A large-scale TT&C data transmission task station network resource scheduling problem modeling and multi-stage scheduling method

Abstract

The application discloses a large-scale TT&C (Tracking, Telemetry and Command) data transmission task station network resource scheduling problem modeling and multi-stage scheduling method, and aims at the problems of a normalized mathematical model construction and a high-performance optimization strategy and algorithm design of large-scale TT&C data transmission task station network resource scheduling, establishes a TT&C data transmission integrated integer programming model, designs four kinds of neighborhood operators based on constraint propagation for large-scale TT&C data transmission task scheduling, and provides a multi-stage optimization strategy and an algorithm framework for large-scale TT&C data transmission resource scheduling, so as to propose a reasonable and efficient scheme to cope with the requirements of large-scale TT&C data transmission task station network resource scheduling.

Description

Technical Field

[0001] This invention belongs to the field of satellite control technology, specifically involving a modeling and multi-stage scheduling method for resource scheduling problems in large-scale telemetry, tracking, and data transmission missions. Background Technology

[0002] With the rapid development of aerospace technology, the number of satellites in orbit is growing exponentially, and the scale of missions is becoming increasingly large, placing higher demands on the telemetry, tracking, and command (TT&C) and data transmission capabilities of ground stations. However, as a crucial aerospace infrastructure, the growth in the number and capacity of ground station resources is far outpaced by the growth rate of satellite numbers, leading to increasingly strained ground station resources. Therefore, how to rationally and efficiently allocate ground station resources, maximize the utilization benefits of the ground station network, and meet the large-scale TT&C and data transmission needs of users is a critical issue that urgently needs to be addressed in the field of satellite ground station network resource scheduling. Large-scale TT&C and data transmission mission network resource scheduling technology, as one of the key technologies in the aerospace field, is of great significance for ensuring the long-term stable operation of satellites and improving satellite data transmission efficiency.

[0003] Currently, in the actual control process, the scheduling of station network resources for large-scale telemetry, tracking, and command (TT&C) and data transmission tasks faces the following problems: 1) There is a lack of standardized and intuitive integrated mathematical models for scheduling TT&C and data transmission, which present constraints and benefits, and thus guide algorithm design. In traditional methods, TT&C and data transmission are usually modeled separately, which leads to the inability to fully utilize station resources and waste of resources. At the same time, existing scheduling models are not applicable to new satellites or ground stations that can simultaneously perform TT&C and data transmission, and cannot fully utilize the capabilities of the stations. 2) In the large-scale TT&C and data transmission task station network resource scheduling problem, there are numerous resources and demands, and the solution space is huge, and the efficiency of traditional algorithms needs to be further improved. Taking a 7-day scheduling requirement as an example, considering the scheduling of both TT&C and data transmission tasks, the algorithm needs to solve the scheduling problem of more than 20,000 tasks at the same time, resulting in a huge solution space. Therefore, traditional algorithms and optimization strategies are prone to getting trapped in local optima, making it difficult to fully utilize station network resources. There is an urgent need for reasonable and efficient algorithms to meet the requirements of large-scale TT&C and data transmission task station network resource scheduling. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned technical problems by proposing a modeling and multi-stage scheduling method for resource scheduling in large-scale telemetry and control data transmission task stations, comprising the following steps:

[0005] S1. Each ground station server acquires the satellite telemetry, tracking, and data transmission task set R;

[0006] Where R = {r i |1≤i≤n r ,|R|=n r}; Each r in set R iEach of them represents a task, which is expressed in a five-tuple:

[0007] r i = {i, s i , du i , A i , type, <RCO i >, <SDA i >} ;

[0008] where i is the index value of the TT&C task r i , s i represents the satellite used by the task, d i represents the duration of the task, A i is the set of all available arcs of the TT&C task, type represents the type of the task. is the arc selection constraint of the task i, RCO i = {MME i , CT i , SD0 i , SD1 i , SD2 i}, MME i is the minimum tracking elevation angle of the task i, CT i is the arc selection constraint type of the task i, SD0 i is the lower offset of the allowed range of the task i, SD1 i is the most desired time / revolution number of the task i, SD2 i is the upper offset of the allowed range of the task i; SDA i is the set of available devices of the task i, SDA i = {SD i1 , SD i2 , …, SD ik} ;

[0009] S2. The respective ground station servers obtain the set of arcs A, which is defined as: A = {a i | 1≤i≤n a , |A| = n a} ;

[0010] where a i represents an arc, and the a i is expressed as a thirteen-tuple:

[0011] a i = {i, s i , d i , RTN j , RI j , RCN j , RF jTI j AI j TM j AM j TO j AO j};

[0012] Wherein, RTN j is the total orbit number of satellite orbit movement; RI j represents the transit orbit number; RCN j is the orbit number visible in China this time; RF j represents the total transit orbit number; TI j represents the properties of the ascending / descending track of the arc segment; is the entry time; AI j represents the entry pitch angle; TM j represents the time of the highest elevation angle; AM j represents the pitch angle of the highest elevation angle; TO j represents the exit time; AO j represents the pitch angle of the exit time;

[0013] S3. Each ground station server obtains all ground station information, and the ground station is defined as: D={d i |1≤i≤n d ,|D|=n d};d i is the ith ground station;

[0014] The d i is expressed as a three-tuple: d i ={i,fb i ,func i};

[0015] Wherein, fb i represents the disabled arc segment set, and func i represents the function of the ith device. The device functions include: TT&C, data transmission, TT&C / data transmission, TT&C+data transmission. TT&C / data transmission means that the device can perform one of TT&C task or data transmission task at the same time, TT&C+data transmission means that the device can perform one of TT&C task or data transmission task at the same time for different satellites, and can perform TT&C and data transmission tasks at the same time for the same satellite.

[0016] S4. Each ground station server obtains a target satellite set S;

[0017] The set S contains all satellites participating in TT&C and data transmission tasks, and the task target satellite of any task r i in the task set R is contained in the set S.

[0018] Further, in the decision relationship, the task ri , A i optional arc segment, in the case of no considering the equipment working time conflict constraint, the task r i can be executed on any arc segment in the optional arc segment set A i .

[0019] Further, in the decision relationship, the following formula is used to describe:

[0020]

[0021] The variable x i describes the decision relationship between the task and the arc segment.

[0022] Further, the execution of each satellite task is unique, and the configuration of the above decision variable makes the constraint naturally satisfied

[0023] The decision relationship between the task and the arc segment is:

[0024]

[0025] Where, r i is the i-th task, a ij is the j-th arc segment of the task i, and x i is the decision variable of the task i.

[0026] Further, the scheduling scheme of the satellite TT&C data transmission scheduling problem is represented as a decision vector X:

[0027]

[0028] Further, in the preprocessing process, static constraints are established,

[0029] Dynamic constraints are adjusted in the problem processing process.

[0030] Further, the static constraints include: arc segment preference constraints, arc segment availability constraints, antenna function constraints.

[0031] Further, the dynamic constraints include: same-satellite same-type task different-orbit constraints, equipment working time non-conflict constraints, task execution uniqueness constraints.

[0032] Further, the reward of each task of the data transmission task is set to 10, the reward of each task of the TT&C task is set to 1, and the decision variable x i is used to represent the arc segment selected by the task r i , and the reward function is set as:

[0033] Where,

[0034] The more the number of tasks assigned to the arc segment, the better under the condition of meeting the constraints. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0036] Figure 1 is the decision relationship diagram between the tasks and the arc segments of the present application;

[0037] Figure 2 is the 0-1 integer decision variable diagram in the satellite TT&C and data transmission task scheduling problem of the present application;

[0038] Figure 3 is the multi-stage optimization strategy flowchart of the present application. DETAILED DESCRIPTION

[0039] The exemplary embodiments of the present application are described below with reference to the accompanying drawings, including various details of the embodiments of the present application to help understanding. They should be considered as merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also, for the sake of clarity and conciseness, the description below omits the description of well-known functions and structures.

[0040] The present application aims at the standardized mathematical model construction of large-scale TT&C and data transmission task station network resource scheduling, and the high-performance optimization strategy and algorithm design problem, 1) an integrated integer programming model of TT&C and data transmission is established, 2) four kinds of neighborhood operators based on constraint propagation for large-scale TT&C and data transmission task scheduling are designed, and 3) a multi-stage optimization strategy and algorithm framework for large-scale TT&C and data transmission resource scheduling is provided.

[0041] I. Establishing an integer programming model for large-scale TT&C and data transmission tasks

[0042] The mathematical expressions of variables, constraint conditions and objective functions in the scheduling problem are proposed, and the integer programming model is standardized.

[0043] The variable symbols and definitions involved in the present application are as follows:

[0044] Table 1 Symbol and definition

[0045]

[0046]

[0047] (I) The scheduling resource description contains task description, arc segment description, ground station description and satellite description, specifically:

[0048] 1. Task description

[0049] The present application uses R to represent the set of satellite TT&C and data transmission tasks, which can be defined as:

[0050] R={r i |1≤i≤n r ,|R|=n r}

[0051] Each r i in the set R represents a task, which can be represented as a five-tuple:

[0052] r i ={i,s i ,du i ,A i ,type,<RCO i >,<SDA i >}

[0053] Wherein, i is the index value of the TT&C task r i , s i represents the satellite used by the task, d i represents the duration of the task, A i is the set of all available arc segments of the TT&C task, type represents the type of the task. is the arc segment selection constraint of task i, RCO i ={MME i ,CT i ,SD0 i ,SD1 i ,SD2 i},MME i is the minimum tracking elevation angle of task i, which means that the maximum elevation angle of the arc segment allocated to the task must be greater than the value, CT i is the arc segment selection constraint type of task i, SD0 i is the lower offset of the allowed range of task i, SD1 i is the most desired time / revolution number of task i, SD2 i is the upper offset of the allowed range of task i; SDA i is the set of available devices of task i, SDA i ={SD i1 ,SD i2 ,…,SD ik},

[0054] 2. Arc description

[0055] In the scheduling problem of large-scale satellite TT&C and data transmission tasks, "arc" refers to the part of the orbit in which the satellite and the ground station can communicate within a certain time window. Each arc involves the occupation of multiple resources such as time, space, and frequency. Due to the large number of satellites and limited ground station resources, arc conflicts may occur, i.e., multiple tasks need to use the same resources at the same time.

[0056] In the present invention, the arc set is defined as:

[0057] A={a i |1≤i≤n a ,|A|=n a}

[0058] where a i represents an arc, which can be represented as a thirteen-tuple:

[0059] a i ={i,s i ,d i ,RTN j ,RI j ,RCN j ,RF j ,TI j ,AI j ,TM j ,AM j ,TO j ,AO j}

[0060] where RTN j is the total orbit number of satellite motion; RI j represents the transit orbit number; RCN j is the orbit number visible in China; RF j represents the total number of transits; TI j represents the properties of the arc; is the time of entry; AI j represents the entry pitch angle; TM j represents the time of maximum elevation; AM j represents the maximum elevation pitch angle; TO j represents the time of exit; AO j represents the exit pitch angle.

[0061] 3. Ground station description

[0062] The ground station, as the core infrastructure of satellite TT&C and data transmission task, is responsible for communication with orbiting satellites, including data reception, command sending and orbit measurement and other key operations. The ground station is equipped with high-sensitivity antennas, receivers and data processing equipment, which can track the satellite orbit and exchange data within the predetermined time window. Each ground station can only communicate when the satellite flies over it, and the resources are limited within a certain period.

[0063] In the present application, the ground station can be defined as:

[0064] D = {d i |1≤i≤n d ,|D|=n d}wherein d i can be represented as a triple:

[0065] d i = {i,fb i ,func i}

[0066] wherein fb i represents the disabled arc segment set, and func i represents the function of the i-th device.

[0067] 4. Satellite description

[0068] The satellite resource description relates to the target satellite set of TT&C and data transmission task, represented as set S, which contains all satellites participating in TT&C and data transmission task. For any task r i , its task target satellite is contained in set S.

[0069] (II) Decision variables include decision relationship, integer decision variable and decision vector

[0070] The present application describes the decision relationship between tasks and arc segments, constructs integer decision variables and decision matrix, and builds a decision model for satellite TT&C and data transmission task scheduling.

[0071] 1. Decision relationship

[0072] Each task r i in the task set R has |A i | optional arc segments, and task r i can be executed on any arc segment in the optional arc segment set A i without considering the device working time conflict constraint. As shown in the following formula, the decision relationship between tasks and arc segments is represented as follows: Figure 1 Figure 1

[0073] 2. Integer decision variable ​​

[0074] For the above decision relationship, the present application uses integer variables to describe this relationship, such as formula:

[0075]

[0076] Wherein, R - task set;

[0077] r i - the i-th task in the task set;

[0078] The variable x i of the above formula describes the decision relationship between the task and the arc segment. At the same time, the execution of each satellite task has uniqueness, so the decision variable should also satisfy the following conditions:

[0079]

[0080] So far, the decision relationship between the task and the arc segment can be intuitively expressed as:

[0081]

[0082] Table 2 Decision variable meaning

[0083]

[0084] 3. Decision vector

[0085] In the above decision variable vector satellite TT&C data transmission scheduling problem, the scheduling scheme can be expressed as a decision vector X as follows:

[0086]

[0087] To intuitively present the decision relationship between the task and the communication opportunity, and further illustrate the integer decision model constructed above, the present application gives a specific integer decision model example, as shown in Figure 2 .

[0088] (Three) Constraints include static constraints and dynamic constraints. Static constraints are established in the preprocessing process, and dynamic constraints are adjusted in the problem processing process. The static constraints include: arc segment preference constraints, arc segment availability constraints, antenna function constraints. The dynamic constraints include: same star same type task different circle constraints, device working time conflict free constraints, task execution uniqueness constraints.

[0089] Static constraints refer to constraint conditions that can be satisfied through data screening, format conversion and design data structure and other preprocessing means. After preprocessing, such constraints usually do not need to be considered again in the solving process. Static constraints include:

[0090] 1. Arc segment preference constraint

[0091] Arc preference constraints describe the preferences and restrictions of each task proposer regarding the arcs used for telemetry, monitoring, or data transmission. Tasks can only be executed on arcs that meet the user's preference requirements. This constraint affects the list of available arcs for a task and is a typical static constraint.

[0092] 2. Availability Constraints for Arc Segments

[0093] Arc availability constraints prohibit tasks from operating on arcs during device-disabled periods; that is, the device can only operate during non-disabled periods.

[0094]

[0095] 3. Antenna functional constraints

[0096] There are four types of antennas in the antenna equipment: telemetry and control (TT&C), data transmission, TT&C or data transmission alone, and TT&C and data transmission. Data transmission tasks can only be performed on antenna equipment that supports data transmission; while TT&C tasks can only be performed on TT&C antenna equipment.

[0097]

[0098] Dynamic constraints are constraints whose satisfaction can only be determined after the arc segments have been assigned to the task. Since the matching relationship between the task and the arc segments cannot be determined before the problem is solved, the satisfaction of these constraints can only be dynamically determined during the solution process by designing a reasonable strategy and observing changes in the solution. Dynamic constraints include:

[0099] 1. Same star and type of mission, different circle constraints

[0100] The telemetry, tracking, and command (TT&C) and data transmission tasks for the same satellite cannot be repeated within the same orbit:

[0101]

[0102] type i =type j ,RI i =RI j

[0103] in

[0104] 2. No conflict constraints on equipment operating time.

[0105]

[0106] Indicator m (x i ) is an indicator function that indicates task i within time window x. iwhether the time period of the task r is conflicted with the time period of other tasks of the device m, if conflicted, 1, if not conflicted, 0.

[0107] 3. Task execution uniqueness constraint

[0108] Due to the setting of the decision variable x of the present application, the value of x i can only be one, so the constraint is naturally satisfied.

[0109] Set the objective function:

[0110] Since the higher the task completion rate is in the management and control, and the completion difficulty of the data transmission task is greater than that of the measurement and control task, the present application plans the data transmission and measurement and control task benefits, sets the task benefit of each data transmission task as 10, and sets the task benefit of each measurement and control task as 1. The present application introduces the decision variable x i to represent the arc segment selected and used by the task r i . Since the optimization objective of the present application is to realize the maximum completion rate of the task, the objective benefit function can be set as:

[0111]

[0112] wherein,

[0113] The optimization objective indicates that the more the number of tasks allocated to the arc segment is, the better.

[0114] The above establishes the integer programming model for large-scale measurement and control data transmission tasks, and normatively gives the mathematical expressions of the decision variables, constraint conditions and objective functions in the model, which helps the management and control department to understand the essence of the combinatorial optimization of the inter-satellite link scheduling, and further better guides the algorithm and operator design.

[0115] II. Neighborhood operator design based on constraint propagation

[0116] In order to enhance the practicability of the model and further optimize the solving efficiency, the present application proposes two effective data preprocessing strategies and four neighborhood operator designs after in-depth analysis of the characteristics of the problem. These strategies aim to accurately filter and convert the original data to reduce the complexity of subsequent model solving, thereby simplifying the solving process. First, the following two core data preprocessing methods are introduced in detail, which lay a solid foundation for subsequent practical application and optimization work.

[0117] Constraint propagation is an efficient algorithm technique in solving constraint satisfaction problems. The basic principle is to continuously update and propagate the value range of the variable during the problem solving process through the known constraint conditions, so as to reduce the search space and improve the efficiency of problem solving.

[0118] (I) Task Optional Time Window Preprocessing for Constraint Propagation

[0119] To reduce the size of the solution space, this strategy filters the available segments for each task and adds them to the task's available segment list. In the subsequent solving process, each task can only use the segments in its available segment list. This strategy effectively reduces the decision space of selecting segments for each task from the entire segment list to the available segment list, significantly reducing the segment selection space for each task. The specific process of optional time window preprocessing is as follows:

[0120] Step 1: Receive the task set R, where each task r i represents a satellite operation that needs to be scheduled. Receive the segment set A, where each segment a i represents a time window in which a ground station can see a satellite. For each task r i , create an empty "available segment list" to store segments that meet the task's constraint conditions.

[0121] Step 2: For each task r i in the task set R, execute steps 3 and 4.

[0122] Step 3: For each segment a i in the segment set A, execute step 4.

[0123] Step 4: Check if segment a i meets all the constraint conditions of task r i . If segment a i meets all the constraint conditions of task r i , add the segment a i to the "available segment list" of task r i .

[0124] Step 5: Complete the traversal of all tasks and all segments. The loop ends with the output result: each task r i corresponds to an "available segment list" containing all available segments that meet its constraint conditions.

[0125] It is worth noting that this preprocessing strategy only modifies the available segment list of each task, without changing the order of the segment list and task list, so it can guarantee the correctness and consistency of the decision matrix.

[0126] (II) Segment Conflict Preprocessing

[0127] In the scheduling problem of the present application, the dynamic constraints mainly include the non-conflict constraint of equipment working time and the constraint of different orbits for the same star and the same type of task. Under these constraints, the conflicts between the arc segments have certain predictability, so the conflicts between the arc segments can be pre-computed to provide prior knowledge for the solving process, and guide the algorithm to select the arc segment for the task according to the conflict degree of the arc segment.

[0128] After deeply analyzing the problem characteristics, constraint mechanisms and the functional characteristics of the ground station, the present application considers that the pre-processing of the arc segment conflicts should consider the following points:

[0129] Since the chain building preparation time and the chain breaking release time of each task are 300s and 60s, the working occupation time of the ground station when the arc segment is selected by the task can be obtained by advancing the start time of the arc segment by 300s and delaying the end time of the arc segment by 60s.

[0130] For the ground station with only TT&C function or only data transmission function, if the two arc segments of the ground station are directed to the same star and in the same orbit, when the two arc segments are selected at the same time, the constraint of different orbits for the same star and the same type of task must not be satisfied.

[0131] For all arc segments of the same antenna, sorting the arc segments according to the start time of the arc segments can significantly speed up the speed of conflict pre-processing and reduce the average complexity of pre-processing.

[0132] Based on the above principles, the specific process of arc segment conflict pre-processing is as follows:

[0133] Input: Arc segment set A, where each arc segment a i represents a time window of a satellite visible to a ground station.

[0134] Step 1: Classify the input arc segment set A according to the ground station antenna to obtain the classified arc segment set AM. At this time, AM contains multiple sub-sets Each sub-set corresponds to an antenna d and contains all the arc segments of the satellite visible to the antenna.

[0135] Step 2: For each sub-set Sort the arc segments in the sub-set according to the start time of the arc segments. Each Internally, the order of the arc segments is arranged in ascending order of the start time.

[0136] Step 3: Traverse each sub-set For each sub-set Step 4 and Step 5 are executed.

[0137] Step 4: Traverse the current sub-set each arc segment a1. Step 5 is performed for each a1.

[0138] Step 5: Starting from the current arc segment a1, traverse each arc segment a2 in the sequence that follows a1 (note that the starting point of each traversal is a2 = a1.next, i.e. a1 is the current next arc segment in the sequence). For each a2, perform the following operations: Conflict check: check if there is a time conflict (e.g. time window overlap) between arc segment a1 and arc segment a2, if there is a conflict: add arc segment a1 to the "conflict arc segment list" of arc segment a2, and add arc segment a2 to the "conflict arc segment list" of arc segment a1. If there is no conflict: break the loop for a2 (i.e. end the traversal for a2, and enter the loop for the next a1). Because the sequence has been sorted by start time, if a1 and a2 have no conflict, then all the arc segments after a2 will also have no conflict with a1, so we can directly break out.

[0139] Step 6: Complete the traversal of all antenna subsets AM and all arc segments within each subset.

[0140] Output result: each arc segment a i contains a "conflict arc segment list" that stores other arc segments that have a time conflict with this arc segment.

[0141] The above process is performed for the i-th arc segment in the sequence, starting from the j-th = i+1 arc segment in the sequence, and checking if arc segment j conflicts with arc segment i, if there is a conflict, continue the traversal, otherwise stop.

[0142] Based on the arc conflict preprocessing result, the set of selectable time windows for each task is dynamically updated during the algorithm solving process, especially based on the time conflict information. This dynamic update can greatly reduce the solution space, thereby improving the optimization effect and efficiency of the algorithm. The multiple operators designed in the present application are all based on the constraint propagation principle, and when generating a neighborhood solution, a new arc segment will be selected for the task according to the dynamically obtained set of selectable arc segments.

[0143] (Three) Operator design

[0144] 1. Random exchange operator

[0145] The random exchange operator is an exchange type operator, mainly used to explore possible better solutions without reducing the solution quality.

[0146] Working mechanism:

[0147] ​Step 1: According to the pre-configured probability, select the completed task set or the uncompleted task set, and then randomly select a task from the task set. After a certain number of iterations, a completed task is selected.

[0148] Step 2: Randomly select an arc segment different from the current selection in the task's selectable arc segments.

[0149] Step 3: Switch the task's occupied arc segment to the newly selected arc segment.

[0150] 2. Forced insertion operator

[0151] The forced insertion operator is a jump-out operator that is specifically used to jump out of a local optimal solution, although it may produce a poor solution, but it provides the possibility for subsequent continuous optimization.

[0152] Working mechanism:

[0153] Step 1: Randomly select an uncompleted task.

[0154] Step 2: Randomly select one of the following methods to select an insertion arc segment.

[0155] Method 1: Minimum conflict degree priority. Traverse all selectable arc segments of the uncompleted task, then calculate the conflict degree of each arc segment after insertion with the current completed tasks. The conflict degree can be defined as: if the arc segment is inserted, it will cause the number of tasks in the completed tasks that conflict with the arc segment. Select the arc segment with the smallest conflict degree. If there are multiple arc segments with the smallest conflict degree, randomly select one.

[0156] Method 2: Random selection. Randomly select an arc segment from the selectable arc segments of the uncompleted task.

[0157] Step 3: Insert the selected uncompleted task into the completed tasks and occupy the selected arc segment. Traverse the completed tasks to check if there are tasks that conflict with the arc segment of the newly inserted task. If there is a conflict: remove the conflicting task from the completed task set. Add the removed task to the uncompleted task set and release the arc segment occupied by the removed task.

[0158] Step 4: Update the status of the inserted task to completed

[0159] 3. Recursive insertion operator

[0160] The recursive insertion operator is also a jump-out operator, but unlike the forced insertion operator, it has the ability to maintain solution quality while jumping out of a local optimal solution.

[0161] Working mechanism:

[0162] Step 1: Randomly select an unfinished task.

[0163] Step 2: Randomly select one of the following methods to select an insertion arc segment.

[0164] Method 1: Minimum conflict priority. Traverse all selectable arc segments of the unfinished task, then calculate the conflict degree of each arc segment after insertion with the current completed tasks. The conflict degree can be defined as: if the arc segment is inserted, it will cause the number of tasks in the completed tasks that conflict with the arc segment. Select the arc segment with the smallest conflict degree. If there are multiple arc segments with the smallest conflict degree, randomly select one.

[0165] Method 2: Random selection. Randomly select an arc segment from the selectable arc segments of the unfinished task.

[0166] Step 3: Insert the selected unfinished task into the completed tasks and occupy the selected arc segment. Traverse the completed tasks to check if there are tasks that conflict with the arc segment of the newly inserted task. If there is a conflict: use step 2 for tasks that conflict due to insertion until the conflict is resolved or the maximum recursion level is reached. Add the removed tasks to the unfinished task set and release the arc segment occupied by the removed task.

[0167] Step 4: Update the status of the inserted task to completed

[0168] 4. Delete repair operator

[0169] The delete repair operator is an optimization operator that explores the neighborhood to find higher quality solutions.

[0170] Working mechanism:

[0171] Step 1: Randomly select some completed tasks for deletion,

[0172] Step 2: Then randomly select some tasks from the unfinished tasks, select the available arc segment and then insert them into the completed task list.

[0173] Step 3: If this operation causes the solution to decrease in yield, abandon the operation; otherwise, accept the solution.

[0174] (Four) Operator adaptive mechanism

[0175] The operator adaptive selection mechanism dynamically adjusts the operators in the algorithm based on the performance of the algorithm, improves the efficiency of the algorithm, enhances the robustness, avoids falling into local optima, and helps the algorithm find better solutions. The operator adaptive selection mechanism of the present invention is described as follows:

[0176] Step 1 Algorithm initialization: Before the algorithm starts, initialization operations may be performed, which may include setting the initial solution, initializing operator weights, etc.

[0177] Step 2 Selection of operator: At the beginning of each iteration, the operator is selected using roulette based on the historical score performance of the operator.

[0178] Step 3 Generation of new solution: After generating a new solution using the operator, it is determined whether the solution can be accepted by the acceptance criterion.

[0179] Step 4 Update the score and weight of the operator according to the acceptance.

[0180] III. Multi-stage optimization strategy and algorithm framework

[0181] Based on the above large-scale TT&C data transmission task scheduling model and operator design, a high-performance multi-stage optimization strategy and improved overdue acceptance algorithm are provided. The specific steps of the multi-stage optimization strategy are as follows:

[0182] Step 1: Receive TT&C data transmission requirements, equipment capability data and satellite visibility forecast.

[0183] Step 2: Use the preprocessing strategy based on constraint propagation to preliminarily preprocess the data.

[0184] Step 3: TT&C data transmission task scheduling stage, which only schedules data transmission tasks. First, complete the initial solution construction of data transmission tasks based on schedulable values, and construct the initial schedulable scheme. Then, use the improved overdue acceptance algorithm and the operator based on constraint propagation to schedule all data transmission tasks. Finally, output the scheduling scheme for data transmission tasks.

[0185] Step 4: TT&C task scheduling stage, which only schedules TT&C tasks. First, complete the initial solution construction of TT&C tasks based on schedulable values, and construct the initial schedulable scheme. Then, use the improved overdue acceptance algorithm and the operator based on constraint propagation to schedule all TT&C tasks.

[0186] Step 5: Unified optimization stage, which schedules data transmission and TT&C tasks. Collect the scheduling schemes obtained in steps 3 and 4. Then, use the improved overdue acceptance algorithm and the operator based on constraint propagation to schedule all data transmission and TT&C tasks.

[0187] Step 6: Finally output the overall scheduling scheme.

[0188] The basic process of the improved overdue acceptance algorithm is consistent with the overdue acceptance algorithm, and the operator is replaced by the operator designed in this technology. Therefore, the algorithm process is not described again.

[0189] IV. Experimental results

[0190] Table 3 Statistics of each planning scene data

[0191]

[0192] In the experiments of the present application, the algorithm proposed in the present application is used to solve 10 different scenarios. The scenario information verified by the experiment is shown in Table 4, and the experimental results are shown in Table 4.

[0193] Table 4 Results of each planning scenario

[0194]

[0195] 1) The scene with less resource conflict performs well: In scenarios 1-5, the resource conflict is low. In these scenarios, the algorithm successfully achieves a task completion rate of 100%. This result shows that in the case of relatively abundant resources and less conflict between tasks, the algorithm proposed in the present application can effectively allocate resources to achieve a global optimal solution. This conclusion is consistent with the data analysis conclusion that in the condition of relatively sufficient resource allocation and less conflict, the algorithm can maximize the task completion rate and show excellent global optimization ability.

[0196] 2) Strong optimization ability in complex scenarios: In scenarios 6-10, as the number of tasks increases and the conflict between arc segments increases significantly, the difficulty of solving the algorithm also increases exponentially. However, even in these complex scenarios, the algorithm can still achieve an average task completion rate of 98.302%. This shows that the algorithm still has strong optimization ability in a high conflict environment and can find a near-optimal solution, showing the robustness and adaptability of the algorithm.

[0197] 3) Effectiveness of algorithm design: The multi-stage optimization algorithm based on constraint propagation designed in the present application successfully copes with scenarios of different task sizes and complexities through reasonable operator design and adaptive selection mechanism. The initial solution construction strategy based on schedulable value, the operator based on constraint propagation and the operator adaptive selection mechanism play a crucial role in each stage of the solution. The algorithm proposed in the present application can start from a high-quality initial solution and gradually optimize the solution quality by adaptively adjusting the operator usage strategy, effectively avoiding getting stuck in local optimum. The multi-stage optimization process enables the algorithm to flexibly configure operators and algorithm parameters according to different resource and demand characteristics, achieving a "suit one's measures to local conditions" optimization effect.

[0198] The above specific embodiments do not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A modeling and multi-stage scheduling method for large-scale TT&C TT&C task network resource scheduling problem, characterized in that, S1.Each ground station server acquires a satellite TT&C and TT&C task set R; wherein, each of the sets R represents a task, expressed as a five-tuple: ； wherein, is a task index value, denotes the satellite used for this task, denotes the duration of this task, is the set of all available arcs for this task, denotes the type of task; is the arc selection constraint for task , , is the minimum tracking elevation for task denotes that the maximum elevation of the arc assigned to this task must be greater than this value, is the arc selection constraint type for task , is the lower offset of the allowed range for task , is the most desired time / revolution number for task , is the upper offset of the allowed range for task ; is the set of available devices for task ; S2. The respective ground station server obtains a set of arcs A, the set of arcs being defined as: ; wherein represents an arc segment, which can be represented as a thirteen tuple: wherein the subscript represents the number of the arc segment and the have the same meaning as in is the total orbit number of the satellite orbit motion of the arc segment ; represents the transit orbit number; i.e. the orbit number visible in this territory; represents the total transit orbit number; represents the LEO / GEO attribute of the arc segment; represents the in-bound pitch angle; represents the time of the highest elevation angle; represents the pitch angle of the highest elevation angle; represents the out-bound time; represents the pitch angle of the out-bound time;​ S3. The respective ground station server acquires all ground station information, the ground station being defined as: ; is the first ground station; and is the second ground station. The is represented as a triple: ; wherein, denotes a set of disabled arc segments, denotes a function of the device; The device functions include: TT&C, TT&C, TT&C or TT&C, and TT&C and TT&C; TT&C or TT&C means that the device can execute one of TT&C tasks or TT&C tasks at the same time; TT&C and TT&C means that the device can execute one of TT&C tasks or TT&C tasks for different satellites at the same time, and can execute TT&C and TT&C tasks for the same satellite at the same time; S4.Each ground station server acquires a target satellite set S; The set S contains all the satellites participating in TT&C and data transmission tasks, and any target satellite of a task is contained in the set S. The set S contains all the satellites participating in TT&C and data transmission tasks, and any target satellite of a task is contained in the set S. The set S contains all the satellites participating in TT&C and data transmission tasks, and any target satellite The steps of the multi-stage scheduling method are: Step 1: receiving TT&C TT&C demand, device capability data and satellite visibility prediction; Step 2: using a preprocessing strategy based on constraint propagation to preliminarily preprocess the data; Step 3: TT&C task scheduling stage, which only schedules TT&C tasks;First, complete the initial solution construction of TT&C tasks based on schedulable values, and construct an initial schedulable scheme;Then, using an improved overdue acceptance algorithm and a constraint propagation-based operator, schedule all TT&C tasks;Finally, output the scheduling scheme for TT&C tasks; Step 4: TT&C task scheduling stage, which only schedules TT&C tasks;First, complete the initial solution construction of TT&C tasks based on schedulable values, and construct an initial schedulable scheme;Then, using an improved overdue acceptance algorithm and a constraint propagation-based operator, schedule all TT&C tasks, and finally output the scheduling scheme for TT&C tasks; Step 5: unified optimization stage, which schedules TT&C and TT&C tasks;Collect the scheduling schemes obtained in steps 3 and 4;Then, using an improved overdue acceptance algorithm and a constraint propagation-based operator, schedule all TT&C and TT&C tasks. 2.The modeling and multi-stage scheduling method for large-scale TT&C TT&C task network resource scheduling problem according to claim 1, characterized in that, In the decision-making relationship, each task set Tasks in ,have The task has several selectable arc segments, without considering device operating time conflict constraints. Can be found in the set of optional arc segments Execute on any arc segment in the array. 3.The modeling and multi-stage scheduling method for large-scale TT&C TT&C task network resource scheduling problem according to claim 2, characterized in that, In the decision relationship, the following formula is used for description: ； Variables The decisional relationship between tasks and arcs is described. 4.The modeling and multi-stage scheduling method for large-scale TT&C TT&C task network resource scheduling problem according to claim 3, characterized in that, The execution of each satellite task is unique, so the decision relationship between the task and the arc segment is: wherein, is the ith task, is the jth arc segment of task i, is the decision variable of task i.

5. The method of claim 4, wherein, The scheduling scheme of the satellite TT&C TT&C scheduling problem is represented as a decision vector X: 6.The modeling and multi-stage scheduling method for large-scale TT&C TT&C task network resource scheduling problem according to claim 5, characterized in that, Static constraints are established in the preprocessing process, Dynamic constraints are adjusted in the problem processing process. 7.The modeling and multi-stage scheduling method for large-scale TT&C TT&C task network resource scheduling problem according to claim 6, characterized in that, The static constraints include: arc segment preference constraints, arc segment availability constraints, and antenna function constraints. 8.The modeling and multi-stage scheduling method for large-scale TT&C TT&C task network resource scheduling problem according to claim 7, characterized in that, The dynamic constraints include: same-star same-type task different-orbit constraints, device working time non-conflict constraints, and task execution uniqueness constraints.

9. The method of claim 8, wherein, Setting each task profit of the data transmission task as 10 and each task profit of the measurement and control task as 1, the decision variable is used to represent the task The target profit function is set as: ; wherein ; The more tasks are allocated to the arc segment under the condition of meeting the constraints, the better.

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