forecast end time - the chain extension time before the forecast start time.

9. A satellite mission scheduling solution space expansion method based on dual-layer granularity optimization according to claim 1, 5, or 8, characterized in that: The specific process of step S4 includes: S41. Based on the new scheduling solution generated in step S3, establish a task scheduling matrix. Each row of the task scheduling matrix represents a successfully scheduled task, and the arc segment corresponding to the task is recorded. S42. Use the tinyxml2 library function to write the task scheduling matrix into an XML file and output it in a formatted manner to obtain the task scheduling result; S43. Analyze the task completion rate and calculation time of the task scheduling results.

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