A fully digital simulation platform for satellite measurement and control subsystem

Abstract

The present invention discloses a fully digital simulation platform for a satellite measurement and control subsystem, which relates to the technical field of satellite measurement, operation and control planning. The present invention uses orbital dynamics simulation and deep learning models to predict orbital coverage and evaluate the resource status of ground stations, generate coverage windows and resource availability data, and generate optimized task scheduling plans through task priority sorting and load balancing models to improve task execution efficiency while balancing ground station resources; uses a digital twin model to simulate task execution paths and link performance, and dynamically adjusts the scheduling plan based on simulation feedback to make the plan more in line with actual task requirements; in addition, relying on orbital dynamics modeling and deep learning technology, the accuracy of orbital coverage prediction is improved; efficient task scheduling is performed through intelligent optimization algorithms; and digital twin technology is used to ensure the reliability of link performance evaluation. Compared with traditional methods, it reduces the time for manual intervention and repeated adjustments, and significantly improves the resource utilization rate of ground measurement and control stations.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite measurement, operation, control and planning, in particular to a full-digital simulation platform for a satellite measurement and control subsystem. Background Art

[0002] Digital simulation is a key tool in satellite mission planning and is widely used in mission verification, resource scheduling and link performance analysis. In actual operation, the satellite measurement, operation and control plan is based on the distribution of satellite orbits and ground stations, calculates the time period when the satellite passes through the ground station, and makes overall planning based on the daily measurement, operation and control needs. The measurement, operation and control plan is generally divided into daily plans and weekly plans. The daily plan is usually formulated one day in advance and is more accurate. It is not adjusted except in special circumstances. In special circumstances, it can be adjusted within a few hours before the mission execution. The weekly plan focuses more on generality, understanding the tracking capabilities of each station in advance, and the specific mission execution is based on the daily plan.

[0003] It can be seen that this method relies on manual experience and multiple adjustments. As the number of tasks increases, the efficiency problem becomes more serious; the planning cycle of the traditional solution is long, and it is difficult to respond to dynamic changes in a timely manner. At the same time, the optimization capability of resource scheduling is insufficient, and the utilization rate of ground station resources is not maximized. Some measurement and control stations are in a high-load state for a long time, while other stations are not fully utilized, and resource allocation is unbalanced.

[0004] To address these issues, some traditional methods have refined the design of simulation modules, separating functions such as orbit prediction, link calculation, and task allocation; some solutions have optimized the resource scheduling process through preset rules and manual intervention, reducing reliance on complex algorithms; however, these improvements can only alleviate the problem to a certain extent, and have not fundamentally solved the problems of long time consumption and low resource utilization efficiency. When dealing with dynamic adjustments and large-scale task collaboration needs, traditional methods still cannot meet actual needs; therefore, there is an urgent need for a fully digital simulation solution for satellite measurement and control subsystems to solve such problems. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a fully digital simulation platform for satellite measurement and control subsystems to solve the problem of coordination requirements for large-scale tasks, the formulation of traditional measurement and control management plans requires a lot of manpower and time, and measurement and control station resources cannot be maximized.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] The embodiment of the present invention provides a full digital simulation platform for a satellite measurement and control subsystem, which includes:

[0009] The initialization module is used to obtain measurement, operation and control data, including satellite orbit data, ground measurement and control station resource load information and mission requirements, and pre-process the measurement, operation and control data to convert it into standardized data;

[0010] The resource analysis module receives standardized data from the initialization module and uses orbital dynamics simulation combined with a deep learning model to predict satellite orbit coverage. It also evaluates the resource load status of the ground tracking and control station and generates analytical data, including coverage window time and resource availability.

[0011] The task allocation and scheduling module uses the analysis data of the resource analysis module and combines the importance of satellite missions, resource availability and coverage window time to dynamically generate task scheduling plans;

[0012] The simulation verification module receives the scheduling plan generated by the task allocation and scheduling module, and uses the digital twin model to simulate and adjust the link performance of the plan to generate the adjusted verification results;

[0013] The plan output module receives the verification results of the simulation verification module and generates a complete plan report.

[0014] As a preferred solution of the satellite measurement and control subsystem full digital simulation platform of the present invention, the method of generating a task scheduling plan includes:

[0015] Build a task priority table and sort the tasks according to their importance and coverage window time.

[0016] A load balancing model is established to adjust the task allocation of ground tracking and control stations based on resource availability.

[0017] As a preferred solution of the satellite measurement and control subsystem all-digital simulation platform of the present invention, wherein: the simulation verification module simulates the task execution path, adjusts the resource configuration according to the simulation feedback, and generates the adjusted verification result;

[0018] The planning report includes the final task allocation table and alternative adjustment plans designed to address possible unexpected tasks or resource conflicts.

[0019] As a preferred solution of the satellite measurement and control subsystem all-digital simulation platform of the present invention, the simulation steps of the satellite measurement and control subsystem all-digital simulation platform include:

[0020] Step S1, extracting and standardizing satellite orbit data, ground tracking and control station resource load information and mission requirements to form basic data input;

[0021] Step S2: Based on the basic data input, the satellite orbit coverage is predicted through orbital dynamics simulation and deep learning models, the ground tracking and control station resource status is evaluated, and analytical data is generated, including coverage window time and resource availability;

[0022] Step S3: construct a task priority table, sort the tasks according to their importance and coverage window time, and adjust the ground tracking and control station task allocation based on resource availability through a load balancing model to output a task scheduling plan;

[0023] Step S4: Use the digital twin model to simulate the link performance of the task scheduling plan, simulate the task execution path, and dynamically adjust the plan based on the feedback to generate the adjusted verification results;

[0024] Step S5: Generate a plan report based on the verification result, the report content including a task allocation table and a backup adjustment plan.

[0025] As a preferred solution of the satellite tracking and control subsystem all-digital simulation platform described in the present invention, the steps of predicting satellite orbit coverage and evaluating the resource status of ground tracking and control stations through orbital dynamics simulation and deep learning model are as follows:

[0026] The dynamic modeling of the track is carried out, and the model formula is:

[0027]

[0028] Among them, r(t) represents the satellite position vector at time t, r0 is the initial position vector, v(t) is the satellite velocity vector at time t, v0 is the initial velocity vector, and a total (τ) represents the total acceleration, including gravity and other external disturbances, and τ is the time-integrated variable;

[0029] Calculate the visibility between the ground station and the satellite. The calculation formula is:

[0030]

[0031] When θ≤θ max When , the ground station s is visible,

[0032] Among them, θ represents the angle between the satellite and the ground station, g s is the position vector of the ground station s, |r(t)| represents the modulus of the satellite position vector, |g s | represents the modulus of the ground station position vector, θ max is the visible angle threshold;

[0033] Perform time discretization processing, the discretization formula is:

[0034] If θ≤θ max, then C s,t =1, otherwise C s,t =0,

[0035] Among them, C s,t represents the coverage status of ground station s at time step t, t is the discretized time step, θ max is the visible angle threshold;

[0036] Perform deep learning model fusion, the fusion formula is:

[0037] y t =σ(W2·ReLU(W1·X t +b1)+b2),

[0038] Among them, y t Output results of the deep learning model at time step t, including coverage period and load status, X t represents the input features at time step t, including orbital parameters and ground station positions, W1 and W2 are the weight matrices of the first and second layers respectively, b1 and b2 are the bias vectors of the first and second layers respectively, ReLU(·) is the activation function with the value max(0,x), and σ(·) is the Sigmoid activation function;

[0039] Evaluate resource status. The evaluation formula is:

[0040]

[0041] Among them, R s represents the average load rate of ground station s, T is the total number of time steps, U s is the unit time resource usage of ground station s, C s,t is the coverage status of ground station s at time step t,

[0042] Output analysis data D analysis :

[0043] D analysis ={(t start ,t end ,R s )|s=1,2,…,S},

[0044] Among them, D analysis To analyze the data set, t start and t end Respectively represent the start time and end time of the coverage window, R s It represents the resource load rate of ground station s, and S is the total number of ground stations.

[0045] As a preferred solution of the satellite measurement and control subsystem full digital simulation platform of the present invention, the steps of constructing a task priority table and sorting tasks according to their importance and coverage window time are as follows:

[0046] Calculate the task priority using the following formula:

[0047]

[0048] Among them, P j represents the priority of task j, w1, w2, w3 are the weight coefficients of task importance, time window and resource requirements respectively, satisfying w1+w2+w3=1, I j Score the importance of task j, T j is the coverage time window length of task j, T max is the maximum time window of all tasks, R j is the resource requirement of task j, and the task list is arranged from high to low priority;

[0049] Construct a load balancing model. The model formula is:

[0050]

[0051] Among them, S is the total number of ground stations, M is the total number of missions, and A j,s is a binary variable indicating whether task j is assigned to ground station s, 1 indicates assigned, 0 indicates not assigned, C s is the total resources of ground station s.

[0052] As a preferred solution of the satellite tracking and control subsystem all-digital simulation platform of the present invention, the step of adjusting the ground tracking and control station task allocation based on resource availability and outputting the task scheduling plan is as follows:

[0053] Define the task allocation adjustment rule as follows:

[0054] Then A j,s =1, otherwise A j,s =0,

[0055] Among them, A j,s It indicates the state of the adjusted task j assigned to the ground station s, threshold is the load balancing threshold, which indicates the upper limit of the resource utilization of the ground station;

[0056] Output task scheduling plan S schedule :

[0057] S schedule ={(j,s)|j=1,2,…,M; s=1,2,…,S},

[0058] Among them, S schedule is the final task scheduling plan, j is the task number, and s is the ground station number.

[0059] As a preferred solution of the satellite measurement and control subsystem full digital simulation platform described in the present invention, the steps of using the digital twin model to simulate the link performance of the task scheduling scheme and simulating the task execution path are as follows:

[0060] Model the link performance indicators and define the link performance score. The model formula is:

[0061]

[0062] Among them, L s represents the link performance score of ground station s, SNR s is the signal-to-noise ratio of ground station s, SNR threshold is the minimum signal-to-noise ratio requirement, B s is the link load of ground station s,

[0063] Combined with the task scheduling scheme, the digital twin model is used to dynamically simulate task execution. The model formula is:

[0064] F s =simulate(S schedule ,L s ,R s ),

[0065] Among them, F s is the simulation feedback data of ground station s, S schedule For the task scheduling scheme, define the distribution relationship between tasks and ground stations, L s R is the link performance score of ground station s. s is the resource load rate of ground station s, simulate(·) represents the simulation function, which simulates the execution of the task in the link. The output includes link delay, task completion rate and resource usage status;

[0066] Analyze the task execution path step by step and extract key performance indicators:

[0067]

[0068] Among them, T delay represents the average delay time of the link task, t represents the discrete time step, T is the total number of time steps, BW t,s is the bandwidth of ground station s at time step t, Load t,s is the link load of ground station s at time step t.

[0069] As a preferred solution of the satellite measurement and control subsystem full digital simulation platform of the present invention, the step of generating the adjusted verification result according to the feedback dynamic adjustment scheme is:

[0070] Generate simulation feedback results, integrate the simulation feedback data of each ground station, and form the task execution performance matrix M performance :

[0071] M performance ={(L s ,T delay ,R s )|s=1,2,…,S},

[0072] Among them, M performance represents the performance matrix, S is the total number of ground stations, L s is the link performance score of ground station s, T delay is the average delay time of ground station S, R s is the resource load rate of ground station S;

[0073] Adjust the task scheduling plan based on simulation feedback. The adjustment formula is:

[0074] S adjusted =S schedule +ΔS(M performance ),

[0075] Among them, S adjusted is the adjusted task scheduling scheme, ΔS(M performance ) is the adjustment optimization amount based on the performance matrix feedback.

[0076] As a preferred solution of the satellite measurement and control subsystem full digital simulation platform of the present invention, the step of generating a plan report based on the verification result is:

[0077] Summarize the adjusted task scheduling plan and backup plan to form the final verification result R final :

[0078] R final ={S adjusted ,S backup},

[0079] Among them, R final is the final verification result set, S adjusted is the final task scheduling plan after adjustment, S backup Scheduling solutions for backup tasks;

[0080] Prepare a plan report based on the verification results, including: task allocation table, link performance data and alternative adjustment plans.

[0081] The beneficial effects of the present invention are:

[0082] This invention uses orbital dynamics simulation and deep learning models to predict orbital coverage and evaluate the resource status of ground stations, generate coverage windows and resource availability data, and generate optimized task scheduling plans through task priority sorting and load balancing models. While improving task execution efficiency, it balances ground station resources to avoid overload and resource waste. It uses a digital twin model to simulate task execution paths and link performance, and dynamically adjusts the scheduling plan based on simulation feedback to make the plan more in line with actual task requirements and improve the robustness and execution effect of the plan.

[0083] This invention relies on orbital dynamics modeling and deep learning technology to improve the accuracy of orbital coverage prediction; performs efficient task scheduling through intelligent optimization algorithms; and uses digital twin technology to ensure the reliability of link performance evaluation. Compared with traditional methods, it not only reduces the time for manual intervention and repeated adjustments, but also significantly improves the overall utilization rate of ground measurement and control station resources, providing a more efficient and robust solution for satellite measurement and control in complex multi-task scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0085] Figure 1 The figure is a schematic diagram of the framework of the satellite measurement and control subsystem full digital simulation platform of the present invention.

[0086] Figure 2 This is a flow chart of the simulation method of the satellite measurement and control subsystem full digital simulation platform of the present invention. DETAILED DESCRIPTION

[0087] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0088] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0089] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0090] Example 1, reference Figure 1 and Figure 2 This embodiment provides a fully digital simulation platform for a satellite tracking and control subsystem, including:

[0091] The initialization module is used to obtain measurement, operation and control data, including satellite orbit data, ground measurement and control station resource load information and mission requirements, and pre-process the measurement, operation and control data to convert it into standardized data;

[0092] The resource analysis module receives standardized data from the initialization module and uses orbital dynamics simulation combined with a deep learning model to predict satellite orbit coverage. It also evaluates the resource load status of the ground tracking and control station and generates analytical data, including coverage window time and resource availability.

[0093] The task allocation and scheduling module uses the analysis data of the resource analysis module and combines the importance of satellite missions, resource availability and coverage window time to dynamically generate task scheduling plans;

[0094] Methods for generating task scheduling solutions include:

[0095] Build a task priority table and sort the tasks according to their importance and coverage window time.

[0096] Establish a load balancing model to adjust the task allocation of ground tracking and control stations based on resource availability;

[0097] The simulation verification module receives the scheduling plan generated by the task allocation and scheduling module, and uses the digital twin model to simulate and adjust the link performance of the plan to generate the adjusted verification results;

[0098] The simulation verification module simulates the task execution path, adjusts resource configuration according to simulation feedback, and generates adjusted verification results;

[0099] The plan output module receives the verification results of the simulation verification module and generates a complete plan report;

[0100] The planning report includes the final task allocation table and alternative adjustment plans designed to deal with possible unexpected tasks or resource conflicts.

[0101] This embodiment also provides a simulation method for a satellite measurement and control subsystem all-digital simulation platform, including:

[0102] Step S1, extracting and standardizing satellite orbit data, ground tracking and control station resource load information and mission requirements to form basic data input;

[0103] Step S2: Based on the basic data input, the satellite orbit coverage is predicted through orbital dynamics simulation and deep learning models, the ground tracking and control station resource status is evaluated, and analytical data is generated, including coverage window time and resource availability;

[0104] The steps to predict satellite orbit coverage and evaluate ground tracking and control station resource status through orbital dynamics simulation and deep learning model are as follows:

[0105] The dynamic modeling of the track is carried out, and the model formula is:

[0106]

[0107] Among them, r(t) represents the satellite position vector at time t, r0 is the initial position vector, v(t) is the satellite velocity vector at time t, v0 is the initial velocity vector, and a total (τ) represents the total acceleration, including gravity and other external disturbances, and τ is the time-integrated variable;

[0108] Calculate the visibility between the ground station and the satellite. The calculation formula is:

[0109]

[0110] When θ≤θ max When , the ground station s is visible,

[0111] Among them, θ represents the angle between the satellite and the ground station, g s is the position vector of the ground station s, |r(t)| represents the modulus of the satellite position vector, |g s | represents the modulus of the ground station position vector, θ max is the visible angle threshold;

[0112] Perform time discretization processing, the discretization formula is:

[0113] If θ≤θ max , then C s,t =1, otherwise C s,t =0,

[0114] Among them, C s,t represents the coverage status of ground station s at time step t, t is the discretized time step, θ max is the visible angle threshold;

[0115] Perform deep learning model fusion, the fusion formula is:

[0116] yt =σ(W2·ReLU(W1·X t +b1)+b2),

[0117] Among them, y t Output results of the deep learning model at time step t, including coverage period and load status, X t represents the input features at time step t, including orbital parameters and ground station positions, W1 and W2 are the weight matrices of the first and second layers respectively, b1 and b2 are the bias vectors of the first and second layers respectively, ReLU(·) is the activation function with the value max(0,x), and σ(·) is the Sigmoid activation function;

[0118] Evaluate resource status. The evaluation formula is:

[0119]

[0120] Among them, R s represents the average load rate of ground station s, T is the total number of time steps, U s is the unit time resource usage of ground station s, C s,t is the coverage status of ground station s at time step t,

[0121] Output analysis data D analysis :

[0122] D analysis ={(T start ,t end ,R s )|s=1,2,…,S},

[0123] Among them, D analysis To analyze the data set, t start and t end Respectively represent the start time and end time of the coverage window, R s represents the resource load rate of ground station s, where S is the total number of ground stations;

[0124] Specifically, through orbital dynamics modeling and time discretization processing, the visibility relationship between the satellite orbit and the ground station is calculated, the coverage prediction results are optimized by combining deep learning models, and the resource status is analyzed through load rate evaluation to generate comprehensive coverage time windows and resource availability data.

[0125] Step S3: construct a task priority table, sort the tasks according to their importance and coverage window time, and adjust the ground tracking and control station task allocation based on resource availability through a load balancing model to output a task scheduling plan;

[0126] The steps to construct a task priority table and sort the tasks according to their importance and coverage window time are as follows:

[0127] Calculate the task priority using the following formula:

[0128]

[0129] Among them, P j represents the priority of task j, w1, w2, w3 are the weight coefficients of task importance, time window and resource requirements respectively, satisfying w1+w2+w3=1, I j Score the importance of task j, T j is the coverage time window length of task j, T max is the maximum time window of all tasks, R j is the resource requirement of task j, and the task list is arranged from high to low priority;

[0130] Construct a load balancing model. The model formula is:

[0131]

[0132] Among them, S is the total number of ground stations, M is the total number of missions, and A j,s is a binary variable indicating whether task j is assigned to ground station s, 1 indicates assigned, 0 indicates not assigned, C s is the total amount of resources of ground station s;

[0133] Adjust the ground tracking and control station task allocation based on resource availability and output the task scheduling plan as follows:

[0134] Define the task allocation adjustment rule as follows:

[0135] Then A j,s =1, otherwise A j,s =0,

[0136] Among them, A j,s It indicates the state of the adjusted task j assigned to the ground station s, threshold is the load balancing threshold, which indicates the upper limit of the resource utilization of the ground station;

[0137] Output task scheduling plan S schedule :

[0138] S schedule ={(j,s)|j=1,2,…,M; s=1,2,…,S},

[0139] Among them, S schedule is the final task scheduling plan, j is the task number, and s is the ground station number;

[0140] Specifically, through priority sorting and load balancing model optimization, balanced distribution is achieved between tasks and ground stations, ensuring the execution of high-priority tasks while balancing resource utilization.

[0141] Step S4: Use the digital twin model to simulate the link performance of the task scheduling plan, simulate the task execution path, and dynamically adjust the plan based on the feedback to generate the adjusted verification results;

[0142] The digital twin model is used to simulate the link performance of the task scheduling scheme. The steps of simulating the task execution path are as follows:

[0143] Model the link performance indicators and define the link performance score. The model formula is:

[0144]

[0145] Among them, L s represents the link performance score of ground station s, SNR s is the signal-to-noise ratio of ground station s, SNR threshold is the minimum signal-to-noise ratio requirement, B s is the link load of ground station s,

[0146] Combined with the task scheduling scheme, the digital twin model is used to dynamically simulate task execution. The model formula is:

[0147] F s =simulate(S schedule ,L s ,R s ),

[0148] Among them, F s is the simulation feedback data of ground station s, S schedule For the task scheduling scheme, define the distribution relationship between tasks and ground stations, L s R is the link performance score of ground station s. s is the resource load rate of ground station s, simulate(·) represents the simulation function, which simulates the execution of the task in the link. The output includes link delay, task completion rate and resource usage status;

[0149] Analyze the task execution path step by step and extract key performance indicators:

[0150]

[0151] Among them, T delay represents the average delay time of the link task, t represents the discrete time step, T is the total number of time steps, BW t,s is the bandwidth of ground station s at time step t, Loadt,s is the link load of ground station s at time step t;

[0152] According to the feedback dynamic adjustment plan, the steps to generate the adjusted verification results are as follows:

[0153] Generate simulation feedback results, integrate the simulation feedback data of each ground station, and form the task execution performance matrix M performance :

[0154] M performance ={(L s ,T delay ,R s )|s=1,2,…,S},

[0155] Among them, M performance represents the performance matrix, S is the total number of ground stations, L s is the link performance score of ground station s, T delay is the average delay time of ground station s, R s is the resource load rate of ground station s;

[0156] Adjust the task scheduling plan based on simulation feedback. The adjustment formula is:

[0157] S adjusted =S schedule +ΔS(M performance ),

[0158] Among them, S adjusted is the adjusted task scheduling scheme, ΔS(M performance ) is the adjustment optimization amount based on the performance matrix feedback;

[0159] Specifically, the link performance and resource status during task execution are evaluated through the digital twin model, and a feedback adjustment mechanism is used to ensure the reliability of link performance and the rationality of resource allocation, so that the scheduling plan meets the actual task requirements.

[0160] Step S5: Generate a plan report based on the verification results, the report content including a task allocation table and backup adjustment plans;

[0161] The steps to generate a plan report based on the verification results are:

[0162] Summarize the adjusted task scheduling plan and backup plan to form the final verification result R final :

[0163] R final ={S adjusted ,S backup},

[0164] Among them, R finalis the final verification result set, S adjusted is the final task scheduling plan after adjustment, S backup Scheduling solutions for backup tasks;

[0165] Prepare a plan report based on the verification results, including: task allocation table, link performance data and backup adjustment plan;

[0166] Specifically, the final plan report integrates verification results and simulation feedback to provide a primary and backup implementation plan for mission execution, ensuring the completion of core tasks while designing response strategies for emergencies.

[0167] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A fully digital simulation platform for satellite measurement and control subsystem, characterized by: include, The initialization module is used to obtain measurement, operation and control data, including satellite orbit data, ground measurement and control station resource load information and mission requirements, and pre-process the measurement, operation and control data to convert it into standardized data; The resource analysis module receives standardized data from the initialization module and uses orbital dynamics simulation combined with a deep learning model to predict satellite orbit coverage. It also evaluates the resource load status of the ground tracking and control station and generates analytical data, including coverage window time and resource availability. The task allocation and scheduling module uses the analysis data of the resource analysis module and combines the importance of satellite missions, resource availability and coverage window time to dynamically generate task scheduling plans; The simulation verification module receives the scheduling plan generated by the task allocation and scheduling module, and uses the digital twin model to simulate and adjust the link performance of the plan to generate the adjusted verification results; The plan output module receives the verification results of the simulation verification module and generates a complete plan report.

2. A satellite measurement and control subsystem full digital simulation platform according to claim 1, characterized in that: The method for generating a task scheduling solution includes: Build a task priority table and sort the tasks according to their importance and coverage window time. A load balancing model is established to adjust the task allocation of ground tracking and control stations based on resource availability.

3. A satellite measurement and control subsystem full digital simulation platform as claimed in claim 2, characterized in that: The simulation verification module simulates the task execution path, adjusts the resource configuration according to the simulation feedback, and generates the adjusted verification result; The planning report includes the final task allocation table and alternative adjustment plans designed to address possible unexpected tasks or resource conflicts.

4. A satellite measurement and control subsystem full digital simulation platform as claimed in claim 3, characterized in that: The simulation steps of the satellite measurement and control subsystem full digital simulation platform include: Step S1, extracting and standardizing satellite orbit data, ground tracking and control station resource load information and mission requirements to form basic data input; Step S2: Based on the basic data input, the satellite orbit coverage is predicted through orbital dynamics simulation and deep learning models, the ground tracking and control station resource status is evaluated, and analytical data is generated, including coverage window time and resource availability; Step S3: construct a task priority table, sort the tasks according to their importance and coverage window time, and adjust the ground tracking and control station task allocation based on resource availability through a load balancing model to output a task scheduling plan; Step S4: Use the digital twin model to simulate the link performance of the task scheduling plan, simulate the task execution path, and dynamically adjust the plan based on the feedback to generate the adjusted verification results; Step S5: Generate a plan report based on the verification result, the report content including a task allocation table and a backup adjustment plan.

5. A satellite measurement and control subsystem full digital simulation platform as claimed in claim 4, characterized in that: The steps of predicting satellite orbit coverage and evaluating ground tracking and control station resource status through orbital dynamics simulation and deep learning model are as follows: The dynamic modeling of the track is carried out, and the model formula is: Among them, r(t) represents the satellite position vector at time t, r0 is the initial position vector, v(t) is the satellite velocity vector at time t, v0 is the initial velocity vector, and a total (τ) represents the total acceleration, including gravity and other external disturbances, and τ is the time-integrated variable; Calculate the visibility between the ground station and the satellite. The calculation formula is: When θ≤θ max When , the ground station s is visible, Among them, θ represents the angle between the satellite and the ground station, g s is the position vector of the ground station s, |r(t)| represents the modulus of the satellite position vector, |g s | represents the modulus of the ground station position vector, θ max is the visible angle threshold; Perform time discretization processing, the discretization formula is: If θ≤θ max , then C s，t =1, otherwise C s，t =0, Among them, C s，t represents the coverage status of ground station s at time step t, t is the discretized time step, θ max is the visible angle threshold; Perform deep learning model fusion, the fusion formula is: y t =σ(W2·ReLU(W1·X t +b1)+b2), Among them, y t Output results of the deep learning model at time step t, including coverage period and load status, X t represents the input features at time step t, including orbital parameters and ground station positions, W1 and W2 are the weight matrices of the first and second layers, b1 and b2 are the bias vectors of the first and second layers, ReLU(·) is the activation function, and its value is max(0, x), and σ(·) is the Sigmoid activation function; Evaluate resource status. The evaluation formula is: Among them, R s represents the average load rate of ground station s, T is the total number of time steps, U s is the unit time resource usage of ground station s, C s，t is the coverage status of ground station s at time step t, Output analysis data D analysis : D analysis ={(t start ,t end ,R s )|s=1,2,...,S}, Among them, D analysis To analyze the data set, t start and t end Respectively represent the start time and end time of the coverage window, R s It represents the resource load rate of ground station s, and S is the total number of ground stations.

6. A satellite measurement and control subsystem full digital simulation platform according to claim 5, characterized in that: The steps of constructing a task priority table and sorting tasks according to their importance and coverage window time are as follows: Calculate the task priority using the following formula: Among them, P j Indicates a task j priority, w1, w2, w3 are the weight coefficients of task importance, time window and resource requirements, respectively, satisfying w1+w2+w3=1, I j Score the importance of task j, T j is the coverage time window length of task j, T max is the maximum time window of all tasks, R j is the resource requirement of task j, and the task list is arranged from high to low priority; Construct a load balancing model. The model formula is: Among them, S is the total number of ground stations, M is the total number of missions, and A j，s is a binary variable indicating whether task j is assigned to ground station s, 1 indicates assigned, 0 indicates not assigned, C s is the total resources of ground station s.

7. A satellite measurement and control subsystem full digital simulation platform according to claim 6, characterized in that: The step of adjusting the ground control station task allocation based on resource availability and outputting the task scheduling plan is as follows: Define the task allocation adjustment rule as follows: Then A j，s =1, otherwise A j，s =0, Among them, A j，s It indicates the state of the adjusted task j assigned to the ground station s, threshold is the load balancing threshold, which indicates the upper limit of the resource utilization of the ground station; Output task scheduling plan S schedule : S schedule ={(j,s)|j=1,2,...,M;s=1,2,...,S}, Among them, S schedule is the final task scheduling plan, j is the task number, and s is the ground station number.

8. A satellite measurement and control subsystem full digital simulation platform according to claim 7, characterized in that: The steps of using the digital twin model to simulate the link performance of the task scheduling scheme and simulating the task execution path are as follows: Model the link performance indicators and define the link performance score. The model formula is: Among them, L s represents the link performance score of ground station s, SNR s is the signal-to-noise ratio of ground station s, SNR threshold is the minimum signal-to-noise ratio requirement, B s is the link load of ground station s, Combined with the task scheduling scheme, the digital twin model is used to dynamically simulate task execution. The model formula is: F s =simulate(S schedule ,L s ,R s ), Among them, F s is the simulation feedback data of ground station s, S schedule For the task scheduling scheme, define the distribution relationship between tasks and ground stations, L s R is the link performance score of ground station s. s is the resource load rate of ground station s, simulate(·) represents the simulation function, which simulates the execution of the task in the link. The output includes link delay, task completion rate and resource usage status; Analyze the task execution path step by step and extract key performance indicators: Among them, T delay represents the average delay time of the link task, t represents the discrete time step, T is the total number of time steps, BW t，s is the bandwidth of ground station s at time step t, Load t，s is the link load of ground station s at time step t.

9. A satellite measurement and control subsystem full digital simulation platform according to claim 8, characterized in that: The step of generating the adjusted verification result according to the feedback dynamic adjustment scheme is: Generate simulation feedback results, integrate the simulation feedback data of each ground station, and form the task execution performance matrix M performance : M performance ={(L s ,T delay ,R s )|s=1,2,...,S}, Among them, M performance represents the performance matrix, S is the total number of ground stations, L s is the link performance score of ground station s, T delay is the average delay time of ground station s, R s is the resource load rate of ground station s; Adjust the task scheduling plan based on simulation feedback. The adjustment formula is: S adjusted =S schedule +ΔS(M performance ), Among them, S adjusted is the adjusted task scheduling scheme, ΔS(M performance ) is the adjustment optimization amount based on the performance matrix feedback.

10. A satellite measurement and control subsystem full digital simulation platform according to claim 9, characterized in that: The steps of generating a plan report based on the verification results are: Summarize the adjusted task scheduling plan and backup plan to form the final verification result R final : R final ={S adjusted ,S backup }, Among them, R final is the final verification result set, S adiusted is the final task scheduling plan after adjustment, S backup Scheduling solutions for backup tasks; Prepare a plan report based on the verification results, including: task allocation table, link performance data and alternative adjustment plans.

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