Large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method

Abstract

The invention discloses a large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method, and aims at the problems of standardized mathematical model construction of large-scale measurement and control data transmission task station network resource scheduling and high-performance optimization strategy and algorithm design, an integer programming model integrating measurement, control and data transmission is established. Four neighborhood operators based on constraint propagation and oriented to large-scale measurement and control data transmission task scheduling are designed, a multi-stage optimization strategy and algorithm framework oriented to large-scale measurement and control data transmission resource scheduling are provided, and a reasonable and efficient scheme is provided to meet the requirement for large-scale measurement and control data transmission task station network resource scheduling.

Description

Technical Field

[0001] The invention belongs to the field of satellite control technology, and in particular relates to a large-scale measurement and control data transmission task station network resource scheduling problem modeling and a multi-stage scheduling method. Background Art

[0002] With the rapid development of aerospace technology, the number of satellites in orbit has increased exponentially, and the scale of missions has become increasingly large, which has put forward higher requirements for the measurement, control and data transmission capabilities of ground stations. However, as an important aerospace infrastructure, the number and capacity of ground station resources are far less than the growth rate of the number of satellites, resulting in increasingly tight ground station resources. Therefore, how to reasonably and efficiently allocate ground station resources, maximize the use efficiency of the ground station network, and meet the large-scale measurement, control and data transmission needs of users is a key issue that needs to be urgently solved in the current field of satellite ground station network resource scheduling. As one of the key technologies in the aerospace field, large-scale measurement, control and data transmission mission station network resource scheduling technology is of great significance for ensuring the long-term stable operation of satellites and improving the efficiency of satellite data transmission.

[0003] At present, in the actual control process, the scheduling of large-scale measurement, control and digital transmission task station network resources faces the following problems: 1) There is a lack of standardized and intuitive measurement, control and digital transmission integrated scheduling mathematical models, which presents constraints and benefits, and then guides the design of algorithms. In traditional methods, measurement, control and digital transmission are usually modeled separately, which makes it difficult to fully utilize the measurement station resources and leads to resource waste. At the same time, the existing scheduling model is difficult to apply to new satellites or ground stations that can perform measurement, control and digital transmission at the same time, and cannot give full play to the capabilities of the measurement station. 2) In the problem of large-scale measurement, control and digital transmission task station network resource scheduling, there are many resources and demands, the problem solution space is huge, and the efficiency of traditional algorithms needs to be further improved. Taking the 7-day scheduling demand as an example, considering the scheduling of measurement, control and digital transmission tasks at the same time, the algorithm needs to solve the scheduling problem of more than 20,000 tasks at the same time, and the scale of the problem solution space is huge. Therefore, traditional algorithms and optimization strategies are prone to fall into local optimality, 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 measurement, control and digital transmission task station network resource scheduling. Summary of the invention

[0004] The purpose of the present invention is to solve the above technical problems and propose a large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method, which performs the following steps:

[0005] S1. Each ground station server obtains the satellite measurement and control and data transmission task set R;

[0006] Where R = {r i |1≤i≤n r ,|R|=n r}; Each r in the set R iAll represent a task, which is represented by a five-tuple:

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

[0008] Among them, i is the subscript value of the TT&C task r i , s i represents the satellite used by this task, d i represents the duration of this task, A i is the set of all available arc segments of this TT&C task, type represents the type of the task. It 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, indicating that the maximum elevation angle of the arc segment assigned to this task must be greater than this value, CT i is the arc segment selection constraint type of task i, SD0 i is the lower offset of the allowable range of task i, SD1 i is the most desired moment / circle number of task i, SD2 i is the upper offset of the allowable range of task i; SDAi is the set of available devices for task i, SDA i = {SD i1 , SD i2 , …, SD ik};

[0009] S2. Each of the ground station servers obtains the short set A, and the arc segment set is defined as: A = {a i | 1 ≤ i ≤ n a , |A| = n a};

[0010] Among them, a i represents an arc segment, and the a i is represented as a thirteen-tuple:

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

[0012] Among them, RTN j is the total number of satellite orbit motion circles; RI j represents the transit circle number; RCN j i.e., which circle is visible within China this time; RF j represents the total number of transit circles; TI j represents the ascending / descending orbit attribute of this arc segment; is the inbound time; AI j represents the inbound 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 outbound time; AO j represents the pitch angle at the outbound time;

[0013] S3. Each of the ground station servers obtains all ground station information, and the ground stations are defined as: D = {d i | 1 ≤ i ≤ n d , |D| = n d}; d i is the i-th ground station;

[0014] The d i is represented as a triple: d i = {i, fb i , func i};

[0015] Among them, fb i represents the set of disabled arc segments, and func i represents the function of the i-th 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 either TT&C tasks or data transmission tasks at the same time. TT&C + data transmission means that the device can perform either TT&C tasks or data transmission tasks for different satellites at the same time, and can perform both TT&C and data transmission tasks for the same satellite at the same time.

[0016] S4. Each of the ground station servers obtains the target satellite set S;

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

[0018] Furthermore, in the decision relationship, for each task r in each task set Ri , there are |A i optional arc segments. Without considering the constraint of equipment working time conflict, task r i can be executed on any one of the arc segments in the set of optional arc segments A i .

[0019] Further, in the decision relationship, it is described by the following formula:

[0020]

[0021] The variable x i describes the decision relationship between tasks and arc segments.

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

[0023] The decision relationship between tasks and arc segments is:

[0024]

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

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

[0027]

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

[0029] and dynamic constraints are adjusted during the problem processing process.

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

[0031] Further, the dynamic constraints include: different circle constraints for the same type of tasks on the same satellite, no conflict constraint for equipment working time, and uniqueness constraint for task execution.

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

[0033] where,

[0034] Under the condition of meeting the constraints, the more tasks assigned to the arc segment, the better. Brief Description of the Drawings

[0035] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

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

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

[0038] Figure 3 It is a multi-stage optimization strategy flowchart of the present invention. Specific Embodiments

[0039] The following will describe the exemplary embodiments of the present application in conjunction with the drawings. Among them, various details of the embodiments of the present application are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described here without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted below.

[0040] The present invention aims at the problems of constructing a standardized mathematical model for large-scale TT&C and data transmission task station network resource scheduling, and designing high-performance optimization strategies and algorithms. 1) An integer programming model for integrated TT&C and data transmission is established. 2) Four neighborhood operators based on constraint propagation for large-scale TT&C and data transmission task scheduling are designed. 3) A multi-stage optimization strategy and algorithm framework for large-scale TT&C and data transmission resource scheduling are provided.

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

[0042] Propose the mathematical expressions of variables, constraint conditions, and objective functions in the scheduling problem, and establish an integer programming model in a standardized manner.

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

[0044] Table 1 Symbols and Definitions

[0045]

[0046]

[0047] (1) The scheduling resource description includes task description, arc segment description, ground station description, and satellite description, specifically:

[0048] 1. Task description

[0049] In the present invention, R represents the set of satellite TT&C and data transmission tasks, and it can be defined as:

[0050] R = {r i | 1 ≤ i ≤ n r , |R| = n r}

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

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

[0053] Among them, i is the subscript value of the TT&C task r i , s i represents the satellite used for this task, d i represents the duration of this task, A i is the set of all available arc segments for this TT&C task, type represents the type of the task. <RCO i = {MME i , CT i , SD0 i , SD1 i , SD2 i}, MME i is the minimum tracking elevation angle of task i, indicating that the maximum elevation angle of the arc segment assigned to this task must be greater than this value, CT i is the arc segment selection constraint type of task i, SD0 i is the lower offset of the allowable range of task i, SD1 i is the most desired time / circle number of task i, SD2 i is the upper offset of the allowable range of task i; <SDA i is the set of available devices for task i, <SDA i = {SD i1 , SD i2 , …, SD ik},

[0054] 2. Arc Segment Description

[0055] When dealing with the scheduling problem of large-scale satellite TT&C and data transmission tasks, an "arc segment" refers to the orbital part where a satellite can communicate with a ground station within a specific time window. Each arc segment involves the occupation of various resources such as time, space, and frequency. Due to the large number of satellites and limited ground station resources, arc segment conflicts may occur, that is, multiple tasks need to use the same resources in the same time period.

[0056] The set of arc segments in this invention is defined as:

[0057] A = {a i | 1 ≤ i ≤ n a , |A| = n a}

[0058] where a i represents an arc segment, 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 number of orbits of the satellite's orbital motion; RI j represents the transit orbit number; RCN j is the number of the visible circle within China this time; RF j represents the total number of transit orbits; TI j represents the ascending / descending orbit attribute of this arc segment; is the inbound time; AI j represents the inbound 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 outbound time; AO j represents the pitch angle at the outbound time.

[0061] 3. Ground Station Description

[0062] As the core infrastructure for satellite TT&C (Telemetry, Tracking, and Command) and data transmission tasks, the ground station is responsible for communicating with orbiting satellites, including key operations such as data reception, command sending, and orbit measurement. The ground station is equipped with highly sensitive antennas, receivers, and data processing equipment, capable of tracking the satellite orbit and conducting data exchange within a predetermined time window. Each ground station can only communicate when the satellite flies over it, and the resources are limited during a specific period.

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

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

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

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

[0067] 4. Satellite Description

[0068] The satellite resource description involves the set of target satellites for TT&C and data transmission tasks, denoted as set S, which contains all satellites participating in TT&C and data transmission tasks. For any task r i , its task target satellites are all included in set S.

[0069] (2) The decision variables include decision relationships, integer decision variables, and decision vectors

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

[0071] 1. Decision Relationship

[0072] For each task r in the task set R i , there are |A i | optional arc segments. Without considering the constraints of device working time conflicts, task r i can be executed on any one of the arc segments in the optional arc segment set A i . For example, Figure 1 represents the decision relationship existing between the TT&C task and...

[0073] 2. Integer Decision Variables

[0074] For the above decision relationship, the present invention uses integer variables to describe this relationship, as shown in the formula:

[0075]

[0076] Wherein, R — measurement and control task set;

[0077] r i — the i-th measurement and control task in the measurement and control task set;

[0078] The variable x in the above formula i describes the decision relationship between tasks and arcs. At the same time, the execution of each satellite task is unique, so the decision variables should also meet the following conditions:

[0079]

[0080] So far, the decision relationship between tasks and arcs can be intuitively expressed as:

[0081]

[0082] Table 2 Meanings of decision variables

[0083]

[0084] 3. Decision vector

[0085] In the above decision variable vector, the scheduling scheme of the satellite measurement and control data transmission scheduling problem can be expressed as a decision vector X as follows:

[0086]

[0087] To intuitively present the decision relationship between tasks and communication opportunities and further illustrate the integer decision model constructed above, the present invention gives a specific example of the integer decision model, as Figure 2 shown.

[0088] (3) Constraint description includes static constraints and dynamic constraints. Static constraints are established during the preprocessing process, and dynamic constraints are adjusted during the problem processing process. The static constraints include: arc preference constraints, arc availability constraints, and antenna function constraints. The dynamic constraints include: different circle constraints for the same type of tasks on the same satellite, non-conflict constraints for equipment working hours, and uniqueness constraints for task execution.

[0089] Static constraints refer to the constraint conditions that can be satisfied through preprocessing means such as data screening, format conversion, and design of data structures. After the preprocessing is completed, such constraints usually do not need to be considered again during the solution process. Static constraints include:

[0090] 1. Arc preference constraints

[0091] The arc segment preference constraint describes the preferences and restrictions of the submitter of each task for the TT&C or data transmission arc segments. The task can only be executed on the arc segments that meet the user's preference requirements. This constraint affects the list of optional arc segments for the task and is a typical static constraint.

[0092] 2. Arc segment availability constraint

[0093] The arc segment availability constraint prohibits the task from selecting the arc segments that work during the device disable time, that is, the device can only operate during the non-disable period.

[0094]

[0095] 3. Antenna function constraint

[0096] There are four types of antennas with TT&C, data transmission, TT&C or data transmission, and TT&C and data transmission functions in the antenna device. In the data transmission task, it can only be executed on the antenna device that supports the data transmission function; while the TT&C task can only be carried out on the TT&C antenna device.

[0097]

[0098] The dynamic constraint is a constraint condition that can only be judged whether it is satisfied after the arc segment is assigned to the task. Since the matching relationship between the task and the arc segment cannot be determined before the problem is solved, the satisfaction of this type of constraint can only be dynamically judged during the solution process by designing a reasonable strategy according to the change of the solution. The dynamic constraints include:

[0099] 1. Different circle constraint for the same type of tasks on the same satellite

[0100] The TT&C tasks and data transmission tasks of the same satellite cannot be repeated in the same circle:

[0101]

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

[0103] where

[0104] 2. No conflict constraint on device working time

[0105]

[0106] Indicator m (x i ) is an indicator function, indicating that task i is in the time window x iWhether the time period conflicts with the time periods of other tasks executed by device m. If there is a conflict, it is 1; if there is no conflict, it is 0.

[0107] 3. Uniqueness constraint for task execution

[0108] Due to the setting of the decision variables in the present invention, the value of x i can only be one kind, so this constraint is naturally satisfied.

[0109] Set the objective function:

[0110] Since in the management and control, the higher the task completion is, the better, and the completion difficulty of the data transmission task is greater than that of the measurement and control task. Therefore, the present invention coordinates the benefits of the data transmission and measurement and control tasks, sets the benefit of each data transmission task to 10, and the benefit of each measurement and control task to 1. The present invention introduces the decision variable x i to represent the arc segment selected and used for task r i . In view of the optimization objective of the present invention being to achieve the maximum task completion rate, the objective benefit function can be set as:

[0111]

[0112] where

[0113] This optimization objective means that under the condition of satisfying the constraints, the more tasks assigned to the arc segment, the better.

[0114] Above, an integer programming model for large-scale measurement and control data transmission tasks is established, and the mathematical expressions of the decision variables, constraint conditions, and objective function in the model are given in a standardized manner, which helps the management and control department understand the combinatorial optimization essence of inter-satellite link scheduling, and further better guides the design of algorithms and operators.

[0115] II. Design of neighborhood operators based on constraint propagation

[0116] To enhance the practicability of the model and further optimize the solution efficiency, after in-depth analysis of the characteristics of the problem, the present invention proposes two effective data preprocessing strategies and 4 neighborhood operator designs. These strategies aim to accurately screen and transform 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 applications and optimization work.

[0117] Constraint propagation is an efficient algorithm technology in solving constraint satisfaction problems. Its basic principle is to continuously update and transmit the value range of variables during the problem-solving process through known constraint conditions, thereby reducing the search space and improving the efficiency of problem-solving.

[0118] (1) Task Optional Time Window Preprocessing for Constraint Propagation

[0119] To reduce the size of the solution space, this strategy filters the available arcs for each task and adds them to the task's list of available arcs. During the subsequent solution process, the arcs available for each task are limited to the task's list of available arcs. This strategy effectively reduces the decision space for task arc selection from the entire arc list to the list of available arcs, significantly reducing the arc 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 arc set A, where each arc a i represents a time window during which a ground station can see the satellite. Create an empty "list of optional arcs" for each task r i to store the arcs that meet the task's constraint conditions.

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

[0122] Step 3: For each arc a in the arc set A i , perform Step 4.

[0123] Step 4: Check whether the arc a i meets all the constraint conditions of the task r i . If the arc a i meets all the constraint conditions of the task r i , add the arc a i to the "list of optional arcs" of the task r i .

[0124] Step 5: Complete the traversal of all tasks and all arcs. After the loop ends, output the result: Each task r i corresponds to a "list of optional arcs" that contains all the optional arcs that meet its constraint conditions.

[0125] It should be noted that this preprocessing strategy only modifies the list of optional arcs for each task without changing the order of the arc list and the task list, so it can ensure the correctness and consistency of the decision matrix.

[0126] (2) Arc Conflict Preprocessing

[0127] In the scheduling problem of the present invention, the dynamic constraints mainly include the constraint that there is no conflict in the working time of devices and the constraint that tasks of the same type on the same satellite are not in the same circle. Under these constraints, the conflicts between arcs have a certain degree of predictability. Therefore, the conflicts between arcs can be pre-computed to provide prior knowledge for the solution process, guiding the algorithm to select arcs for tasks according to the conflict degree of arcs.

[0128] After deeply analyzing the problem characteristics, constraint mechanism and the functional characteristics of ground stations, the present invention believes that the following points should be considered in the preprocessing of arc conflicts:

[0129] Since the link establishment preparation time and link disconnection release time for each task are 300s and 60s respectively, for the arcs on the same ground station, the working occupation time of the ground station when the arc is selected by the task can be obtained by advancing the start time and delaying the end time of the arc by 300s and 60s respectively.

[0130] For a ground station that only has TT&C function or only has data transmission function, if two arcs of this ground station are for the same satellite and in the same circle, then when these two arcs are selected simultaneously, the constraint that tasks of the same type on the same satellite are not in the same circle must not be satisfied.

[0131] For all arcs of the same antenna, sorting them according to the start time of the arcs can significantly accelerate the speed of conflict preprocessing and reduce the average complexity of preprocessing.

[0132] Based on the above principles, the specific process of arc conflict preprocessing is as follows:

[0133] Input: arc set A, where each arc a i represents the time window during which a ground station can see a satellite.

[0134] Step 1: Classify the input arc set A according to the ground station antenna to obtain the classified arc set AM. At this time, AM contains multiple subsets Each subset corresponds to an antenna d and contains all the arcs of the satellites visible to this antenna.

[0135] Step 2: For each subset in AM Sort the arcs in this subset according to the start time of the arcs. After sorting, within each the order of the arcs is arranged in ascending order according to the start time.

[0136] Step 3: Traverse each subset in AM For each subset Execute Step 4 and Step 5.

[0137] Step 4: Traverse the current subset Each arc segment a in 1 . For each a 1 Execute Step 5.

[0138] Step 5: Starting from the current arc segment a 1 begin, traverse each arc segment a in 1 that comes after a 2 (Note that the starting point of each traversal is a 2 = a 1 .next, that is, the next arc segment of a 1 in the current ). For each a 2 , perform the following operations: Conflict check: Check whether there is a time conflict (e.g., time window overlap) between arc segment a 1 and arc segment a 2 . If there is a conflict: Add arc segment a 1 to the "conflicting arc segment list" of arc segment a 2 , and add arc segment a 2 to the "conflicting arc segment list" of arc segment a 1 . If there is no conflict: Break out of the current loop for a 2 (i.e., end the traversal for a 2 and enter the loop for the next a 1 ). Because has been sorted by start time, if a 1 and a 2 do not conflict, then all subsequent arc segments will not conflict with a 1 either, so it can be directly broken out of.

[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 "conflicting arc segment list" that stores other arc segments that have a time conflict with this arc segment.

[0141] The above process is for the i-th arc segment in the sequence. Starting from the (i + 1)-th arc segment in the sequence, traverse the sequence to check whether arc segment j in the sequence conflicts with arc segment i. If there is a conflict, continue traversing; otherwise, stop.

[0142] Based on the preprocessing results of arc segment conflicts, during the algorithm solving process, the set of optional time windows for each task is dynamically updated, especially based on the information of time conflicts. This dynamic update can significantly narrow the solution space, thereby improving the optimization effect and efficiency of the algorithm. Multiple operators designed in the present invention are all based on the principle of constraint propagation. When generating neighborhood solutions, new arc segments will be selected for tasks according to the dynamically obtained set of optional arc segments.

[0143] (III) 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 set of completed tasks or the set of uncompleted tasks, and then randomly select a task from the task set. Every certain number of iterations, a non-completed task is forced to be selected.

[0148] Step 2: Randomly select an arc segment different from the current selection from the optional arc segments of this task.

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

[0150] 2. Forced Insertion Operator

[0151] The forced insertion operator is a jump-out type operator, specifically used to jump out of the local optimal solution. Although it may generate inferior solutions, it provides the possibility for subsequent continuous optimization.

[0152] Working mechanism:

[0153] Step 1: Randomly select a non-completed task.

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

[0155] Method 1: Minimum conflict degree first. Traverse all the optional arc segments of this non-completed task, and then calculate the conflict degree of each arc segment after insertion with the currently completed tasks. The conflict degree can be defined as: if this arc segment is inserted, it will cause the number of tasks in the completed tasks that conflict with this arc segment. Select the arc segment with the minimum conflict degree. If there are multiple arc segments with the minimum conflict degree, randomly select one.

[0156] Method 2: Random selection. Randomly select an arc segment from the optional arc segments of this non-completed task.

[0157] 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 is any task conflicting with the arc segment of the newly inserted task. If there is a conflict: Remove the conflicting task from the set of completed tasks. Add the removed task to the set of unfinished tasks 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 type operator. However, different from the forced insertion operator, while jumping out of the local optimum, it has a certain ability to maintain the solution quality.

[0161] Working mechanism:

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

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

[0164] Method 1: Priority of minimum conflict degree. Traverse all the optional arc segments of the unfinished task, and then calculate the conflict degree of each arc segment after insertion with the current completed tasks. The conflict degree can be defined as: If inserting this arc segment will cause the number of tasks conflicting with this arc segment in the completed tasks. Select the arc segment with the minimum conflict degree. If there are multiple arc segments with the minimum conflict degree, randomly select one.

[0165] Method 2: Random selection. Randomly select an arc segment from the optional 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 is any task conflicting with the arc segment of the newly inserted task. If there is a conflict: For the task that causes conflict due to insertion, use Step 2 until the conflict is resolved or the maximum recursion level is reached. Add the removed task to the set of unfinished tasks and release the arc segment occupied by the removed task.

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

[0168] 4. Deletion and Repair Operator

[0169] The deletion and repair operator is an optimization type operator, which explores near the neighborhood to find a solution with higher quality.

[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, and insert them into the completed task list after selecting the available arcs.

[0173] Step 3: If this operation results in a decrease in the benefit of the solution, then abandon this operation; otherwise, accept this solution.

[0174] (4) Operator adaptation mechanism

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

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

[0177] Step 2 Select an operator: At the beginning of each iteration, select an operator using the roulette wheel method based on the historical score performance of the operator.

[0178] Step 3 Generate a new solution: After generating a new solution using the operator, determine whether this solution can be accepted by the acceptance criterion.

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

[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 an improved late acceptance algorithm are provided below. 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 forecasts.

[0183] Step 2: Use a preprocessing strategy oriented to constraint propagation to perform preliminary preprocessing on the data.

[0184] Step 3: Data transmission task scheduling stage, in which only data transmission tasks are scheduled. First, construct an initial solution for the data transmission tasks based on the schedulable value to build an initial schedulable scheme. Then use the improved late acceptance algorithm and the operator based on constraint propagation to schedule all data transmission tasks. Finally, output the scheduling scheme for the data transmission tasks.

[0185] Step 4: Measurement and control task scheduling phase. In this phase, only measurement and control tasks are scheduled. First, based on the schedulable value, the initial solution of the measurement and control tasks is constructed to build an initial schedulable scheme. Then, using the improved late acceptance algorithm and the operator based on constraint propagation, all measurement and control tasks are scheduled.

[0186] Step 5: Unified optimization phase. In this phase, data transmission and measurement and control tasks are scheduled uniformly. The scheduling schemes obtained in Step 3 and Step 4 are collected. Then, using the improved late acceptance algorithm and the operator based on constraint propagation, all data transmission and measurement and control tasks are scheduled.

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

[0188] Among them, the basic process of the improved late acceptance algorithm is the same as that of the late acceptance algorithm, and the operator is replaced by the operator designed in this technology, so the algorithm process will not be elaborated.

[0189] IV. Experimental Results

[0190] Table 3 Data statistics of each planning scenario

[0191]

[0192]

[0193] In the experiment of the present invention, the algorithm proposed in the present invention solved 10 different scenarios, and the scenario information verified by the experiment is shown in Table 4.

[0194] Table 4 Results of each planning scenario

[0195]

[0196] 1) Excellent performance in scenarios with less resource conflict: In Scenarios 1-5, the resource conflict is low. In these scenarios, the algorithm successfully achieved a 100% task completion rate. This result indicates that in the case of relatively abundant resources and fewer conflicts between tasks, the algorithm proposed in the present invention can effectively allocate resources to achieve the global optimal solution. This conclusion is consistent with the data analysis conclusion, that is, under the condition of relatively sufficient resource allocation and fewer conflicts, the algorithm can maximize the task completion rate and show excellent global optimization ability.

[0197] 2) Powerful optimization ability in complex scenarios: In Scenarios 6-10, with the increase in the number of tasks and the significant increase in the conflict degree between arcs, the solving difficulty of 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 powerful optimization ability in a high-conflict environment, can find a solution close to the optimal solution, and demonstrates the robustness and adaptability of the algorithm.

[0198] 3) Effectiveness of algorithm design: The multi-stage optimization algorithm based on constraint propagation designed in the present invention successfully addresses scenarios with different task scales and complexities through reasonable operator design and adaptive selection mechanisms. The initial solution construction strategy based on schedulable values, the operator based on constraint propagation, and the operator adaptive selection mechanism in the algorithm play crucial roles in each stage of the solution. The algorithm proposed in the present invention can start from a high-quality initial solution and gradually optimize the solution quality by adaptively dynamically adjusting the operator usage strategy, effectively avoiding being trapped in local optima. The multi-stage optimization process enables the algorithm to flexibly configure operators and algorithm parameters according to different resource and demand characteristics, achieving an "optimization effect tailored to local conditions".

[0199] The above specific embodiments do not constitute a limitation to the protection scope of this 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 this application shall be included within the protection scope of this application.

Claims

1. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method, characterized in that: S1. Each ground station server obtains the satellite measurement and control and data transmission task set R; Where R = {r i |1≤i≤n r ,|R|=n r }; Each r in the set R i Each represents a task, expressed as a five-tuple: r i ={i,s i ,du i ,A i ,type,<RCO i >,<ADA i >}; Among them, i is the measurement and control task r i The subscript value, s i represents the satellite used by the mission, d i Indicates the duration of the task, A i is the set of all available arcs for the measurement and control task, type indicates the type of task. is the arc selection constraint for 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, indicating that the maximum elevation angle of the arc segment assigned to this task must be greater than this value. i is the arc selection constraint type of task i, SD0 i is the lower deviation of the allowable range of task i, SD1 i The most desired time / circle number for task i, SD2 i is the upper deviation of the allowed range of task i; SDA i is the set of available devices for task i, SDA i ={SD i1 ,SD i2 ,…,SD ik }; S2. Each ground station server obtains a short protection set A, and the arc segment set is defined as: A = {a i |1≤i≤n a ,|A|=n a }; where a i represents an arc segment, the a i is represented as a 13-tuple: 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 }; Among them, RTN j is the total circle number of the satellite's orbit; RI j Represents the transit circle number; RCN j That is, the number of circles visible in China this time; RF j Indicates the total number of transit laps; TI j Indicates the ascending and descending track attributes of the arc segment; is the time of entering the station; AI j Indicates the approach pitch angle; TM j Indicates the moment of highest elevation; AM j Indicates the highest elevation angle pitch angle; TO j Indicates the departure time; AO j Indicates the pitch angle at the departure time; 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 i-th ground station The d i is represented as a triple: d i ={i,fb i ,func i }; Among them, fb i Indicates a set of disabled arcs, func i Indicates the function of the i-th device; Equipment functions include: measurement and control, data transmission, measurement and control or data transmission, and measurement and control and data transmission; Measurement and control or data transmission means that the device can perform one of the measurement and control tasks or data transmission tasks at the same time; Measurement, control and data transmission means that the equipment can perform measurement, control or data transmission tasks for different satellites at the same time, and can perform measurement, control and data transmission tasks for the same satellite at the same time; S4. Each ground station server obtains a target satellite set S; The set S contains all satellites participating in the telemetry mission. i The mission target satellites are all included in the set S.

2. According to claim 1, a large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method is characterized in that: In the decision relationship, each task r in the task set R i , there is |A i | optional arcs, without considering the equipment working time conflict constraints, task r i In the optional arc set A i Execute on any arc segment in .

3. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method according to claim 2, characterized in that: In the decision relationship, the following formula is used: variable x i Describes the decision relationship between tasks and arcs.

4. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method according to claim 3, characterized in that: The execution of each satellite mission is unique, and the setting of decision variables makes this constraint naturally satisfied. Therefore, the decision relationship between tasks and arcs is: Among them, r i is the i-th task, a ij is the jth arc of task i, x i is the decision variable for task i.

5. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method according to claim 4, characterized in that: The scheduling solution of the decision variable vector satellite measurement and control data transmission scheduling problem is expressed as a decision vector X:

6. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method according to claim 5, characterized in that: Static constraints are established during preprocessing. Adjust dynamic constraints during problem solving.

7. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method according to claim 6, characterized in that: The static constraints include: arc segment preference constraints, arc segment availability constraints, and antenna function constraints.

8. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method according to claim 7, characterized in that: The dynamic constraints include: different circle constraints for the same type of tasks on the same satellite, non-conflict constraints for equipment working hours, and unique constraints for task execution.

9. A large-scale measurement and control data transmission task station network resource scheduling problem modeling and multi-stage scheduling method according to claim 8, characterized in that: The benefit of each task of the data transmission task is set to 10, and the benefit of each task of the measurement and control task is set to 1. i To represent the task r i For the selected arc segment, set the standard gain function as: in, Under the condition of satisfying the constraints, the more tasks are assigned to the arc segment, the better.

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