An automatic task allocation system and method based on workload and capacity balance monitoring

Abstract

This invention discloses an automatic task allocation system and method based on workload and capacity balance monitoring. The system includes: a dynamic capacity modeling module for constructing ground station capacity models and measurement, control, and operations (TT&C) task models; a real-time workload assessment module for dynamically assessing ground station workload; an adaptive allocation decision module for adaptively balancing and allocating optimal ground station resources; and a task execution monitoring module for employing conflict resolution strategies to eliminate abnormal ground station states. By introducing real-time workload assessment and capacity margin calculation mechanisms, this invention effectively solves the problem of uneven resource utilization caused by traditional static allocation modes. The adaptive allocation decision module can dynamically adjust task allocation strategies and automatically trigger task migration mechanisms, effectively avoiding resource waste caused by some stations being overloaded while others are idle, achieving dynamic load balancing, and significantly improving system resource utilization.

Description

Technical Field

[0001] This invention relates to the field of automatic mission allocation technology for multi-source heterogeneous satellite constellations, and in particular to an automatic mission allocation system and method based on workload and capacity balance monitoring. Background Technology

[0002] With the rapid development of commercial spaceflight, satellite constellations are becoming increasingly complex, exhibiting significant characteristics of being multi-source (multiple operators, multiple models) and heterogeneous (different orbits, different telemetry and control systems, different business requirements). Against this backdrop, higher demands are placed on the mission allocation capabilities of ground-based telemetry, tracking, and command (TT&C) systems. Currently, mission allocation in this field mainly relies on the following traditional technical solutions:

[0003] Fixed allocation mode: This mode assigns fixed satellites or orbital planes to specific ground stations for telemetry, tracking, and command (TT&C). While simple to implement, it lacks flexibility, leading to low resource utilization and an inability to adapt to dynamically changing TT&C needs. Simple rule-based scheduling: For example, allocation is based on the order of mission arrival or a preset priority. Although easy to implement, this method lacks optimization considerations for the overall workload, easily resulting in some ground stations being overloaded while others are idle. Centralized optimization algorithm: This method uses linear programming, heuristic algorithms, etc., to perform static optimization within a single scheduling cycle. Theoretically, this method can achieve good allocation schemes, but it is computationally complex, has poor dynamic adaptability, and struggles to effectively handle dynamic scenarios such as sudden real-time insertion of missions or anomalies in ground station resources.

[0004] Existing technical solutions suffer from the following major drawbacks: First, uneven resource utilization and a lack of a global perspective in allocation strategies can easily lead to some ground stations being overloaded for extended periods while others remain idle, resulting in low overall system resource utilization efficiency. Second, poor heterogeneous compatibility; for various satellites supporting different frequency bands, protocols, and antenna requirements, there is a lack of a unified and quantitative matching model, making it difficult to achieve efficient and accurate resource and task adaptation. Third, a lack of dynamic balancing capabilities; once tasks are allocated, the system cannot monitor the matching degree between the actual workload of each ground station and its processing capacity in real time, and cannot dynamically adjust according to the actual operating status, potentially leading to task backlog or resource idleness. Fourth, slow emergency response; when sudden high-priority tasks occur or a ground station malfunctions, task reassignment heavily relies on manual intervention, resulting in slow response times and affecting the timeliness and reliability of task execution. Fifth, difficulty in multi-objective optimization; task allocation needs to simultaneously consider multiple constraints such as task priority, tracking arc, data real-time performance, and equipment health status, making it difficult for traditional methods to achieve an effective balance among these potentially conflicting objectives.

[0005] For example, invention application No. 202311598703.4 discloses an automated operation method, system, and computer equipment for satellite telemetry, tracking, and command (TT&C) equipment. This solution can automatically configure appropriate task macros based on different satellite TT&C requirements and the status of different subsystem equipment, reducing manual operation, ensuring the accuracy of parameter issuance, and thus improving the success rate of TT&C missions. However, this solution also suffers from uneven utilization of ground station resources and poor heterogeneous compatibility.

[0006] Therefore, there is an urgent need in this field for a new method that can automate and adaptively allocate the telemetry, tracking, and command (TT&C) tasks of multi-source heterogeneous satellite constellations and balance the workload and capacity of ground stations in real time. Summary of the Invention

[0007] To address the aforementioned problems, the present invention aims to provide an automatic task allocation system and method based on workload and capacity balance monitoring, enabling automated and adaptive allocation of multi-source heterogeneous satellite telemetry, tracking, and command (TT&C) tasks, thereby improving overall resource utilization. A dynamic feedback mechanism is established to continuously optimize the allocation strategy during task execution. This enhances the system's robustness in responding to unexpected tasks and resource anomalies.

[0008] This invention provides an automatic task allocation system and method based on workload and capacity balance monitoring.

[0009] First aspect: An automatic task allocation system based on workload and capacity balance monitoring, comprising:

[0010] The dynamic capability modeling module is used to manage resource information of multiple ground stations, build ground station capability models, receive telemetry, tracking, and command (TT&C) mission requests from multi-source heterogeneous satellite constellations, and build TT&C mission models.

[0011] The real-time workload assessment module is used to dynamically assess the workload of ground stations in conjunction with measurement, control, and operation task requests.

[0012] The adaptive allocation decision module is used to calculate the ground station capacity margin and, based on the ground station capacity margin and combined with the telemetry, tracking and command (TT&C) mission requests, adaptively balance and allocate the optimal ground station resources.

[0013] The task execution monitoring module is used to monitor the real-time operation status of ground station resources and computing power matching degree, and to take conflict resolution strategies to eliminate abnormal states of ground stations.

[0014] In one embodiment of the present invention, the ground station capability model is for each ground station. Establish a multidimensional capability vector, represented as:

[0015]

[0016] in, To support frequency band sets, For transmit power and receive sensitivity, For antenna aperture and tracking angular velocity, Rate the health status of the equipment. For time availability.

[0017] In one embodiment of the present invention, the measurement, operation and control task model is applied to each measurement, operation and control task. Establish a multidimensional capability vector, represented as:

[0018]

[0019] in, For task type, Identify the target satellite. Task time window , For the amount of data or the number of instructions, Priority For resource needs.

[0020] In one embodiment of the present invention, the dynamic assessment of ground station workload is expressed as:

[0021]

[0022] For ground station At any moment The workload, This represents the current number of parallel tasks. The task data volume divided by the station's support rate for that task. For task switching overhead, These are the weighting coefficients.

[0023] In one embodiment of the present invention, the ground station capability margin is expressed as:

[0024]

[0025] in, For ground station In the time window Internal capacity margin For the task Expected occupancy Time, Factors such as weather and equipment reliability during this period are considered reduction factors. Normalized value for health status (0~1).

[0026] In one embodiment of the present invention, the ground station resource allocation is represented as follows:

[0027]

[0028] in, For the newly arrived task set, As an allocation marker, To preset the load safety factor, This represents the capability margin threshold.

[0029] In one embodiment of the present invention, adaptive balancing allocation of optimal ground station resources includes:

[0030] Set overload warning value and idle resources ,when or When this occurs, adaptive allocation is triggered, which includes task migration and load balancing.

[0031] In one embodiment of the present invention, the computing capability matching degree is expressed as:

[0032]

[0033] in, For the task With ground station Matching degree For frequency band matching indication function, The minimum power required for the task. The angle between the satellite and the ground station is the geometric angle.

[0034] In one embodiment of the present invention, the conflict resolution strategy includes:

[0035] When multiple tasks compete for the same ground station, a tiered decision-making process is adopted: high-priority tasks with high capability matching are allocated first; for tasks of the same priority, the task that maximizes the ground station's capability margin is selected. The task with the least impact is dropped; if conflicts still occur, a virtual queue is introduced, and the task time window is readjusted.

[0036] The second aspect: A method for automatic task allocation based on workload and capacity balance monitoring, including the following steps:

[0037] S1: Receives measurement, operation and control task requests, parses task parameters, and obtains resource information and current working status of each ground station;

[0038] S2: Dynamically assess the workload of ground stations based on telemetry, tracking, and command (TT&C) mission requests;

[0039] S3: Calculate the ground station capacity margin, and based on the ground station capacity margin and the telemetry, tracking, and command (TT&C) mission requests, adaptively balance and allocate the optimal ground station resources;

[0040] S4. Monitor the operational status of ground station resources and computing capacity matching in real time, and adopt conflict resolution strategies to eliminate abnormal states of ground stations.

[0041] Third aspect: An electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, performs the steps of the method provided in the second aspect.

[0042] Fourth aspect: A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the second aspect.

[0043] The beneficial effects of this invention are:

[0044] 1. This invention effectively solves the problem of uneven resource utilization caused by the traditional static allocation mode by introducing a real-time workload assessment and capacity margin calculation mechanism. The system can continuously and quantitatively assess the workload of each ground station at time t and predict its capacity margin within a future time window Δt. The adaptive allocation decision module can dynamically adjust the task allocation strategy and automatically trigger the task migration mechanism. This closed-loop control mechanism ensures that the load of the ground station cluster is always balanced, effectively avoiding the waste of resources caused by some stations being overloaded while others are idle, achieving dynamic load balancing, and significantly improving the system's resource utilization rate.

[0045] 2. This invention constructs a unified ground station capability model and telemetry, tracking, and command (TT&C) task model. By defining multi-dimensional capability vectors and task requirement vectors, and introducing a quantitative matching degree algorithm, it evaluates the suitability between tasks and resources. Priority is given to allocating high-priority tasks with high matching degrees, and for tasks of the same priority, the solution that minimizes the decrease in ground station capability margin is selected. This achieves optimal overall system efficiency while satisfying multiple constraints.

[0046] 3. The closed-loop control mechanism established in this invention significantly improves the system's ability to respond to sudden anomalies. The task execution monitoring module tracks the health status of the ground station and external factors in real time. Once equipment failure or task execution anomaly is detected, the emergency takeover process is immediately triggered. This dynamic rebalancing capability enables the system to quickly recover and re-optimize allocation strategies when faced with sudden high-priority tasks or resource anomalies, improving system robustness and emergency response efficiency, and ensuring reliable task execution. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0048] Figure 2 This is a schematic flowchart of the method of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of the electronic device of the present invention. Detailed Implementation

[0050] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0051] Existing intelligent allocation methods for telemetry, tracking, command and control (TT&C) tasks in multi-source heterogeneous satellite constellations suffer from problems such as uneven resource utilization, poor heterogeneous compatibility, lack of dynamic balance, slow emergency response, and conflicts among multiple objectives.

[0052] To address the aforementioned problems, this invention discloses an automatic task allocation method based on workload and capacity balance monitoring. To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments.

[0053] Example 1:

[0054] This embodiment discloses an automatic task allocation system based on workload and capacity balance monitoring, such as... Figure 1 As shown, the system includes a dynamic capacity modeling module, a real-time workload assessment module, an adaptive allocation decision module, and a task execution monitoring module. The system continuously monitors the remaining capacity margin of each ground station and dynamically adjusts the measurement, operation, and control task allocation strategy accordingly. Among these:

[0055] The dynamic capability modeling module is used to manage resource information of multiple ground stations, build ground station capability models, receive telemetry, tracking, and command (TT&C) mission requests from multi-source heterogeneous satellite constellations, and build TT&C mission models.

[0056] Ground station capability model for each ground station Establish a multidimensional capability vector, represented as:

[0057]

[0058] Supports a set of frequency bands (such as S-band, X-band, and Ka-band).

[0059] Transmit power and receiver sensitivity;

[0060] Antenna aperture and tracking angular velocity;

[0061] Equipment health status score (based on telemetry data);

[0062] Time availability (considering factors such as maintenance and weather);

[0063] The measurement, operation, and control task model applies to each measurement, operation, and control task. Establish a multidimensional capability vector, represented as:

[0064]

[0065] Task type (telemetry, remote control, data transmission, orbit determination, etc.);

[0066] Target satellite identifier;

[0067] Task Time Window ;

[0068] : Data volume or number of instructions;

[0069] Priority (urgent, routine, maintenance, etc.);

[0070] Resource requirements (minimum signal-to-noise ratio, code rate, antenna requirements, etc.);

[0071] Based on the dynamic capability modeling module, the system can collect and analyze the current operating status and performance parameters of ground station resources in real time, and construct a ground station capability model for these resources. This model not only includes the basic attributes of the resources but also updates their actual available capabilities based on real-time data. By performing multi-dimensional matching between the dynamic capability vectors of ground station resources and the multi-dimensional capability vectors of telemetry, tracking, and command (TT&C) tasks, the system can accurately assess the adaptability of various ground station resources to different TT&C tasks, providing underlying capability support for the intelligent allocation of subsequent tasks.

[0072] Furthermore, the dynamic capability modeling module possesses self-learning and adaptive capabilities, enabling it to continuously optimize the capability assessment algorithm based on feedback from the execution results of measurement, operation, and control tasks. This improves the accuracy and timeliness of the resource capability model, ensuring that ground station resources are highly aligned with the actual needs of measurement, operation, and control tasks in complex and ever-changing environments.

[0073] The real-time workload assessment module is used to dynamically assess the workload of ground stations in conjunction with measurement, control, and operation task requests.

[0074] Dynamic assessment of ground station workload includes: for ground stations At any moment workload Defined as:

[0075]

[0076] : The current number of parallel tasks;

[0077] : The amount of task data divided by the station's support rate for that task;

[0078] Task switching overhead (antenna turning, protocol reconfiguration, etc.);

[0079] Weighting coefficients are calibrated based on actual measured data.

[0080] The real-time workload assessment module dynamically calculates and summarizes the workload of currently undertaken tasks at each ground station. This calculation comprehensively considers factors such as task data volume, support rate, and task switching overhead to provide a quantitative assessment, thereby accurately grasping the real-time load workload of each ground station. Combined with the available time of the ground station within the time window Δt, the estimated time occupied by scheduled tasks, the normalized value of health status, and reduction factors determined by factors such as weather and equipment reliability, the capacity margin of each ground station is calculated and updated in real time. .

[0081] The adaptive allocation decision module is used to adaptively balance and allocate optimal ground station resources based on the ground station's capacity margin and the telemetry, tracking, and command (TT&C) mission requests.

[0082] Ground station capacity margin is expressed as:

[0083]

[0084] in, For ground station In the time window Internal capacity margin For the task Expected occupancy Time, Factors such as weather and equipment reliability during this period are considered reduction factors. Normalized value for health status (0~1).

[0085] Initial allocation, for newly arrived task sets The optimization solution for ground station resource allocation is expressed as follows:

[0086]

[0087] : Assignment flag;

[0088] : Preset load safety factor (e.g., 80%);

[0089] Capacity margin threshold: ensures that there is still spare capacity after allocation.

[0090] Based on monitoring triggers, the system adaptively balances and allocates optimal ground station resources, dynamically rebalancing the system while continuously monitoring all ground stations. When detected:

[0091] Overload warning: (e.g., <10%); or resources are idle: When (e.g., >60%) and there are pending tasks, a balanced allocation is triggered, including:

[0092] Task migration: Migrate some migrateable tasks (such as non-real-time data transmission) from overloaded stations to idle stations.

[0093] Load balancing: Based on margin values, an optimal power flow algorithm similar to power grid scheduling is used to redistribute tasks for future time periods.

[0094] The task execution monitoring module is used to monitor the real-time operation status of ground station resources and computing power matching degree, and to take conflict resolution strategies to eliminate abnormal states of ground stations.

[0095] For the task With ground station Define the matching degree :

[0096]

[0097] : Band matching indicator function;

[0098] Minimum power required for the task;

[0099] The geometric angle between the satellite and the ground station affects tracking performance.

[0100] When a station fails or multiple tasks compete for the same ground station, a hierarchical decision-making process is adopted to automatically migrate the tasks to the backup station with the highest priority.

[0101] This includes prioritizing tasks based on high priority and high matching degree; for tasks of the same priority, selecting the station that best matches the task. The task with the least impact (i.e., the most energy-efficient allocation) is selected; if conflicts still occur, a virtual queue is introduced, and the task planning layer is notified to adjust the time window.

[0102] This system, in this embodiment, constructs and stores static / dynamic capability models of various ground stations, receives and standardizes measurement, control, and operation task requirements. Real-time ground stations. At any moment workload With ground station In the time window Internal capacity margin The initial allocation and dynamic rebalancing of resources for the measurement, control, and operation tasks are performed. Then, through calculation... Resolve abnormal conflicts.

[0103] Example 2:

[0104] Based on the system of Embodiment 1, this embodiment discloses an automatic task allocation method based on workload and capacity balance monitoring, such as... Figure 2 The method described herein includes:

[0105] S1: Receives measurement, operation and control task requests, parses task parameters, and obtains resource information and current working status of each ground station.

[0106] Standardize the processing of telemetry, tracking, and command (TT&C) mission requests and ground station resource information, unifying data formats and indicator definitions to ensure the accuracy and consistency of subsequent calculations. Mission parameters include key information such as mission type, priority, time window requirements, and required resource types and quantities. Ground station resource information covers its static capability parameters (such as the maximum number of missions it can handle, equipment models and performance indicators, etc.) and dynamic capability parameters (such as the current assigned mission status, available equipment resources, and real-time communication link status, etc.). The current working status is specifically reflected in the workload of each ground station at time t and its capacity margin within a specific time window.

[0107] Through standardization, data from different sources and in different formats are transformed into unified data that the system can directly recognize and process, providing a data foundation for subsequent task matching and allocation decisions.

[0108] S2: Dynamically assess the workload of ground stations based on telemetry, tracking, and command (TT&C) mission requests.

[0109] A workload assessment model is constructed, comprehensively considering the number of tasks currently undertaken by the ground station, the complexity coefficient of each task, the estimated execution time, and the task priority weight. For each assigned task, a corresponding complexity coefficient is assigned based on its task type. The complexity coefficient is positively correlated with the task's utilization of equipment resources, data processing volume, and operational difficulty. Simultaneously, the task priority weight is transformed into an impact factor on workload; higher-priority tasks are assigned higher weight values ​​during assessment to reflect their actual contribution to the ground station's workload. The real-time workload value of the ground station at the current time t is calculated using a weighted summation method.

[0110] Secondly, when calculating the ground station capacity margin, the ground station is determined within a specific time window. The system predicts the total workload demand within the time window. The ground station capacity margin M is defined as the difference between the theoretical maximum processing capacity and the predicted total workload demand. If the capacity margin M is positive, it indicates that the ground station still has remaining capacity to take on new tasks within the time window; if M is zero or negative, it indicates that the ground station has no capacity margin or is even overloaded, and it should avoid assigning tasks.

[0111] S3: Calculate the ground station capacity margin, and based on the ground station capacity margin and combined with the telemetry, tracking, and command (TT&C) mission requests, adaptively balance and allocate the optimal ground station resources.

[0112] When a new measurement, control, and operations (TCA) task request is received, the system first estimates the incremental workload that will be generated when the task is executed at different ground stations, based on the task type, priority, and other information, according to the workload assessment method. Then, it calculates the predicted total workload requirement for each ground station after incorporating the new task and compares it with the theoretical maximum processing capacity of each ground station to obtain a new capacity margin prediction value.

[0113] The system prioritizes ground stations with the largest positive capacity margin prediction values ​​as the initial assignment targets for the mission. This ensures that ground stations still have sufficient capacity redundancy to cope with emergencies after mission assignment. The system continuously monitors all ground stations. When an overload warning is detected: (e.g., <10%); or resources are idle: When there are tasks pending (e.g., >60%), trigger task migration, load balancing, or emergency takeover to balance the allocation.

[0114] Throughout the entire resource allocation and balancing process, the system continuously monitors the real-time workload and capacity margin changes of each ground station to ensure that the overall load of the ground station cluster is in a balanced and controllable state, avoiding task execution failures or inefficiencies caused by single-point overload, and achieving optimal matching between measurement, operation and control tasks and ground station resources.

[0115] S4. Monitor the operational status of ground station resources and computing capacity matching in real time, and adopt conflict resolution strategies to eliminate abnormal states of ground stations.

[0116] For the task With ground station Define capability matching degree : Adopt conflict resolution strategies based on capability matching.

[0117] When multiple tasks compete for the same ground station, a hierarchical decision-making process is adopted: high-priority and highly matched tasks are prioritized for allocation; for tasks of the same priority, the station is selected as the preferred choice. The task with the least impact (i.e., the most energy-efficient allocation) is selected; if conflicts still occur, a virtual queue is introduced, and the task planning layer is notified to adjust the time window.

[0118] Application examples:

[0119] The constellation is assumed to consist of 50 low-Earth orbit satellites (including different types such as optical, SAR, and IoT), and the ground network consists of 4 main ground stations (supporting multiple frequency bands) and 3 mobile ground stations (specific frequency bands).

[0120] Scenario 1: Daily task allocation. The system allocates tasks according to the initial optimized allocation and monitors the capacity margin of each ground station. When a ground station is affected by weather... When the capacity margin decreases or falls below the threshold, some of its non-real-time data transmission tasks are automatically migrated to mobile ground stations with higher capacity margins.

[0121] Scenario 2: An emergency mission occurs when an urgent telemetry command (high priority) is received from a satellite. The system immediately assigns the ground station with the highest search capability margin and matching accuracy (even if it is currently idle) and triggers a slight rearrangement of subsequent tasks.

[0122] Scenario 3: Ground station failure, health status of a major ground station The sudden drop in performance was identified as an abnormal state. The system migrated all its tasks to other ground stations based on priority and compatibility, and adjusted its plans for the next 24 hours to ensure that critical missions were not affected.

[0123] The present invention also provides an electronic device, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 3 As shown, the electronic device may include a processor, a communications interface, memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions from the memory, for example, to execute the following method:

[0124] S1: Receives measurement, operation and control task requests, parses task parameters, and obtains resource information and current working status of each ground station;

[0125] S2: Dynamically assess the workload of ground stations based on telemetry, tracking, and command (TT&C) mission requests;

[0126] S3: Calculate the ground station capacity margin, and based on the ground station capacity margin and the telemetry, tracking, and command (TT&C) mission requests, adaptively balance and allocate the optimal ground station resources;

[0127] S4. Monitor the operational status of ground station resources and computing capacity matching in real time, and adopt conflict resolution strategies to eliminate abnormal states of ground stations.

[0128] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] This invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the methods provided in the above embodiments, including, for example:

[0130] S1: Receives measurement, operation and control task requests, parses task parameters, and obtains resource information and current working status of each ground station;

[0131] S2: Dynamically assess the workload of ground stations based on telemetry, tracking, and command (TT&C) mission requests;

[0132] S3: Calculate the ground station capacity margin, and based on the ground station capacity margin and the telemetry, tracking, and command (TT&C) mission requests, adaptively balance and allocate the optimal ground station resources;

[0133] S4. Monitor the operational status of ground station resources and computing capacity matching in real time, and adopt conflict resolution strategies to eliminate abnormal states of ground stations.

[0134] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0135] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A task automatic assignment system based on workload and capacity balance monitoring, characterized by, include: The dynamic capability modeling module is used to manage resource information of multiple ground stations, build ground station capability models, receive telemetry, tracking, and command (TT&C) mission requests from multi-source heterogeneous satellite constellations, and build TT&C mission models. The real-time workload assessment module is used to dynamically assess the workload of ground stations in conjunction with measurement, control, and operation task requests. The adaptive allocation decision module is used to calculate the ground station capacity margin and, based on the ground station capacity margin and combined with the telemetry, tracking and command (TT&C) mission requests, adaptively balance and allocate the optimal ground station resources. The task execution monitoring module is used to monitor the real-time operation status of ground station resources and computing power matching degree, and to take conflict resolution strategies to eliminate abnormal states of ground stations.

2. The system according to claim 1, characterized in that, The ground station capability model represents for each ground station A multidimensional capability vector is established, denoted as: wherein, for supporting a frequency band set, for transmitting power and receiving sensitivity, for an antenna aperture and a tracking angular velocity, for a device health status score, for time availability.

3. The system according to claim 2, characterized in that, The measurement operation control task model is for each measurement operation control task A multidimensional capability vector is established and is represented as: wherein, is a task type, is a target satellite identification, is a task time window , is a data volume or instruction number, is a priority, is a resource requirement.

4. The system of claim 3, wherein, The dynamic assessment of ground station workload is expressed as follows: for the ground station at time , is the current number of parallel tasks, is the amount of task data divided by the support rate of the station for the task, is the task switching overhead, is the weight coefficient.

5. The system of claim 4, wherein, The ground station capacity margin is expressed as: wherein, is a ground station the capacity margin within a time window, the capacity margin within a time window, is a mission the time of the projected occupancy, the time of the projected occupancy, is a reduction factor for weather, equipment reliability, etc. within the period, is a health state normalized value (0~1).

6. The system according to claim 5, characterized in that, The allocation of ground station resources is represented as follows: in, For the newly arrived task set, As an allocation marker, To preset the load safety factor, This represents the capability margin threshold.

7. The system according to claim 6, characterized in that, Adaptive balancing allocation of optimal ground station resources, including: Set overload warning value and idle resources ,when or When this occurs, adaptive allocation is triggered, which includes task migration and load balancing.

8. The system according to claim 7, characterized in that, Computational capability matching degree, expressed as: in, For the task With ground station Matching degree For frequency band matching indication function, The minimum power required for the task. The angle between the satellite and the ground station is the geometric angle.

9. The system according to claim 8, characterized in that, Conflict resolution strategies include: When multiple tasks compete for the same ground station, a hierarchical decision-making process is adopted: high-priority and high-capability matching tasks are allocated first; for tasks of the same priority, the task that minimizes the decrease in ground station capability margin is selected; if there is still a conflict, a virtual queue is introduced and the task time window is readjusted.

10. The system according to any one of claims 1 to 9, characterized in that, An automatic task allocation method based on workload and capacity balance monitoring, the method comprising: S1: Receives measurement, operation and control task requests, parses task parameters, and obtains resource information and current working status of each ground station; S2: Dynamically assess the workload of ground stations based on telemetry, tracking, and command (TT&C) mission requests; S3: Calculate the ground station capacity margin, and based on the ground station capacity margin and the telemetry, tracking, and command (TT&C) mission requests, adaptively balance and allocate the optimal ground station resources; S4. Monitor the operational status of ground station resources and computing capacity matching in real time, and adopt conflict resolution strategies to eliminate abnormal states of ground stations.

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