Multi-satellite mission intelligent scheduling and conflict resolution method

Abstract

The application discloses a multi-satellite task intelligent scheduling and conflict resolution method, comprising the following steps: S1, constructing a double-layer cooperative task scheduling framework on the satellite and the ground, completing hierarchical function division and double-layer data linkage mechanism building, the double-layer cooperative task scheduling framework comprises a ground global pre-scheduling layer and a satellite local real-time scheduling layer, the multi-satellite task intelligent scheduling and conflict resolution method realizes deep cooperation of the satellite-ground double-layer scheduling, solves the industry pain points of satellite-ground scheduling fragmentation, non-uniform resource modeling, conflict processing lag and scheduling without closed loop. Through unified modeling of heterogeneous resources, the standardization of satellite-ground resource representation is realized, and the resource matching accuracy is improved; through the hierarchical conflict resolution mechanism, the conflict processing efficiency is greatly improved, and the global conflict is optimized and the local conflict is autonomously resolved in seconds.

Description

Technical Field

[0001] This invention relates to the field of satellite mission scheduling related products, specifically a method for intelligent scheduling and conflict resolution of multiple satellite missions. Background Technology

[0002] With the rapid development of satellite networking technology, multi-satellite cluster collaborative observation has become the core method for acquiring space information. The massive, diverse, and dynamically emerging multi-source observation tasks place extremely high demands on the real-time performance, accuracy, and coordination of satellite mission scheduling. Currently, most mainstream multi-satellite mission scheduling methods adopt a ground-based single-layer centralized scheduling mode, where the ground control center uniformly completes task allocation, resource matching, and timing arrangement. The satellites only execute fixed scheduling instructions, which has many technical shortcomings.

[0003] First, the scheduling of satellite and ground resources is fragmented, resulting in low resource utilization. The existing scheduling system manages onboard payload resources and real-time computing power resources separately from ground-based telemetry, tracking, and command resources and data transmission resources. It relies solely on historical static resource data from the ground for task matching, failing to perceive dynamic information such as the real-time status of onboard resources, payload workload, and satellite attitude constraints. This leads to mismatches between multi-source observation tasks and heterogeneous resources, resulting in a large amount of idle onboard resources not being fully utilized, and an overall resource utilization rate of less than 60%.

[0004] Secondly, it cannot achieve cross-level dynamic pre-scheduling, and its dynamic task adaptability is poor. Most existing scheduling methods are static and fixed scheduling methods, which can only complete the timing planning for fixed tasks that have been reported in advance. They cannot respond quickly to dynamic scenarios such as sudden observation tasks, satellite attitude deviations, and temporary resource occupation. They lack a pre-scheduling mechanism for space-ground linkage, and the task adjustment is seriously delayed.

[0005] Finally, the conflict resolution mechanism is simplistic and lacks scheduling stability. Existing conflict resolution methods are mostly reactive and reactive on the ground, which can only make simple adjustments to the overlapping of mission times and resource contention conflicts that have already occurred. They cannot predict potential conflicts in advance, and they do not distinguish between local conflicts on the satellite and global conflicts on the ground. They lack the ability to resolve conflicts in a tiered manner, which can easily lead to problems such as large-scale mission scheduling paralysis and observation mission failure. Summary of the Invention

[0006] The purpose of this invention is to provide a method for intelligent scheduling and conflict resolution of multi-satellite missions, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent scheduling and conflict resolution of multi-satellite missions, comprising the following steps:

[0008] S1. Construct a two-layer collaborative mission scheduling framework between the satellite and the ground, complete the hierarchical functional division and the construction of a two-layer data linkage mechanism. The two-layer collaborative mission scheduling framework includes a ground global pre-scheduling layer and a satellite local real-time scheduling layer.

[0009] S2. Establish a unified modeling system for heterogeneous resources across space and ground levels, standardize and quantify the static resources on the ground and the dynamic real-time resources on the satellite, and construct a cross-level resource status database;

[0010] S3. Based on the dual-layer collaborative task scheduling framework and the cross-level heterogeneous resource unified model, realize cross-level dynamic resource intelligent matching of multi-source observation tasks, complete global pre-scheduling planning, and generate an initial dual-layer scheduling scheme.

[0011] S4. Establish a two-layer linkage conflict pre-detection and hierarchical resolution mechanism to make a global prediction of potential conflicts in the initial two-layer scheduling scheme, distinguish between global conflicts and local conflicts, and execute differentiated resolution strategies.

[0012] S5 outputs the optimized final scheduling command. The ground global pre-scheduling layer issues the global scheduling baseline, and the on-board local real-time scheduling layer fine-tunes the local scheduling command in real time to complete the collaborative execution of multiple satellite missions and transmit status data back in real time, thereby realizing closed-loop dynamic scheduling.

[0013] As a preferred embodiment of the present invention, the functions of the ground global pre-scheduling layer include: integrating ground telemetry and control resources, data transmission bandwidth, and satellite orbit prediction static resources; receiving the requirements of multi-source observation tasks across the entire domain; completing task priority sorting, preliminary allocation of global resources, and cross-satellite task timing pre-arrangement; generating a global pre-scheduling baseline scheme; and coordinating global conflict handling and hierarchical parameter distribution.

[0014] The functions of the on-board local real-time scheduling layer include: real-time acquisition of on-board payload working status, on-board computing power margin, satellite attitude, and dynamic energy storage resource data; receiving the ground global pre-scheduling baseline; completing local task fine-tuning and real-time resource precise matching; responding to sudden on-board tasks; and transmitting back on-board status data and local conflict information.

[0015] As a preferred embodiment of the present invention, in step S1, the dual-layer data linkage mechanism adopts a millisecond-level bidirectional data interaction protocol, in which the satellite layer uploads resource status and task execution feedback in real time, and the ground layer updates the global scheduling baseline in real time, thereby realizing dynamic synchronization of scheduling parameters between the two layers.

[0016] As a preferred embodiment of the present invention, in step S2, the cross-level heterogeneous resource unified modeling system includes ground static resource modeling and on-board dynamic resource modeling.

[0017] The ground static resource modeling involves: quantitatively modeling the coverage period of ground telemetry and control stations, data transmission bandwidth capacity, ground data processing computing power, and fixed parameters of satellite orbits to generate a static resource constraint matrix;

[0018] The on-board dynamic resource modeling is as follows: dynamically quantify the satellite payload working time margin, attitude adjustment capability, on-board storage margin, real-time computing power load, and battery energy storage status, and generate a dynamic resource state vector after normalization.

[0019] By integrating the static resource constraint matrix and the dynamic resource state vector, a unified resource model spanning satellite and ground levels is constructed.

[0020] As a preferred embodiment of the present invention, the multi-source observation task cross-level dynamic resource matching and pre-scheduling in step S3 specifically includes:

[0021] S31. Perform attribute analysis on multi-source observation tasks, extract observation type, observation period, accuracy requirements, payload requirements, priority weight parameters, and complete standardized task modeling;

[0022] S32. The ground global pre-scheduling layer, based on a cross-level unified resource model and combined with task priorities, completes the preliminary matching and temporal arrangement of multi-source tasks and global static resources, and generates a preliminary pre-scheduling plan.

[0023] S33. The on-board local real-time scheduling layer combines the on-board real-time dynamic resource status to make precise fine-tuning of the task matching relationship and execution time of the preliminary pre-scheduling plan;

[0024] S34. With the goal of maximizing resource utilization and task scheduling success rate, a two-layer iterative optimization algorithm is used to achieve precise matching of dynamic resources across levels and generate a two-layer pre-scheduling scheme.

[0025] As a preferred embodiment of the present invention, the dual-layer linkage conflict pre-detection and hierarchical resolution mechanism described in step S4 specifically includes:

[0026] S41. Conflict Pre-detection: From four dimensions, namely resource preemption, time overlap, spatial conflict, and load constraints, the potential conflict prediction of the two-layer pre-scheduling scheme is carried out in the whole domain.

[0027] S42. Conflict Classification: Conflicts are divided into Level 1 global conflicts and Level 2 local conflicts. The Level 1 global conflicts are global conflicts involving resource contention and cross-satellite timing overlap among multiple satellites. The Level 2 local conflicts are local conflicts involving task timing and payload resource occupation within a single satellite.

[0028] S43. Hierarchical resolution: The ground-based global pre-scheduling layer is responsible for resolving first-level global conflicts by optimizing the global task arrangement and reallocating cross-satellite resources to achieve global conflict optimization; the on-board local real-time scheduling layer autonomously resolves second-level local conflicts by fine-tuning the time periods of individual satellite tasks and switching idle payload resources to achieve rapid resolution of local conflicts.

[0029] As a preferred embodiment of the present invention, the closed-loop dynamic scheduling in step S5 specifically involves the following: during the mission execution, the on-board local real-time scheduling layer transmits data on payload working status, mission completion progress, and remaining resources at a frequency of seconds. The ground-based global pre-scheduling layer monitors the global scheduling status in real time. When large-scale resource changes or new emergency missions occur, the two-layer pre-scheduling scheme is iteratively optimized to achieve dynamic closed-loop scheduling.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] This intelligent scheduling and conflict resolution method for multi-satellite missions achieves deep collaboration between satellite and ground-based scheduling, addressing industry pain points such as fragmented satellite-ground scheduling, inconsistent resource modeling, delayed conflict handling, and lack of closed-loop scheduling. It achieves standardized representation of satellite and ground resources through unified modeling of heterogeneous resources, improving resource matching accuracy; significantly enhances conflict handling efficiency through a hierarchical conflict resolution mechanism, with global conflict optimization and second-level autonomous resolution of local conflicts; and adapts to complex and dynamic multi-satellite networking mission scenarios through closed-loop dynamic scheduling, significantly improving the success rate of multi-satellite mission scheduling and the overall resource utilization rate. It can be widely applied to various multi-satellite collaborative observation, telemetry, tracking, and data transmission mission scenarios. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating the intelligent scheduling and conflict resolution method for multi-satellite missions of the present invention.

[0033] Figure 2 This is a framework diagram of the multi-source observation task cross-level dynamic resource matching and pre-scheduling for the multi-satellite task intelligent scheduling and conflict resolution method of the present invention;

[0034] Figure 3 This is a framework diagram of the two-layer linkage conflict pre-detection and hierarchical resolution mechanism of the multi-satellite mission intelligent scheduling and conflict resolution method of the present invention. Detailed Implementation

[0035] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0036] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," "front end," "rear end," "both ends," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0037] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0038] Please see Figure 1-3 The present invention provides an embodiment of a multi-satellite mission intelligent scheduling and conflict resolution method, which includes the following detailed implementation steps:

[0039] S1. Construct a two-tier collaborative mission scheduling framework for both onboard and ground-based systems, and complete the hierarchical functional division and the establishment of a two-tier data linkage mechanism.

[0040] This step establishes a layered, collaborative, and clearly defined space-ground dual-layer scheduling architecture. It addresses the issues of disconnect between ground and satellite scheduling in traditional scheduling models, as well as the inability to balance global coordination with local adaptation. The framework is divided into a ground-based global pre-scheduling layer and a satellite-based local real-time scheduling layer. The two layers operate independently yet are deeply interconnected, with precise hierarchical functions.

[0041] The ground-based global pre-scheduling layer, serving as the core of the global scheduling, is deployed at the ground-based telemetry, tracking, and command (TT&C) center. It integrates static fixed resources across the entire domain, specifically encompassing the coverage periods of each ground-based TT&C station, data transmission bandwidth, ground data processing computing power, and long-term orbit forecast parameters for multiple satellites. Simultaneously, it uniformly receives multi-source observation mission requirements from across the domain, including land surveying, environmental monitoring, and disaster early warning, centrally aggregating and processing massive amounts of data. In terms of functional implementation, it first prioritizes tasks based on their urgency, observation value, and command priority. Then, combined with global static resource reserves, it performs preliminary allocation of global resources, pre-arranging the overall timing of cross-satellite observation, transmission, and TT&C tasks for multiple satellites. This avoids large-scale, cross-satellite fundamental scheduling conflicts and generates a standardized global pre-scheduling baseline scheme. Furthermore, it is fully responsible for overall conflict coordination, hierarchical scheduling parameter distribution, and global scheduling status management, providing fundamental constraints and scheduling basis for on-board local scheduling.

[0042] The onboard local real-time scheduling layer is deployed in the onboard scheduling processor of each satellite, focusing on scheduling adaptation for dynamic real-time scenarios. Through onboard sensors, payload monitoring modules, and computing power monitoring modules, it collects various dynamic resource data on the satellite in milliseconds, including the operating time and remaining operational time of onboard optical and radar payloads, attitude adjustment capability parameters such as satellite attitude adjustment angular velocity and attitude maneuver margin, used capacity and remaining storage capacity of onboard storage modules, real-time computing power load and remaining computing power margin of onboard processors, and energy storage dynamic data such as real-time voltage, remaining energy storage, and charging / discharging status of satellite batteries. The onboard local real-time scheduling layer receives the global pre-scheduling baseline from the ground in real time. Using the baseline scheme as a constraint, it combines its own real-time resource status to fine-tune the execution time of local tasks and accurately match idle resources on the satellite. It can quickly respond to sudden onboard needs such as unexpected temporary observation tasks and emergency telemetry and control tasks, while simultaneously transmitting onboard resource status, task execution progress, and local scheduling conflict information back to the ground in real time, achieving autonomous adaptation of local scheduling.

[0043] The dual-layer data linkage mechanism established in this step adopts a customized millisecond-level bidirectional data interaction protocol, abandoning the traditional minute-level and hour-level satellite-to-ground data interaction mode. The spacecraft layer continuously and in real time uploads the latest resource status parameters, task execution feedback results, and local conflict data to the ground layer. After receiving the data from the spacecraft, the ground layer updates the global scheduling baseline parameters in real time and synchronously corrects core scheduling parameters such as resource allocation and task timing arrangement. This achieves dynamic and real-time synchronization of scheduling parameters between the ground global layer and the spacecraft local layer, completely solving the scheduling failure problem caused by data lag and parameter inconsistency between satellite and ground, and ensuring the collaborative unity of the two-layer scheduling.

[0044] S2. Establish a unified modeling system for heterogeneous resources across space and ground levels, and construct a cross-level resource status database.

[0045] To address the issues of fragmented modeling of ground static resources and onboard dynamic resources, non-standardized resource representation, and inability to achieve cross-level resource linkage and matching in traditional satellite scheduling, this step establishes a unified modeling system for heterogeneous resources across satellite and ground levels. It completes the standardization and quantification modeling of ground static resources and onboard dynamic real-time resources, merges them to generate a unified resource model, and builds a dedicated resource status database.

[0046] The implementation process of ground static resource modeling is as follows: For static resources that remain unchanged for a long period or whose changes can be predicted in advance, such as ground tracking and control stations, transmission networks, ground computing power, and fixed satellite orbit parameters, refined quantitative modeling is performed. Specifically, this involves quantifying the time windows and effective tracking and control durations of each ground tracking and control station covering each satellite, the maximum bandwidth capacity, stable transmission periods, and transmission delay parameters of the data transmission link, the peak computing power, effective computing power margin, and data storage capacity of the ground data processing center in a single time period, and fixed orbit parameters such as the semi-major axis, eccentricity, and orbital inclination of the satellite orbits. All quantified static resource parameters are normalized, and combined with resource constraints, available time periods, and performance limits, a multi-dimensional static resource constraint matrix is ​​constructed. Each dimension of the matrix corresponds to the constraint threshold and availability status of a type of ground resource, providing a static constraint basis for global pre-scheduling.

[0047] The implementation process of on-board dynamic resource modeling is as follows: Dynamic quantification and standardized representation are performed on the real-time fluctuating dynamic resources on the satellite. Real-time data is collected for each satellite, including payload working time margin, maximum attitude adjustment angle and response speed, remaining on-board storage capacity, real-time computing power utilization and remaining computing power, real-time battery energy storage, and sustainable working time. Due to the significant differences in the dimensions and numerical ranges of various on-board resources, this embodiment employs an extreme value normalization algorithm to uniformly map all dynamic resource parameters to the 0-1 numerical range, eliminating the influence of dimensions. Standardized dynamic resource state vectors are generated according to resource type, satellite number, and data acquisition time, accurately representing the real-time resource surplus status and working constraints of each satellite.

[0048] After completing the hierarchical modeling, the ground static resource constraint matrix and the onboard dynamic resource state vector are dimensionally fused and correlated to establish a unified cross-level resource model between space and ground, achieving an integrated representation of static fixed resources and dynamic real-time resources. Simultaneously, a cross-level resource state database is built based on this model to store and update static resource parameters and real-time onboard dynamic resource data in real time, providing complete and real-time data support for subsequent cross-level mission resource matching, scheduling optimization, and conflict detection.

[0049] S3. Based on a two-layer collaborative framework and a unified resource model, realize cross-level dynamic resource intelligent matching for multi-source observation tasks and generate an initial two-layer scheduling scheme.

[0050] This step relies on the aforementioned scheduling framework and resource model to complete the standardized processing of multi-source observation tasks, cross-level resource matching and iterative optimization, and output an initial two-level scheduling scheme, which is implemented through four sub-steps.

[0051] S31. Standardized Modeling of Multi-Source Observation Tasks: For multi-source observation tasks with diverse sources, types, and requirements, comprehensive attribute analysis is performed to accurately extract core task parameters. These parameters include observation types such as optical observation, radar observation, and infrared detection; effective observation periods and tolerable periods; accuracy requirements such as observation resolution, positioning accuracy, and imaging clarity; payload requirements such as payload type, operating mode, and continuous operating duration; and priority weight parameters set according to the task's importance level. All parameters are standardized and structured, eliminating invalid and redundant information to complete unified modeling of multi-source tasks. This achieves a unified parameter format for different task types, laying the foundation for subsequent intelligent matching.

[0052] S32. Ground-based Preliminary Global Pre-scheduling: The ground-based global pre-scheduling layer retrieves data from the cross-level unified resource model and resource status database. Using task priority weights as the core sorting criterion, it prioritizes resource allocation for high-priority tasks such as disaster monitoring and national defense observation. Combined with the ground static resource constraint matrix, it performs preliminary matching between standardized multi-source tasks and global static resources such as ground telemetry, tracking, command and control, transmission, computing power, and orbit. Based on task time windows and resource constraints, it completes the overall timing arrangement of multi-satellite cross-satellite tasks, avoiding large-scale resource mismatches and timing conflicts, and generating a preliminary pre-scheduling plan.

[0053] S33. On-board Local Precise Fine-tuning: Each satellite's on-board local real-time scheduling layer receives the preliminary pre-scheduling plan from the ground and, combined with its own real-time dynamic resource state vector, performs fine-tuning of the tasks allocated in the plan. For real-time scenarios such as insufficient on-board payload capacity, computing power overload, and insufficient energy storage, it fine-tunes the execution start time, single observation duration, and payload working mode of local tasks, precisely matching idle on-board resources to tasks to be executed. This solves the problem that global pre-scheduling cannot adapt to real-time dynamic changes on the satellite, improving task matching accuracy.

[0054] S34. Initial Scheme Generation Through Two-Layer Iterative Optimization: This embodiment aims to maximize both global resource utilization and multi-source task scheduling success rate, constructing a two-layer iterative optimization algorithm. The ground layer and the satellite layer alternately iteratively optimize task resource matching relationships and timing arrangements. The ground layer optimizes global resource allocation and cross-satellite timing, while the satellite layer optimizes single-satellite task adaptation and resource utilization. After multiple iterations and convergence, precise matching of dynamic resources across layers is achieved, ultimately generating an initial two-layer scheduling scheme that balances global coordination and local adaptability.

[0055] S4. Establish a two-layer linkage conflict pre-detection and hierarchical resolution mechanism to achieve full-domain conflict prediction and differentiated resolution.

[0056] To address potential task conflicts in the initial two-layer scheduling scheme, this step establishes a two-layer linkage conflict pre-detection and hierarchical resolution mechanism. This mechanism addresses the lag in traditional post-event conflict handling and enables pre-event conflict prediction, hierarchical handling, and hierarchical resolution. Specifically, it includes three core steps: conflict pre-detection, conflict classification, and hierarchical resolution.

[0057] S41. Comprehensive Conflict Pre-detection: This invention constructs a prediction model from four core conflict dimensions to conduct all-round, comprehensive potential conflict detection on the initial two-layer scheduling scheme. Specifically, these include: resource preemption dimension, detecting conflicts arising from multiple tasks and multiple satellites competing for the same ground transmission bandwidth, the same telemetry and control period, and the same onboard payload resources; time overlap dimension, detecting cross-over conflicts between different task execution periods, telemetry and control periods, and data transmission periods; airspace conflict dimension, detecting overlapping airspace of multiple satellite observations and airspace conflicts related to attitude maneuvering; and payload constraint dimension, detecting constraint conflicts such as conflicts in task payload operating modes, exceeding limits on continuous payload operating time, and payload performance mismatch, comprehensively identifying various potential scheduling conflicts in the scheme.

[0058] S42. Conflict Classification: Based on the scope of impact, level of influence, and difficulty of handling, detected conflicts are classified into two categories: Level 1 global conflicts and Level 2 local conflicts. Level 1 global conflicts are core conflicts affecting the entire satellite network, primarily including conflicts over ground control and transmission bandwidth resources among multiple satellites, large-scale overlap of mission timings across satellites, and conflicts in the entire observation space. These conflicts affect the execution of multi-satellite network missions and can lead to global scheduling failures. Level 2 local conflicts are localized conflicts within a single satellite, affecting only the execution of a single mission. These mainly include overlapping mission timings within a single satellite, conflicts over payload resource allocation on a single satellite, and conflicts over the allocation of computing power and energy storage resources on a single satellite. These conflicts have a small scope and low impact.

[0059] S43. Layered and Differentiated Resolution: A differentiated resolution strategy is adopted, consisting of "ground-based handling of global conflicts and on-board autonomous handling of local conflicts." The ground-based global pre-scheduling layer is dedicated to resolving first-level global conflicts. By reconstructing the global task scheduling sequence, rebalancing the allocation of cross-satellite ground static resources, and adjusting the observation windows of cross-satellite tasks, the global scheduling logic is optimized to completely eliminate global scheduling conflicts and ensure the overall orderliness of multi-satellite network scheduling. The on-board local real-time scheduling layer autonomously completes the rapid resolution of second-level local conflicts without ground command intervention. It quickly resolves single-satellite local conflicts through lightweight adjustments such as fine-tuning the execution time of local tasks on a single satellite, switching idle on-board spare payload resources, and dynamically adjusting the allocation ratio of on-board computing power and energy storage. The resolution response is fast and highly adaptable.

[0060] S5. Output optimized scheduling instructions to achieve closed-loop dynamic scheduling of multi-satellite missions.

[0061] After completing conflict pre-detection and hierarchical resolution, and optimizing the scheduling scheme, the final optimized scheduling command is output. The multi-satellite mission is executed collaboratively through a space-ground dual-layer collaborative execution mode, and a closed-loop scheduling system with real-time feedback and dynamic iteration is constructed.

[0062] In practice, the ground-based global pre-scheduling layer uniformly distributes the optimized global scheduling baseline downwards. This baseline clarifies the basic constraints of each satellite, such as the overall mission arrangement, global resource allocation, and core execution time periods, serving as the core basis for the execution of each satellite's missions. Based on the global scheduling baseline, each satellite's onboard local real-time scheduling layer, combined with its own real-time resource status and the results of local conflict resolution, fine-tunes local scheduling commands in real time to control the onboard payloads, attitude control, and computing modules to complete tasks such as observation, transmission, and telemetry.

[0063] Throughout the entire mission execution, the onboard local real-time scheduling layer continuously transmits data back to the ground at a high frequency of seconds, including the real-time working status of onboard payloads, mission completion progress, remaining resources, and local scheduling adjustment records. The ground-based global pre-scheduling layer monitors the global network scheduling status, the execution status of all satellite missions, and resource change trends in real time, achieving full-domain awareness of the scheduling status.

[0064] When abnormal scenarios such as large-scale changes in ground resources, satellite orbital parameter deviations, sudden addition of emergency missions, and large-scale weather changes affecting observation missions are detected, the ground layer immediately coordinates with the satellite layer to iteratively optimize the two-layer pre-scheduling scheme, re-complete resource matching, timing arrangement and conflict detection, update the global scheduling baseline and local scheduling instructions, and continuously adapt to dynamically changing mission scenarios and resource statuses, forming a closed-loop dynamic scheduling process of "scheduling planning - mission execution - status feedback - iterative optimization" to ensure the continuity, stability and efficiency of multi-satellite mission scheduling.

[0065] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for intelligent scheduling and conflict resolution of multi-satellite missions, characterized in that, Includes the following steps: S1. Construct a two-layer collaborative mission scheduling framework between the satellite and the ground, complete the hierarchical functional division and the construction of a two-layer data linkage mechanism. The two-layer collaborative mission scheduling framework includes a ground global pre-scheduling layer and a satellite local real-time scheduling layer. S2. Establish a unified modeling system for heterogeneous resources across space and ground levels, standardize and quantify the static resources on the ground and the dynamic real-time resources on the satellite, and construct a cross-level resource status database; S3. Based on the dual-layer collaborative task scheduling framework and the cross-level heterogeneous resource unified model, realize cross-level dynamic resource intelligent matching of multi-source observation tasks, complete global pre-scheduling planning, and generate an initial dual-layer scheduling scheme. S4. Establish a two-layer linkage conflict pre-detection and hierarchical resolution mechanism to make a global prediction of potential conflicts in the initial two-layer scheduling scheme, distinguish between global conflicts and local conflicts, and execute differentiated resolution strategies. S5 outputs the optimized final scheduling command. The ground global pre-scheduling layer issues the global scheduling baseline, and the on-board local real-time scheduling layer fine-tunes the local scheduling command in real time to complete the collaborative execution of multiple satellite missions and transmit status data back in real time, thereby realizing closed-loop dynamic scheduling.

2. The intelligent scheduling and conflict resolution method for multi-satellite missions according to claim 1, characterized in that, In step S1, the functions of the ground global pre-scheduling layer include: integrating ground telemetry and control resources, data transmission bandwidth, and satellite orbit prediction static resources; receiving the requirements of multi-source observation tasks across the entire domain; completing task priority sorting, preliminary allocation of global resources, and cross-satellite task timing pre-arrangement; generating a global pre-scheduling baseline scheme; and coordinating global conflict handling and hierarchical parameter distribution. The functions of the on-board local real-time scheduling layer include: real-time acquisition of on-board payload working status, on-board computing power margin, satellite attitude, and dynamic energy storage resource data; receiving the ground global pre-scheduling baseline; completing local task fine-tuning and real-time resource precise matching; responding to sudden on-board tasks; and transmitting back on-board status data and local conflict information.

3. The intelligent scheduling and conflict resolution method for multi-satellite missions according to claim 1, characterized in that, In step S1, the dual-layer data linkage mechanism adopts a millisecond-level bidirectional data interaction protocol. The satellite layer uploads resource status and task execution feedback in real time, and the ground layer updates the global scheduling baseline in real time, so as to realize dynamic synchronization of scheduling parameters between the two layers.

4. The intelligent scheduling and conflict resolution method for multi-satellite missions according to claim 1, characterized in that, In step S2, the cross-level heterogeneous resource unified modeling system includes ground static resource modeling and on-board dynamic resource modeling; The ground static resource modeling involves: quantitatively modeling the coverage period of ground telemetry and control stations, data transmission bandwidth capacity, ground data processing computing power, and fixed parameters of satellite orbits to generate a static resource constraint matrix; The on-board dynamic resource modeling is as follows: dynamically quantify the satellite payload working time margin, attitude adjustment capability, on-board storage margin, real-time computing power load, and battery energy storage status, and generate a dynamic resource state vector after normalization. By integrating the static resource constraint matrix and the dynamic resource state vector, a unified resource model spanning satellite and ground levels is constructed.

5. The intelligent scheduling and conflict resolution method for multi-satellite missions according to claim 1, characterized in that, Step S3, the multi-source observation task's cross-level dynamic resource matching and pre-scheduling, specifically includes: S31. Perform attribute analysis on multi-source observation tasks, extract observation type, observation period, accuracy requirements, payload requirements, priority weight parameters, and complete standardized task modeling; S32. The ground global pre-scheduling layer, based on a cross-level unified resource model and combined with task priorities, completes the preliminary matching and temporal arrangement of multi-source tasks and global static resources, and generates a preliminary pre-scheduling plan. S33. The on-board local real-time scheduling layer combines the on-board real-time dynamic resource status to make precise fine-tuning of the task matching relationship and execution time of the preliminary pre-scheduling plan; S34. With the goal of maximizing resource utilization and task scheduling success rate, a two-layer iterative optimization algorithm is used to achieve precise matching of dynamic resources across levels and generate a two-layer pre-scheduling scheme.

6. The intelligent scheduling and conflict resolution method for multi-satellite missions according to claim 1, characterized in that, The two-layer linkage conflict pre-detection and hierarchical resolution mechanism described in step S4 specifically includes: S41. Conflict Pre-detection: From four dimensions, namely resource preemption, time overlap, spatial conflict, and load constraints, the potential conflict prediction of the two-layer pre-scheduling scheme is carried out in the whole domain. S42. Conflict Classification: Conflicts are divided into Level 1 global conflicts and Level 2 local conflicts. The Level 1 global conflicts are global conflicts involving resource contention and cross-satellite timing overlap among multiple satellites. The Level 2 local conflicts are local conflicts involving task timing and payload resource occupation within a single satellite. S43. Hierarchical resolution: The ground-based global pre-scheduling layer is responsible for resolving first-level global conflicts by optimizing the global task arrangement and reallocating cross-satellite resources to achieve global conflict optimization; the on-board local real-time scheduling layer autonomously resolves second-level local conflicts by fine-tuning the time periods of individual satellite tasks and switching idle payload resources to achieve rapid resolution of local conflicts.

7. The intelligent scheduling and conflict resolution method for multi-satellite missions according to claim 1, characterized in that, The closed-loop dynamic scheduling described in step S5 is as follows: During the mission execution, the on-board local real-time scheduling layer transmits data on payload working status, mission completion progress, and remaining resources at a frequency of seconds. The ground global pre-scheduling layer monitors the global scheduling status in real time. When large-scale resource changes or new emergency missions occur, the two-layer pre-scheduling scheme is iteratively optimized to achieve dynamic closed-loop scheduling.

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