Simulation Evaluation Method and Device for Multi-Satellite Cooperative Missions

Abstract

The embodiments of the present application provide a simulation evaluation method and device for multi-satellite collaborative tasks. By constructing a unified space-time reference based on standard time signals, a complete environmental disturbance model including solar radiation pressure, atmospheric drag, and geomagnetic field interference is established. An innovative fault injection mechanism for payload, attitude, and orbit control is designed to achieve real simulation of multi-satellite collaborative motion. The system establishes a multi-level evaluation index system at the system level, subsystem level, and component level, adopts a dynamic weight calculation method based on the task execution status, and combines the matching analysis of three-dimensional error curves and key event timings to achieve accurate evaluation of the performance of multi-satellite collaborative tasks, providing a reliable decision-making basis for space mission planning and system optimization.

Description

Technical Field

[0001] This application relates to the field of data processing, and particularly to a simulation evaluation method and device for multi-satellite collaborative missions. Background Art

[0002] The existing simulation evaluation technologies for multi-satellite collaborative missions have obvious deficiencies. Traditional systems usually adopt simplified dynamic models and fail to fully consider the comprehensive influence of environmental disturbance factors such as solar radiation pressure, atmospheric drag, and geomagnetic field, resulting in a large deviation between the simulation results and the actual operating state. At the same time, the existing systems lack a perfect fault simulation mechanism and it is difficult to effectively verify the collaborative performance of multi-satellite systems under various fault conditions.

[0003] In addition, the existing technologies also have limitations in the construction of the evaluation index system and data analysis. Most systems adopt a single-level evaluation index and fail to establish a multi-level evaluation system at the system level, subsystem level, and component level, making it difficult to comprehensively reflect the execution effect of multi-satellite collaborative missions. The visualization and correlation analysis capabilities of the evaluation results are insufficient, and there is a lack of in-depth exploration of key events and performance inflection points.

[0004] The existing systems have technical bottlenecks in telemetry data processing and time alignment. Lack of a unified spatio-temporal reference framework, it is difficult to achieve precise synchronization and comparative analysis of multi-source telemetry data. Solving these problems is of great significance for improving the accuracy and practicality of the simulation evaluation of multi-satellite collaborative missions. Summary of the Invention

[0005] Aiming at the problems in the existing technology, this application provides a simulation evaluation method and device for multi-satellite collaborative missions, which can achieve precise evaluation of the performance of multi-satellite collaborative missions and provide a reliable decision-making basis for space mission planning and system optimization.

[0006] To solve at least one of the above problems, this application provides the following technical solutions:

[0007] In a first aspect, this application provides a simulation evaluation method for multi-satellite collaborative missions, including:

[0008] The simulation industrial control computer receives the standard time signal sent by the second pulse controller and constructs a unified space-time reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite cooperative mission dynamics model and uses the unified space-time reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance model; the simulation industrial control computer generates multi-satellite cooperative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite cooperative motion simulation data;

[0009] The simulation industrial control computer collects the telemetry data of each satellite system to be measured through a bus interface. The telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters; the telemetry data is time-aligned according to the unified space-time reference to generate data to be evaluated; the simulation industrial control computer writes the simulation data and the data to be evaluated into an evaluation database;

[0010] The simulation industrial control computer reads the simulation data and the data to be evaluated from the evaluation database, establishes a multi-satellite cooperative mission evaluation index system. The evaluation index system includes system-level indicators, subsystem-level indicators, and component-level indicators; the simulation industrial control computer calculates the weight coefficients of each level of indicators based on the execution status of the cooperative mission; data analysis is performed on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients to generate a three-dimensional error curve graph and a critical event time series graph; the simulation industrial control computer extracts the inflection point information in the three-dimensional error curve graph and matches the inflection point information with the critical event time series graph to generate an evaluation result report.

[0011] Further, the simulation industrial control computer receives the standard time signal sent by the second pulse controller and constructs a unified space-time reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite cooperative mission dynamics model and uses the unified space-time reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance model, including:

[0012] The simulation industrial control computer receives the standard time signal sent by the second pulse controller, reads the internal timing signal of the timer, compares the phase of the standard time signal with the internal timing signal, calculates the time synchronization error, calibrates the frequency and phase of the internal timing signal based on the time synchronization error, generates a reference clock signal, maps the reference clock signal to the Coordinated Universal Time scale, and constructs a unified space-time reference;

[0013] The simulation industrial control computer establishes a multi-satellite cooperative mission dynamics model according to Newton's mechanical equations, uses the unified space-time reference as the time parameter of the dynamics model, calculates the six orbital elements and Euler angle parameters of each satellite, inputs the solar radiation pressure value, atmospheric drag coefficient, and geomagnetic field strength value into the dynamics model, constructs an environmental disturbance term matrix, and superimposes the environmental disturbance term matrix onto the motion equation of the dynamics model.

[0014] Furthermore, the simulation industrial control computer generates multi-satellite cooperative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite cooperative motion simulation data, including:

[0015] The simulation industrial control computer inputs the dynamics model and the environmental disturbance model into the Runge-Kutta numerical integrator, sets the integration step size based on the unified space-time reference, calculates the changes in the orbital elements and attitude angles of each satellite, substitutes the changes into the orbit prediction model, calculates the position vectors, velocity vectors, and attitude vectors of each satellite during the cooperative motion process, and generates multi-satellite cooperative motion simulation data;

[0016] The simulation industrial control computer uses a Markov chain to establish a payload switch fault model, an attitude sensor drift fault model, and an orbit control engine start-stop fault model, calculates the state transition probability matrix of each fault model, generates a fault state sequence based on the state transition probability matrix, and writes the fault parameters in the fault state sequence to the corresponding time points of the multi-satellite cooperative motion simulation data.

[0017] Furthermore, the simulation industrial control computer collects the telemetry data of each satellite system to be measured through a bus interface, and the telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters, including:

[0018] The simulation industrial control computer reads the 1553B bus communication protocol stack, configures the message block descriptor of the bus controller, sets the data length, sampling period, and buffer address of the message block, sends a data acquisition instruction to each satellite system to be measured, reads the data frames of each sub-address on the bus, and parses the six orbital elements, Euler angles, angular velocities, engine switch quantities, sensor output values, and payload working states in the data frames;

[0019] The simulation industrial control computer detects the data integrity according to the synchronization word and check code in the data frame, extracts the time tag information in the data frame, maps the time tag information to the unified space-time reference, generates a time series of the telemetry data, and reconstructs the six orbital elements, Euler angles, angular velocities, engine switch quantities, sensor output values, and payload working states into a telemetry data stream according to the time series.

[0020] Further, the step of aligning the telemetry data in time according to the unified spatio-temporal reference to generate data to be evaluated; the simulation industrial control computer writing the simulation data and the data to be evaluated into the evaluation database includes:

[0021] The simulation industrial control computer reads the time tag information in the telemetry data stream, calculates the time interval between adjacent data points, performs cubic spline interpolation on the unequally spaced telemetry data, resamples the interpolated data into an equally spaced sequence, performs linear fitting on the resampled data based on the unified spatio-temporal reference, calculates the time deviation compensation amount, and superimposes the compensation amount on the time tag of the resampled data to generate time-aligned data to be evaluated;

[0022] The simulation industrial control computer constructs the relational database table structure, indexes the simulation data and the data to be evaluated according to the satellite number, data type and time tag, calculates the statistical characteristic values of the data, writes the statistical characteristic values as data quality marks into the database table, establishes a database transaction, and writes the simulation data and the data to be evaluated into the evaluation database in a batch processing manner.

[0023] Further, the simulation industrial control computer reads the simulation data and the data to be evaluated from the evaluation database and establishes a multi-satellite collaborative task evaluation index system. The evaluation index system includes system-level indexes, subsystem-level indexes and component-level indexes, including:

[0024] The simulation industrial control computer executes a database query instruction, constructs a time window, reads the simulation data and the data to be evaluated from the evaluation database based on the time window, groups the simulation data and the data to be evaluated according to the satellite number and data type, calculates the mean and standard deviation of each group of data, and establishes a data preprocessing matrix;

[0025] The simulation industrial control computer constructs a hierarchical evaluation index system based on the data preprocessing matrix, sets formation keeping accuracy, task completion rate, and resource utilization rate indexes at the system level, sets orbit control accuracy, attitude stability, and payload working efficiency indexes at the subsystem level, and sets engine working status, sensor measurement error, and data transmission quality indexes at the component level, and writes the calculation formulas and threshold ranges of each level of indexes into the index library.

[0026] Further, the simulation industrial control computer calculates the weight coefficients of each level of indexes based on the collaborative task execution status; performs data analysis on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients to generate a three-dimensional error curve graph and a key event time sequence graph; the simulation industrial control computer extracts the inflection point information in the three-dimensional error curve graph, matches the inflection point information with the key event time sequence graph, and generates an evaluation result report, including:

[0027] The simulation industrial control computer reads the execution status data of the collaborative task, establishes an analytic hierarchy process model for the system-level indicators, subsystem-level indicators, and component-level indicators, calculates the eigenvalues and eigenvectors of the judgment matrix, conducts a consistency test, calculates the weight coefficients of each level of indicators based on the eigenvector corresponding to the maximum eigenvalue, and combines the weight coefficients with the calculation formulas in the indicator library to form an evaluation model;

[0028] The simulation industrial control computer applies the evaluation model to the simulation data and the data to be evaluated, calculates the three-dimensional position error and attitude error at each moment, draws an error curve graph, extracts the inflection point coordinates in the curve, reads the key event information corresponding to the inflection point moment, establishes a mapping relationship between the error and the event, and generates an evaluation result report including indicator scores, error analysis, and key events.

[0029] In a second aspect, the present application provides a simulation evaluation device for a multi-satellite collaborative task, including:

[0030] A simulation model construction module, configured to enable the simulation industrial control computer to receive a standard time signal sent by a second pulse controller, and construct a unified space-time reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite collaborative task dynamics model, and uses the unified space-time reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance model; the simulation industrial control computer generates multi-satellite collaborative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite collaborative motion simulation data;

[0031] A data processing module, configured to enable the simulation industrial control computer to collect telemetry data of each satellite system to be measured through a bus interface, where the telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters; align the telemetry data in time according to the unified space-time reference to generate data to be evaluated; the simulation industrial control computer writes the simulation data and the data to be evaluated into an evaluation database;

[0032] The task index evaluation module is used for the simulation industrial control computer to read the simulation data and the data to be evaluated from the evaluation database, establish a multi-satellite collaborative task evaluation index system, and the evaluation index system includes system-level indexes, subsystem-level indexes, and component-level indexes; the simulation industrial control computer calculates the weight coefficients of each level of indexes based on the collaborative task execution status; performs data analysis on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients, and generates a three-dimensional error curve graph and a key event time sequence graph; the simulation industrial control computer extracts the inflection point information in the three-dimensional error curve graph, matches the inflection point information with the key event time sequence graph, and generates an evaluation result report.

[0033] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps of the simulation evaluation method for multi-satellite collaborative tasks are implemented.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the simulation evaluation method for multi-satellite collaborative tasks are implemented.

[0035] In a fifth aspect, the present application provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the simulation evaluation method for multi-satellite collaborative tasks are implemented.

[0036] As can be seen from the above technical solutions, the present application provides a simulation evaluation method and device for multi-satellite collaborative tasks. By constructing a unified spatio-temporal reference based on a standard time signal, a complete environmental perturbation model including solar radiation pressure, atmospheric drag, and geomagnetic field interference is established. An innovative fault injection mechanism for payload, attitude, and orbit control is designed to achieve real simulation of multi-satellite collaborative motion. The system establishes a multi-level evaluation index system at the system level, subsystem level, and component level, adopts a dynamic weight calculation method based on task execution status, and combines the matching analysis of the three-dimensional error curve and the key event time sequence to achieve accurate evaluation of the performance of multi-satellite collaborative tasks, providing a reliable decision-making basis for space mission planning and system optimization. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1It is one of the flow schematic diagrams of the simulation evaluation method for the multi-satellite collaborative mission in the embodiments of the present application;

[0039] Figure 2 It is the second of the flow schematic diagrams of the simulation evaluation method for the multi-satellite collaborative mission in the embodiments of the present application;

[0040] Figure 3 It is the third of the flow schematic diagrams of the simulation evaluation method for the multi-satellite collaborative mission in the embodiments of the present application;

[0041] Figure 4 It is the fourth of the flow schematic diagrams of the simulation evaluation method for the multi-satellite collaborative mission in the embodiments of the present application;

[0042] Figure 5 It is the fifth of the flow schematic diagrams of the simulation evaluation method for the multi-satellite collaborative mission in the embodiments of the present application;

[0043] Figure 6 It is the sixth of the flow schematic diagrams of the simulation evaluation method for the multi-satellite collaborative mission in the embodiments of the present application;

[0044] Figure 7 It is the seventh of the flow schematic diagrams of the simulation evaluation method for the multi-satellite collaborative mission in the embodiments of the present application;

[0045] Figure 8 It is the structural diagram of the simulation evaluation device for the multi-satellite collaborative mission in the embodiments of the present application;

[0046] Figure 9 It is the structural schematic diagram of the electronic device in the embodiments of the present application.

[0047] Reference numerals:

[0048] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage section 9142, data storage section 9143, driver program storage section 9144, antenna 9111, speaker 9131, microphone 9132. Detailed implementation manners

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0050] In the technical solution of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0051] Considering the problems existing in the prior art, this application provides a simulation evaluation method and device for multi-satellite cooperative tasks. By constructing a unified spatio-temporal reference based on the standard time signal, a complete environmental perturbation model including solar radiation pressure, atmospheric drag, and geomagnetic field interference is established. An innovative fault injection mechanism for payload, attitude, and orbit control is designed to achieve a realistic simulation of multi-satellite cooperative motion. The system establishes a multi-level evaluation index system at the system level, subsystem level, and component level, adopts a dynamic weight calculation method based on the task execution status, and combines the matching analysis of the three-dimensional error curve and the key event time sequence to achieve an accurate evaluation of the performance of multi-satellite cooperative tasks, providing a reliable decision-making basis for space mission planning and system optimization.

[0052] In order to accurately evaluate the performance of multi-satellite cooperative tasks and provide a reliable decision-making basis for space mission planning and system optimization, this application provides an embodiment of a simulation evaluation method for multi-satellite cooperative tasks. Refer to Figure 1 , the simulation evaluation method for multi-satellite cooperative tasks specifically includes the following content:

[0053] Step S101: The simulation industrial control computer receives the standard time signal sent by the second pulse controller, constructs a unified spatio-temporal reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite cooperative task dynamics model, and uses the unified spatio-temporal reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental perturbation model; the simulation industrial control computer generates multi-satellite cooperative motion simulation data based on the dynamics model and the environmental perturbation model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite cooperative motion simulation data;

[0054] Optionally, in this embodiment, a dual-channel signal isolation and conditioning architecture is adopted when receiving the second pulse standard time signal. The main channel signal is electrically isolated by the high-speed optocoupler MOC8102, and the secondary channel signal uses the digital isolator ADuM1401 for backup. The signal conditioning circuit uses the operational amplifier AD8065 to build a band-pass filter with cut-off frequencies designed to be 10Hz and 100kHz to achieve noise suppression of the second pulse signal. Signal sampling uses the high-speed comparator LT1719, and the trigger level is dynamically adjusted by a precision DAC to ensure stable trigger characteristics when the signal amplitude fluctuates. To improve the sampling accuracy, an FPGA is used to implement an oversampling clock of 100MHz, and digital noise reduction processing is performed through a moving average algorithm.

[0055] This embodiment realizes a highly stable spatio-temporal reference construction mechanism. The reference clock uses an oven-controlled crystal oscillator (OCXO), whose temperature stability is better than 0.1 PPB / °C. The phase-locked loop circuit uses the ADF4355 as the main phase-locked loop chip, and cooperates with a fractional divider to achieve precise adjustment of the local clock. The phase detector uses a phase-frequency detector, and realizes fast locking and low jitter through an adaptive loop bandwidth algorithm. Temperature compensation uses a third-order polynomial fitting, and the compensation coefficient is dynamically updated through real-time temperature monitoring. The time reference mapping uses a two-way time comparison method, and eliminates the influence of transmission delay through iterative calculation. The reference signal distribution uses a fan-out buffer to ensure the phase consistency of multiple outputs.

[0056] This embodiment designs a complete dynamic modeling framework. The satellite motion equation uses the Newton-Euler equations to couple the translational and rotational motions for calculation. The state vector includes the position vector r, velocity vector v, attitude quaternion q, and angular velocity vector ω in the inertial coordinate system. In the motion equation, the gravitational term uses the EGM2008 gravity field model, expanded to the 15th order; the atmospheric drag term considers the influence of solar activity and geomagnetic disturbances, and the drag coefficient is dynamically updated by calculating the Reynolds number; the solar radiation pressure calculation considers the material properties of each surface element on the satellite surface, and calculates the photon reflection and re-radiation effects through the Monte Carlo method; the geomagnetic torque is calculated by the dipole model, considering the influence of the residual magnetic moment. The model supports multi-satellite collaborative simulation, and realizes inter-satellite information interaction through a communication delay model.

[0057] This embodiment realizes high-precision environmental disturbance modeling. When calculating the solar radiation pressure, the satellite surface is divided into multiple surface elements, and the optical properties of each surface element are described by the bidirectional reflectance distribution function. Three basic processes of specular reflection, diffuse reflection, and absorption are considered, and the reflection coefficient decays dynamically with the service time of the material. The atmospheric drag model uses the NRLMSISE-00 standard, and the input parameters include the heliospheric space weather index F10.7, the geomagnetic index Ap, and the geographical location parameters. The model considers the daily, seasonal, and solar cycle variations of the atmospheric density, and realizes density field reconstruction through three-dimensional interpolation. The geomagnetic field model uses the IGRF13 coefficient set, realizes the spherical harmonic expansion, and uses a recursive algorithm in the calculation process to improve efficiency.

[0058] This embodiment adopts a multi-step adaptive numerical integration strategy. The 8th-order Runge-Kutta method is used to solve the motion equation, and the local truncation error is calculated through the embedded 5th-order solution. The step size control uses a PI controller to dynamically adjust the step size according to the error estimate value. The integrator supports parallel calculation, divides the state vector into blocks for processing, and realizes multi-thread acceleration through OpenMP. For the strongly nonlinear region, it automatically switches to the symplectic integrator GGL4 to maintain the energy and angular momentum conservation characteristics of the system.

[0059] In this embodiment, a comprehensive fault model injection mechanism is constructed. The payload fault model includes three categories: switch faults, performance degradation, and data anomalies. Each type of fault describes the state transition process through a Markov chain. The transition probability matrix is obtained through training with historical data, taking into account the temporal correlation of faults. Attitude control faults include typical fault modes such as gyro drift, star sensor failure, and momentum wheel jamming. Gyro drift adopts a random walk model, and the drift rate is related to temperature changes. Star sensor faults include three modes: increased random noise, offset mutation, and output truncation. Momentum wheel faults are described by changes in frictional torque and output torque saturation characteristics. The orbit control fault model includes characteristics such as thruster start-up delay, turn-off overshoot, and thrust decay. Fault parameters are generated through the Monte Carlo method. Various fault models are injected into the simulation data stream in an event-driven manner, realizing the complete process simulation of fault occurrence, development, and recovery.

[0060] In this embodiment, a high-fidelity multi-satellite collaborative simulation system is established through accurate modeling and reliable fault injection. On the basis of ensuring the time synchronization accuracy, the simulation fidelity is improved through multi-level optimization. The overall design fully considers the complexity of the space environment and realizes the accurate description of the characteristics of the actual system. Through a complete dynamic model and accurate fault injection, a reliable simulation platform is provided for the verification of multi-satellite cooperative control algorithms, supporting the development and testing of key technologies such as fault diagnosis and fault-tolerant control.

[0061] Step S102: The simulation industrial computer collects the telemetry data of each satellite system to be tested through a bus interface. The telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters; the telemetry data is time-aligned according to the unified spatio-temporal reference to generate data to be evaluated; the simulation industrial computer writes the simulation data and the data to be evaluated into the evaluation database;

[0062] Optionally, in this embodiment, a dual-card redundant structure is adopted during bus interface acquisition. The main acquisition card uses an AceXtreme series PCIe interface 1553B card, and the backup acquisition card uses a Mini-PCIe interface card. The two acquisition cards realize data interaction through an optical fiber backplane and support hot backup switching. The data acquisition process adopts a polling method to request data from each remote terminal on the 1553B bus according to a preset time sequence. The acquisition card is configured with a dual-buffer structure to realize seamless data acquisition. When the terminal response times out, a retransmission mechanism is used to ensure data integrity.

[0063] This embodiment designs an accurate data parsing framework. The telemetry data frame adopts a hierarchical parsing structure. First, the frame is delimited by the frame synchronization word, and then the time code, sequence number, and data length fields in the frame header are parsed. The data area parsing uses a look-up table method to extract parameters such as six orbital elements, attitude quaternion, angular velocity vector, engine working state, torque wheel speed, sun sensor output, star sensor quaternion, GPS position and velocity, and payload working mode according to the data identification code. The parameter parsing process includes range conversion and engineering value conversion to ensure the correct physical meaning of the data.

[0064] This embodiment implements a reliable data integrity verification mechanism. The verification process adopts a multi-verification strategy, including frame synchronization check, CRC check, parameter validity check, and timing continuity check. The parameter validity check is achieved through threshold judgment, and the threshold range is dynamically adjusted according to the satellite working mode. The timing continuity check is achieved by comparing the frame count and time code. When a dropped frame is found, data repair is performed through interpolation.

[0065] This embodiment constructs an innovative time alignment algorithm. First, the time tags in the telemetry data are extracted and converted into time stamps under a unified space-time reference. The time conversion considers the leap second relationship between GPS time and UTC time and achieves accurate conversion through a look-up table method. The data resampling uses the cubic spline interpolation method, and the interpolation nodes are adaptively selected through curvature calculation. The resampled data is smoothed through a sliding window, and the window length is dynamically adjusted according to the data change rate.

[0066] This embodiment designs an efficient data synchronization mechanism. The synchronization process first establishes a relative time series based on a reference satellite and calculates the time deviation of other satellite data relative to the reference series. The time deviation compensation uses a linear fitting method, and the fitting coefficient is calculated through the least squares method. The synchronized effect of the compensated data is verified through cross-correlation analysis, and the correlation coefficient is used as the synchronization quality index.

[0067] This embodiment implements a reliable database writing strategy. The database uses a PostgreSQL time series database, and the table structure design includes fields such as time stamp index, satellite identification, data type, data value, and quality mark. The data writing adopts a batch processing mode and realizes efficient writing through a data buffer. The transaction processing adopts a two-phase commit protocol to ensure data consistency. The index optimization uses a B-tree structure to improve the query efficiency.

[0068] This embodiment constructs a complete data backup mechanism. The real-time data is recorded in a log manner, and incremental backups are performed regularly. The backup strategy is classified according to the importance of the data, and critical data is stored in multiple copies. The data recovery adopts a rollback mechanism to support data recovery at a specified time point. The storage space management adopts a cyclic overwrite strategy to automatically clean up expired data.

[0069] In this embodiment, an efficient telemetry data processing system is established through precise data acquisition and reliable storage management. While ensuring data integrity, this solution improves processing efficiency through multi-level optimization. The overall design fully considers the characteristics of space telemetry data and realizes the full-process management of data acquisition, parsing, alignment, and storage. Through a complete data processing link and a reliable storage mechanism, high-quality data support is provided for subsequent evaluation and analysis, serving the performance evaluation and fault diagnosis of multi-satellite collaborative missions.

[0070] Step S103: The simulation industrial control computer reads the simulation data and the data to be evaluated from the evaluation database, establishes a multi-satellite collaborative mission evaluation index system, and the evaluation index system includes system-level indicators, subsystem-level indicators, and component-level indicators; the simulation industrial control computer calculates the weight coefficients of each level of indicators based on the execution status of the collaborative mission; performs data analysis on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients to generate a three-dimensional error curve graph and a key event time sequence graph; the simulation industrial control computer extracts the inflection point information in the three-dimensional error curve graph, matches the inflection point information with the key event time sequence graph, and generates an evaluation result report.

[0071] Optionally, in this embodiment, a distributed query engine is used for data reading. During the query process, a time window index is first constructed, and the query task is decomposed into multiple sub-queries through a parallel query optimizer. The sub-queries adopt a vectorized execution method and accelerate data processing through the SIMD instruction set. The data cache adopts a multi-level structure, where the L1 cache stores hot data and the L2 cache stores prefetch data to reduce database access latency.

[0072] This embodiment designs a hierarchical evaluation index system. System-level indicators include formation geometric configuration maintenance accuracy, task completion timeliness rate, system resource utilization efficiency, etc. Among them, the formation configuration is described by relative position and relative attitude, task completion is verified through a key event sequence, and resource utilization is comprehensively evaluated through indicators such as energy, storage, and bandwidth. Subsystem-level indicators are further divided into orbit control accuracy, attitude stability, payload working efficiency, etc. Orbit control accuracy is characterized by a position error ellipsoid, attitude stability is statistically analyzed using Euler angle deviations, and payload efficiency is evaluated through working time sequences and data quality. Component-level indicators include specific parameters such as the working characteristics of the propulsion system, the measurement accuracy of sensors, and the data transmission quality.

[0073] This embodiment realizes an adaptive weight calculation mechanism. The weight calculation adopts an improved analytic hierarchy process. First, a judgment matrix is established, and the matrix elements are dynamically updated through the task status. Expert experience knowledge is introduced through a fuzzy rule base to achieve the integration of experience and data. The consistency test adopts the eigenvalue method, and the consistency ratio is improved through iterative optimization. The weight normalization adopts the geometric mean method to reduce the influence of extreme values.

[0074] This embodiment constructs an innovative data analysis framework. The analysis process first performs data preprocessing, including outlier detection, trend decomposition, and feature extraction. The outlier detection adopts the local outlier factor algorithm, considering the spatio-temporal correlation of the data. The trend decomposition adopts the empirical mode decomposition method to extract the multi-scale features of the data. The feature extraction is achieved through wavelet transform, and the most representative wavelet coefficients are selected.

[0075] This embodiment designs an accurate error analysis method. The three-dimensional error curve is represented parametrically and smoothed through B-spline curve fitting. The inflection point extraction adopts the curvature extreme value method, and the robustness of inflection point detection is improved through multi-scale analysis. The curve matching adopts the dynamic time warping algorithm to handle the non-linear stretching of time series data.

[0076] This embodiment realizes intelligent event correlation analysis. The key event extraction adopts the sequence pattern mining method, and frequent patterns are discovered through sliding window scanning. The event correlation analysis adopts a causal inference network, and the network structure is obtained through learning historical data. The time series matching adopts the graph matching algorithm to judge the similarity of event sequences through subgraph isomorphism.

[0077] This embodiment constructs a comprehensive evaluation report generation mechanism. The report generation adopts a templated design, and the content structure is dynamically adjusted according to the evaluation objective. The data visualization adopts an adaptive layout, supporting the interactive display of multi-dimensional data. The text description adopts natural language generation technology to generate a readable evaluation conclusion through template filling and grammar rules.

[0078] This embodiment establishes an intelligent multi-satellite cooperative task evaluation system through in-depth data analysis and a systematic evaluation system. While ensuring the comprehensiveness of the evaluation, this solution improves the analysis efficiency through multi-level optimization. The overall design fully considers the complexity of space missions and realizes a complete evaluation link from data to conclusion. Through accurate error analysis and intelligent event correlation, it provides a reliable basis for performance evaluation and optimization of multi-satellite cooperative tasks, supporting mission planning and decision-making optimization.

[0079] The evaluation system of this embodiment has good scalability and can dynamically adjust the index structure and weight allocation according to task requirements. Through intelligent data analysis methods, in-depth understanding of the behavior of complex systems is achieved, providing strong support for the continuous optimization of multi-satellite systems. The implementation of this solution significantly improves the accuracy and efficiency of evaluation, providing an important reference for the performance improvement and fault prevention of space missions.

[0080] As can be seen from the above description, the simulation evaluation method for multi-satellite cooperative tasks provided by the embodiments of this application can construct a unified spatio-temporal reference based on the standard time signal, establish a complete environmental disturbance model including solar radiation pressure, atmospheric drag, and geomagnetic field interference. An innovative fault injection mechanism for payload, attitude, and orbit control is designed to achieve real simulation of multi-satellite cooperative motion. The system establishes a multi-level evaluation index system at the system level, subsystem level, and component level, adopts a dynamic weight calculation method based on the task execution status, and combines the matching analysis of the three-dimensional error curve and the key event time sequence to achieve accurate evaluation of the performance of multi-satellite cooperative tasks, providing a reliable decision-making basis for space mission planning and system optimization.

[0081] In an embodiment of the simulation evaluation method for multi-satellite cooperative tasks of this application, referring to Figure 2 , it may specifically include the following content:

[0082] Step S201: The simulation industrial control computer receives the standard time signal sent by the second pulse controller, reads the internal timing signal of the timer, compares the phase of the standard time signal with the internal timing signal, calculates the time synchronization error, calibrates the frequency and phase of the internal timing signal based on the time synchronization error, generates a reference clock signal, maps the reference clock signal to the Coordinated Universal Time scale, and constructs a unified spatio-temporal reference;

[0083] Step S202: The simulation industrial control computer establishes a multi-satellite cooperative task dynamics model according to Newton's mechanical equation, uses the unified spatio-temporal reference as the time parameter of the dynamics model, calculates the orbital six-element numbers and Euler angle parameters of each satellite, inputs the solar radiation pressure value, atmospheric drag coefficient, and geomagnetic field intensity value into the dynamics model, constructs an environmental disturbance term matrix, and superimposes the environmental disturbance term matrix on the motion equation of the dynamics model.

[0084] Optionally, in this embodiment, a high-precision clock synchronization architecture is adopted when receiving the second pulse. The signal receiving circuit adopts a dual-channel design. The main channel realizes electrical isolation through a high-speed optocoupler, and the standby channel adopts a digital isolator to provide redundant protection. Signal conditioning uses a high-speed operational amplifier to construct a band-pass filter, and the cut-off frequency is designed in the range of 0.5 Hz to 1 kHz to realize signal shaping and noise suppression. The sampling circuit uses a high-precision comparator to accurately capture signal transitions through a programmable threshold.

[0085] This embodiment realizes a precise internal timing mechanism. The timer uses a temperature-compensated crystal oscillator as the reference source and generates the system clock through a multi-stage frequency division circuit. Frequency stability control uses a digital phase-locked loop to achieve fast locking through an adaptive loop bandwidth. Phase comparison uses the bilateral sampling method to improve measurement accuracy through oversampling and digital filtering. The time synchronization error calculation uses a sliding window average to eliminate the influence of random disturbances.

[0086] This embodiment designs an innovative clock calibration algorithm. Frequency calibration uses a proportional-integral controller, and the control parameters are optimized by the least squares method. Phase calibration uses a prediction-correction method to predict clock drift through Kalman filtering. The calibration process takes into account the influence of temperature and establishes a temperature compensation model through polynomial fitting. The reference clock signal generation uses digital direct frequency synthesis technology to ensure the phase continuity of the output signal.

[0087] This embodiment constructs a reliable space-time reference mapping mechanism. The UTC time mapping uses a two-way time transfer method to eliminate the influence of transmission delay through multiple measurements. Leap second processing adopts a smooth transition strategy to avoid system disturbances caused by time jumps. The time scale conversion takes into account the relativistic effect and realizes high-precision synchronization through gravitational potential difference correction.

[0088] This embodiment realizes a complete multi-satellite dynamics modeling. The motion equation is described by the Newton-Euler equations, and the state variables include the position vector, velocity vector, attitude quaternion, and angular velocity vector. The orbital dynamics calculation is derived using the variational method, considering perturbations from J2 to J4 terms. The attitude dynamics uses the rigid body motion equation, and the quaternion representation is used to avoid singularities.

[0089] This embodiment designs an accurate method for calculating orbital elements. The six orbital elements are obtained through the conversion of the position and velocity vectors, and an iterative algorithm is used in the conversion process to improve accuracy. The Euler angle calculation uses quaternion conversion, and singular value decomposition is used to ensure calculation stability. Parameter update adopts a prediction-correction strategy to improve calculation efficiency through multi-step numerical integration.

[0090] In this embodiment, a comprehensive environmental disturbance model is constructed. The solar radiation pressure model considers the optical characteristics of each surface element on the satellite surface and calculates the photon reflection effect through the Monte Carlo method. The atmospheric drag calculation uses the NRLMSISE-00 model, considering the influence of solar activity and geomagnetic disturbances. The geomagnetic field model adopts the IGRF series expansion and improves the calculation efficiency through a recursive algorithm.

[0091] In this embodiment, an efficient disturbance matrix construction is achieved. The calculation of matrix elements adopts a parallel processing architecture, and multi-thread acceleration is realized through OpenMP. The numerical integration uses a variable step size algorithm to control the accuracy through local truncation error. The disturbance superposition adopts a sub-item calculation strategy to balance the influence of each item through weight coefficients.

[0092] In this embodiment, a high-fidelity multi-satellite simulation system is realized through accurate spatio-temporal reference construction and reliable dynamics modeling. On the basis of ensuring the time synchronization accuracy, the model authenticity is improved through multi-level optimization. The overall design fully considers the complexity of the space environment and realizes an accurate description of the characteristics of the actual system.

[0093] The spatio-temporal reference of this embodiment has high stability and traceability, providing a unified time reference for multi-satellite cooperative control. The dynamics model realizes an accurate description of the satellite motion law through complete disturbance modeling, providing reliable data support for subsequent simulation evaluation. The implementation of this scheme significantly improves the authenticity and reliability of the simulation system, providing an important basis for space mission planning and control strategy optimization.

[0094] In an embodiment of the simulation evaluation method for the multi-satellite cooperative mission of this application, refer to Figure 3 , and it may specifically include the following content:

[0095] Step S301: The simulation industrial control computer inputs the dynamics model and the environmental disturbance model into the Runge-Kutta numerical integrator, sets the integration step size based on the unified spatio-temporal reference, calculates the orbital element variation and attitude angle variation of each satellite, substitutes the variation into the orbit prediction model, calculates the position vector, velocity vector and attitude vector of each satellite during the cooperative motion process, and generates multi-satellite cooperative motion simulation data;

[0096] Step S302: The simulation industrial control computer uses a Markov chain to establish a load switch fault model, an attitude sensor drift fault model, and an orbit control engine start-stop fault model, calculates the state transition probability matrix of each fault model, generates a fault state sequence based on the state transition probability matrix, and writes the fault parameters in the fault state sequence to the corresponding time points of the multi-satellite cooperative motion simulation data.

[0097] Optionally, a high-precision numerical integration framework is adopted in this embodiment. The Runge-Kutta integrator uses an 8th-order formula, and calculates the truncation error through the embedded 5th-order solution. The integration step size control adopts a PI controller and dynamically adjusts according to the local error estimation. To improve the calculation efficiency, the integrator supports parallel calculation, processes the state vector in blocks, and each block contains position, velocity, and attitude parameters. The calculation process uses double-precision floating-point operations to ensure numerical stability.

[0098] This embodiment implements an adaptive step size control mechanism. The step size selection is based on a unified space-time reference and is automatically adjusted through the error tolerance. The initial step size is estimated through the system characteristic time to avoid numerical oscillations. The step size is automatically subdivided at key event points to ensure the accurate capture of important state transitions. The step size adjustment adopts a smooth transition strategy to avoid numerical instability caused by mutations.

[0099] This embodiment designs an accurate method for calculating orbital elements. The calculation of variations considers the comprehensive influence of perturbing forces, including the non-spherical gravity of the Earth, atmospheric drag, and solar radiation pressure. The calculation process is derived using the variational method and describes the evolution of orbital elements through the Gauss variational equation. The update of orbital elements adopts a prediction-correction strategy and improves the calculation efficiency through multi-step extrapolation.

[0100] This embodiment constructs an innovative orbital prediction model. The prediction process adopts an analytical-numerical hybrid method, where the main terms use analytical solutions and the perturbation terms use numerical integration. The position vector is calculated by solving the Kepler equation using the Newton-Raphson iteration. The velocity vector is obtained by inverting the orbital elements, considering the velocity distortion correction. The attitude vector prediction uses quaternion integration and maintains the constraints through the Lie group method.

[0101] This embodiment implements a reliable fault model construction. The load switch fault model considers three modes: switch jamming, poor contact, and delay response. The state transition probability is based on historical data statistics and is obtained through maximum likelihood estimation. The model considers the influence of temperature and aging effects on the fault probability and adjusts the transition matrix through a correction factor.

[0102] This embodiment designs an accurate sensor drift model. The drift fault is described by a random walk process, and the drift rate is related to the temperature change. The model includes two parts: deterministic drift and random drift, and describes the state transition through a Markov jump process. The parameter estimation uses the expectation maximization algorithm and is obtained through training with historical data.

[0103] This embodiment constructs a comprehensive engine fault model. The start-stop faults include three characteristics: start delay, shutdown overshoot, and thrust decay. The state transition is described by a multi-layer Markov chain, considering the cumulative effect and recovery characteristics of the faults. The generation of fault parameters is achieved through the Monte Carlo method, maintaining physical constraints.

[0104] This embodiment realizes an efficient fault injection mechanism. The fault state sequence is generated by Markov chain sampling, and the sampling process uses a random number seed to ensure repeatability. The fault parameter writing adopts an event-driven manner, and the time point is located through binary search. The data update adopts atomic operations to ensure concurrent security.

[0105] This embodiment designs a complete data management architecture. The simulation data adopts a hierarchical storage structure, supporting fast retrieval by time and satellite number. The data compression uses a lossless algorithm, and the storage space is reduced through differential coding. The data consistency is guaranteed by the checksum mechanism, supporting the detection and recovery of damaged data.

[0106] This embodiment establishes a high-fidelity multi-satellite cooperative simulation system through precise numerical calculations and reliable fault injection. On the basis of ensuring the calculation accuracy, this scheme improves the simulation efficiency through multi-level optimization. The overall design fully considers the complexity of the space system and realizes an accurate description of the actual working characteristics.

[0107] The numerical integration framework of this embodiment has the characteristics of high precision and high efficiency, providing a reliable guarantee for multi-satellite orbit prediction. The fault model realizes an accurate description of various types of faults through the Markov chain, providing a test platform for fault diagnosis and fault-tolerant control. The implementation of this scheme significantly improves the authenticity and usability of the simulation system, providing important support for space mission planning and control strategy optimization.

[0108] In an embodiment of the simulation evaluation method for the multi-satellite cooperative mission of this application, see Figure 4 , and it may specifically include the following content:

[0109] Step S401: The simulation industrial control computer reads the 1553B bus communication protocol stack, configures the message block descriptors of the bus controller, sets the data length, sampling period, and buffer address of the message block, sends data acquisition instructions to each satellite system to be tested, reads the data frames of each sub-address on the bus, and parses the six orbital elements, Euler angles, angular velocities, engine switch quantities, sensor output values, and payload working states in the data frames;

[0110] Step S402: The simulation industrial control computer detects the data integrity according to the synchronization word and checksum in the data frame, extracts the time tag information in the data frame, maps the time tag information to the unified spatio-temporal reference, generates the time series of the telemetry data, and reconstructs the six orbital elements, Euler angles, angular velocities, engine switch quantities, sensor output values, and payload working states into a telemetry data stream according to the time series.

[0111] Optionally, a dual-buffer architecture is adopted during bus configuration in this embodiment. The bus controller uses an AceXtreme series PCIe interface card, which supports high-speed data acquisition and real-time processing. The message block descriptors are organized in a linked list manner, and each descriptor contains message type, data length, sub-address, and time tag. The cache management adopts a circular queue structure, and seamless data switching is achieved through a dual-pointer mechanism. The configuration process is directly programmed through hardware registers, reducing software overhead.

[0112] This embodiment implements a reliable data acquisition strategy. The acquisition instructions are issued in a polling manner, and data requests are made to each remote terminal on the bus according to a preset time sequence. The instruction scheduling adopts a priority queue, and the key parameters have a higher sampling priority. The data frame reception adopts an interrupt mode, and high-speed data transmission is achieved through a DMA channel. The timeout handling adopts a watchdog mechanism to automatically resend the failed data requests.

[0113] This embodiment designs an accurate data parsing framework. Frame parsing first identifies the frame boundary through a sync word, and then extracts the time code, sequence number, and data length in the frame header. Parameter parsing adopts a look-up table method to extract specific parameters according to the sub-address and data identifier. The number of orbital roots parsing considers range conversion and restores the physical quantity through a calibration coefficient. The attitude parameters are represented by quaternions, and visualization is achieved through Euler angle conversion.

[0114] This embodiment constructs an innovative data integrity verification mechanism. The integrity check includes frame synchronization check, CRC check, and parameter validity verification. The synchronization check is implemented through a sliding window to improve the anti-interference ability. The CRC check adopts hardware acceleration and supports polynomial parallel calculation. The parameter verification is through threshold judgment, and the threshold range is dynamically adjusted according to the working mode.

[0115] This embodiment implements an accurate time mapping algorithm. The time tag is first converted into a timestamp under a unified space-time reference, and the leap second relationship between GPS time and UTC time is considered during the conversion process. The time synchronization adopts a relative time scale method to eliminate system delay through linear fitting. The time series reconstruction adopts cubic spline interpolation to maintain data continuity.

[0116] This embodiment designs an efficient data reconstruction mechanism. The reconstruction process first establishes a time reference sequence, and then maps each parameter to a unified time point through time alignment. The parameter resampling adopts linear interpolation, and the interpolation nodes are adaptively selected through curvature calculation. The data smoothing adopts a sliding window process, and the window length is dynamically adjusted according to the data change rate.

[0117] This embodiment constructs a complete data flow management architecture. The data flow is organized in a pipeline mode, supporting multi-level caching and parallel processing. Data compression uses differential coding and achieves lossless compression through entropy coding. Flow control uses the token bucket algorithm to prevent buffer overflows caused by data bursts.

[0118] This embodiment implements a reliable exception handling mechanism. Exception detection includes three categories: data loss, parameter out-of-bounds, and timing exceptions. Lost data is repaired by interpolation, and the interpolation method is selected according to the data characteristics. Out-of-bounds parameters are processed by marking to retain the original information. Timing exceptions are resolved by reordering to ensure the causality of the data.

[0119] This embodiment designs a comprehensive data quality assessment system. Quality assessment includes three dimensions: integrity, accuracy, and timeliness. Integrity is evaluated through data frame statistics, accuracy is determined through noise analysis, and timeliness is calculated through latency statistics. The assessment results serve as data credibility indicators to guide subsequent processing.

[0120] This embodiment establishes an efficient telemetry data acquisition system through precise bus configuration and reliable data processing. While ensuring data integrity, this solution improves processing efficiency through multi-level optimization. The overall design fully considers the characteristics of space telemetry data and realizes the full-process management from acquisition to reconstruction.

[0121] The bus communication architecture of this embodiment has high reliability and high real-time performance, providing a reliable guarantee for multi-satellite telemetry data acquisition. Data processing ensures the quality of telemetry data through a complete verification mechanism and precise time synchronization. The implementation of this solution significantly improves the reliability and efficiency of data acquisition, providing high-quality data support for subsequent performance evaluation.

[0122] In an embodiment of the simulation evaluation method for multi-satellite collaborative tasks of this application, refer to Figure 5 , it may specifically include the following content:

[0123] Step S501: The simulation industrial computer reads the time tag information in the telemetry data stream, calculates the time interval between adjacent data points, performs cubic spline interpolation on the unequally spaced telemetry data, resamples the interpolated data into an equally spaced sequence, performs linear fitting on the resampled data based on the unified spatio-temporal reference, calculates the time deviation compensation amount, and superimposes the compensation amount on the time tag of the resampled data to generate the data to be evaluated with time alignment;

[0124] Step S502: The simulation industrial control mechanism constructs the relational database table structure, indexes the simulation data and the data to be evaluated according to satellite number, data type, and time tag, calculates the statistical feature values of the data, writes the statistical feature values as data quality marks into the database table, establishes a database transaction, and writes the simulation data and the data to be evaluated into the evaluation database in a batch processing manner.

[0125] Optionally, in this embodiment, an adaptive interpolation architecture is adopted in data processing. First, the distribution characteristics of the sampling interval are calculated through time difference, and an irregular sampling point sequence is established. Cubic spline interpolation adopts a segmented construction method, and the continuity of the curve is ensured through boundary condition constraints. Node selection adopts local curvature calculation, and sampling points are encrypted at locations where the data changes violently. The interpolation coefficient is solved by the chasing method for a tridiagonal system of equations to improve the calculation efficiency.

[0126] This embodiment realizes an accurate resampling mechanism. The resampling period is determined by the Shannon sampling theorem, considering the highest frequency component of the data. Sampling point generation adopts a linear-phase filter to avoid phase distortion. Data reconstruction adopts sinc function interpolation, and the ringing effect is suppressed by a window function. To improve the calculation efficiency, fast Fourier transform is used to achieve resampling in the frequency domain.

[0127] This embodiment designs an innovative time deviation compensation algorithm. In the compensation process, a reference time series is first established, and the time deviation trend is fitted by the least squares method. The fitting model considers linear drift and periodic changes, and the model parameters are obtained through iterative optimization. The compensation amount is calculated using piecewise linear interpolation to ensure the continuity of time correction.

[0128] This embodiment constructs a reliable data quality evaluation mechanism. Statistical feature calculations include parameters such as mean, variance, skewness, and kurtosis. Outlier detection uses the Mahalanobis distance criterion and realizes dynamic judgment through an adaptive threshold. Data integrity evaluation is performed through missing rate statistics, considering the data distribution within the time window.

[0129] This embodiment realizes an efficient database table structure design. The table structure adopts a star schema, the main table stores time series data, and the dimension tables contain satellite information and parameter types. The index design adopts a composite index to optimize the query performance. The partitioning strategy is based on time range and supports historical data archiving.

[0130] This embodiment designs an accurate data quality marking mechanism. The quality mark includes three dimensions: data availability, accuracy, and timeliness. Availability is evaluated through data integrity, accuracy is determined through noise analysis, and timeliness is calculated through latency statistics. The marking result adopts bitmap encoding to support fast retrieval.

[0131] This embodiment constructs a reliable database writing strategy. The batch writing uses prepared statements to improve the execution efficiency through parameterized queries. The transaction management adopts the ACID characteristics to ensure data consistency through two-phase commit. The concurrency control adopts the multi-version concurrency control mechanism to improve the concurrency performance.

[0132] This embodiment implements a complete data backup mechanism. The backup strategy adopts the incremental backup method and records the changes through binary logs. The data recovery supports point-in-time recovery and realizes transaction rollback through rollback logs. The storage optimization adopts compressed storage to reduce the storage space through dictionary encoding.

[0133] This embodiment designs an intelligent index optimization mechanism. The index selection is based on query pattern analysis, and the index effect is evaluated through the execution plan. The index maintenance adopts online reconstruction to avoid service interruption caused by table locking. The statistical information update adopts adaptive sampling to ensure that the optimizer selects the appropriate execution plan.

[0134] This embodiment establishes an efficient evaluation data processing system through precise data processing and reliable storage management. While ensuring the data quality, this solution improves the processing efficiency through multi-level optimization. The overall design fully considers the characteristics of spatial data and realizes the full-process optimization from data alignment to storage management.

[0135] The data processing architecture of this embodiment has high precision and high reliability, providing a reliable guarantee for multi-satellite evaluation data management. The database design ensures the consistency and availability of data through a complete optimization mechanism and reliable transaction management. The implementation of this solution significantly improves the efficiency of data processing and storage, provides high-quality data support for subsequent performance evaluation, and realizes the precise management and efficient utilization of space mission data.

[0136] In an embodiment of the simulation evaluation method for multi-satellite collaborative tasks of this application, refer to Figure 6 , it may specifically include the following content:

[0137] Step S601: The simulation industrial control computer executes a database query instruction, constructs a time window, reads the simulation data and the data to be evaluated from the evaluation database based on the time window, groups the simulation data and the data to be evaluated according to the satellite number and data type, calculates the mean and standard deviation of each group of data, and establishes a data preprocessing matrix;

[0138] Step S602: The simulation industrial control computer constructs a hierarchical evaluation index system based on the data preprocessing matrix. At the system level, formation keeping accuracy, mission completion rate, and resource utilization rate indicators are set. At the subsystem level, orbit control accuracy, attitude stability, and payload working efficiency indicators are set. At the component level, engine working status, sensor measurement error, and data transmission quality indicators are set. The calculation formulas and threshold ranges of each level of indicators are written into the index library.

[0139] Optionally, in this embodiment, a distributed processing architecture is adopted during query execution. The time window construction adopts a sliding window mechanism, and the window size is dynamically adjusted according to the evaluation task. The query optimizer adopts a cost-based optimization strategy and selects the optimal execution plan through statistical information. Data grouping adopts hash partitioning, and load balancing is achieved through the consistent hashing algorithm. Distributed queries adopt the MapReduce mode to reduce network transmission overhead.

[0140] This embodiment implements an efficient data preprocessing mechanism. Data grouping first establishes a primary index according to the satellite number, and then performs sub-grouping according to the data type. The calculation of statistics adopts an online algorithm, and the mean and standard deviation are calculated through a single pass scan. The outlier processing adopts the median absolute deviation method to ensure the robustness of the statistical results. The preprocessing matrix is stored in a sparse matrix, and the memory occupancy is reduced through a compressed format.

[0141] This embodiment designs an innovative method for constructing an index system. Among the system-level indicators, the formation keeping accuracy is evaluated by the relative position error, considering the geometric configuration and dynamic characteristics. The mission completion rate is analyzed through the key event sequence, combined with time constraints and functional requirements. The resource utilization rate is evaluated through multiple dimensions, including energy efficiency, storage utilization, and bandwidth occupancy.

[0142] This embodiment constructs an accurate subsystem-level evaluation framework. The orbit control accuracy is characterized by the position error ellipsoid, and the control characteristics are determined through principal axis analysis. The attitude stability is statistically analyzed by the Euler angle deviation, considering the influence of angular velocity and angular acceleration. The payload working efficiency is evaluated through the task execution characteristics, including response time and data quality.

[0143] This embodiment implements a reliable component evaluation mechanism. The evaluation of the engine working status includes starting characteristics, thrust stability, and shutdown performance. The Allan variance method is used for the sensor error analysis to identify the contributions of various error sources. The data transmission quality is evaluated through the statistics of the bit error rate and delay, considering the influence of channel characteristics.

[0144] This embodiment designs a complete index calculation system. The calculation formula adopts a modular design, supporting parameterized configuration and dynamic adjustment. The numerical calculation adopts an adaptive algorithm, and the appropriate calculation method is selected according to the data characteristics. The threshold setting adopts a multi-level judgment mechanism, and flexible evaluation is achieved through fuzzy rules.

[0145] In this embodiment, an intelligent index library management system is constructed. The index library adopts a hierarchical structure, and the dependency relationship between indexes is described through a relational model. Index definition supports version control, and the change history is tracked through differential comparison. Index reuse adopts a template mechanism to simplify the definition process of new indexes.

[0146] In this embodiment, an efficient data access mechanism is implemented. Index query adopts a multi-level cache, and the access latency is reduced through a prefetching mechanism. Data update adopts an incremental method, and only the changed part is processed. Concurrent control adopts a read-write lock mechanism to improve the efficiency of concurrent access.

[0147] In this embodiment, a reliable index verification mechanism is designed. The verification process includes numerical range check, physical constraint verification, and logical relationship judgment. Exception handling adopts a multi-level fault tolerance strategy, and the system availability is ensured through a backup mechanism. Result feedback adopts real-time monitoring to support dynamic adjustment of the evaluation strategy.

[0148] In this embodiment, a comprehensive multi-satellite evaluation framework is established through precise data processing and a systematic index system. While ensuring the comprehensiveness of the evaluation, this solution improves the processing efficiency through multi-level optimization. The overall design fully considers the complexity of the space system and realizes the complete link from data preprocessing to index evaluation.

[0149] The evaluation architecture of this embodiment has high scalability and high adaptability, providing a reliable guarantee for the evaluation of multi-satellite systems. The index system is realized through hierarchical design and modularization, ensuring the systematicness and maintainability of the evaluation. The implementation of this solution significantly improves the accuracy and efficiency of the evaluation, provides a scientific basis for the performance evaluation and optimization decision-making of space missions, and realizes the precise evaluation and effective management of complex space systems.

[0150] In an embodiment of the simulation evaluation method for multi-satellite cooperative missions of this application, referring to Figure 7 , it may specifically include the following content:

[0151] Step S701: The simulation industrial control computer reads the execution status data of the cooperative mission, establishes an analytic hierarchy process model for the system-level indexes, subsystem-level indexes, and component-level indexes, calculates the eigenvalues and eigenvectors of the judgment matrix, conducts a consistency test, calculates the weight coefficients of each level of indexes based on the eigenvector corresponding to the maximum eigenvalue, and combines the weight coefficients with the calculation formulas in the index library to form an evaluation model;

[0152] Step S702: The simulation industrial control computer applies the evaluation model to the simulation data and the data to be evaluated, calculates the three-dimensional position error and attitude error at each moment, draws an error curve graph, extracts the inflection point coordinates in the curve, reads the key event information corresponding to the inflection point moment, establishes a mapping relationship between the error and the event, and generates an evaluation result report including the index score, error analysis, and key events.

[0153] Optionally, in this embodiment, an adaptive weight calculation framework is adopted in the hierarchical analysis. The task execution state data is first reduced in dimension by principal component analysis to extract key features. The judgment matrix is constructed by combining expert experience and historical data, and the relative importance between indicators is established through fuzzy comprehensive evaluation. The eigenvalue calculation adopts the power iteration method, and the calculation stability is improved through QR decomposition. The consistency test is evaluated by the random consistency index to ensure the rationality of the judgment.

[0154] This embodiment realizes an innovative weight optimization mechanism. The system-level index weights are determined through task criticality analysis, considering task objectives and constraints. The subsystem-level index weights are calculated based on functional dependencies, and the influence degree is determined through sensitivity analysis. The component-level index weights adopt the entropy weight method and are dynamically adjusted according to the data fluctuation characteristics. The weight combination adopts the linear weighting method, and the sum of the weights is ensured to be 1 through normalization processing.

[0155] This embodiment designs an accurate evaluation model construction method. The model construction first establishes a mathematical expression for index calculation and ensures physical consistency through dimensional analysis. The parameter setting adopts an adaptive mechanism and is dynamically adjusted according to task characteristics. The model is verified through historical data backtesting to evaluate the prediction ability and stability of the evaluation model.

[0156] This embodiment constructs a reliable error analysis framework. The three-dimensional position error adopts ellipsoidal error analysis, and the error distribution characteristics are determined through principal axis transformation. The attitude error is calculated through quaternion difference, considering the treatment of Euler angle singularities. The error statistics adopt non-parametric methods, and the error distribution is described through kernel density estimation.

[0157] This embodiment realizes an intelligent curve analysis mechanism. The curve is drawn using adaptive sampling, and the sampling points are encrypted in key areas. The inflection point detection adopts the wavelet transform method, and the feature points are extracted through multi-scale analysis. The coordinate extraction adopts an interpolation algorithm to improve the inflection point positioning accuracy. The curve smoothing adopts the local regression method to eliminate the influence of random fluctuations.

[0158] This embodiment designs a complete event correlation analysis system. The key event extraction adopts temporal pattern mining, and the typical patterns are identified through sequence alignment. The event classification adopts hierarchical clustering, and the event families are established through similarity measurement. The mapping relationship is established through association rule mining to identify the causal relationship between the error and the event.

[0159] In this embodiment, a comprehensive evaluation report generation mechanism is constructed. The fuzzy evaluation method is used to calculate the index scores, and the qualitative-quantitative conversion is realized through the membership function. The error analysis adopts multi-dimensional characterization, including statistical characteristics and dynamic features. The event description uses natural language generation, and a readable report is generated through template filling.

[0160] In this embodiment, an efficient visualization display framework is realized. The error curve is drawn using a responsive design, supporting multi-resolution display. The graphic interaction adopts an event-driven mechanism, supporting data drilling and detail magnification. The annotation system adopts an intelligent layout to avoid label overlap.

[0161] In this embodiment, a reliable quality control mechanism is designed. The data quality control is realized through anomaly detection to identify and mark suspicious data points. The numerical stability check is adopted in the calculation process, and the calculation accuracy is evaluated through condition number analysis. The result verification adopts the cross-validation method to evaluate the reliability of the evaluation results.

[0162] In this embodiment, a comprehensive performance evaluation system is established through accurate hierarchical analysis and systematic error evaluation. While ensuring the evaluation accuracy, the analysis efficiency is improved through multi-level optimization. The overall design fully considers the complexity of space missions and realizes the complete process from data analysis to result display.

[0163] The evaluation system of this embodiment has high precision and high interpretability, providing a reliable basis for the evaluation of multi-satellite systems. The evaluation results ensure the scientificity and traceability of the evaluation through complete error analysis and event correlation. The implementation of this solution significantly improves the accuracy and efficiency of the evaluation, provides an important basis for the performance optimization and decision support of space missions, and realizes the accurate evaluation and in-depth understanding of complex aerospace systems.

[0164] In order to accurately evaluate the performance of multi-satellite cooperative missions and provide a reliable decision-making basis for space mission planning and system optimization, this application provides an embodiment of a simulation evaluation device for multi-satellite cooperative missions, which implements all or part of the content of the simulation evaluation method for the multi-satellite cooperative missions. Refer to Figure 8 , the simulation evaluation device for the multi-satellite cooperative missions specifically includes the following contents:

[0165] The simulation model construction module 10 is used to simulate that an industrial control computer receives a standard time signal sent by a second pulse controller, and constructs a unified space-time reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite cooperative mission dynamics model, and uses the unified space-time reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance model; the simulation industrial control computer generates multi-satellite cooperative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite cooperative motion simulation data;

[0166] The data processing module 20 is used for the simulation industrial control computer to collect telemetry data of each satellite system to be measured through a bus interface, and the telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters; time-align the telemetry data according to the unified space-time reference to generate data to be evaluated; the simulation industrial control computer writes the simulation data and the data to be evaluated into an evaluation database;

[0167] The mission index evaluation module 30 is used for the simulation industrial control computer to read the simulation data and the data to be evaluated from the evaluation database, and establish a multi-satellite cooperative mission evaluation index system, and the evaluation index system includes system-level indexes, subsystem-level indexes, and component-level indexes; the simulation industrial control computer calculates the weight coefficients of each level of indexes based on the execution status of the cooperative mission; perform data analysis on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients to generate a three-dimensional error curve graph and a key event time sequence graph; the simulation industrial control computer extracts the inflection point information in the three-dimensional error curve graph, matches the inflection point information with the key event time sequence graph, and generates an evaluation result report.

[0168] As can be seen from the above description, the simulation evaluation device for multi-satellite cooperative missions provided by the embodiments of the present application can construct a unified space-time reference based on a standard time signal, and establish a complete environmental disturbance model including solar radiation pressure, atmospheric drag, and geomagnetic field interference. Innovatively designed a fault injection mechanism for payload, attitude, and orbit control to realize the real simulation of multi-satellite cooperative motion. The system has established a multi-level evaluation index system at the system level, subsystem level, and component level, adopted a dynamic weight calculation method based on the mission execution status, and combined with the matching analysis of the three-dimensional error curve and the key event time sequence to realize the accurate evaluation of the performance of multi-satellite cooperative missions, providing a reliable decision-making basis for space mission planning and system optimization.

[0169] At the hardware level, in order to accurately evaluate the performance of multi-satellite collaborative tasks and provide a reliable decision-making basis for space mission planning and system optimization, this application provides an embodiment of an electronic device for implementing all or part of the simulation evaluation method for the multi-satellite collaborative tasks. The electronic device specifically includes the following:

[0170] A processor, a memory, a communications interface, and a bus; wherein, the processor, the memory, and the communications interface complete communication with each other through the bus; the communications interface is used to implement information transmission between the simulation evaluation device for multi-satellite collaborative tasks and related devices such as the core business system, the user terminal, and the relevant database. The logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., and this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiments of the simulation evaluation method for multi-satellite collaborative tasks and the embodiments of the simulation evaluation device for multi-satellite collaborative tasks, and the content thereof is incorporated herein, and the repeated parts will not be elaborated again.

[0171] It can be understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0172] In practical applications, part of the simulation evaluation method for multi-satellite collaborative tasks can be executed on the electronic device side as described above, or all operations can be completed in the client device. Specifically, it can be selected according to the processing capacity of the client device and the limitations of the user usage scenario. This application does not make any limitations in this regard. If all operations are completed in the client device, the client device may further include a processor.

[0173] The above-mentioned client device may have a communication module (i.e., a communication unit), and can be communicatively connected to a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, it may also include a server on the intermediate platform, such as a server on a third-party server platform communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster composed of multiple servers, or a server structure of a distributed device.

[0174] Figure 9 It is a schematic block diagram of the system composition of the electronic device 9600 according to the embodiment of this application. As Figure 9As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It should be noted that this Figure 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0175] In one embodiment, the function of the simulation evaluation method for multi-satellite cooperative missions can be integrated into the central processing unit 9100. Among them, the central processing unit 9100 can be configured to perform the following controls:

[0176] Step S101: The simulation industrial control computer receives the standard time signal sent by the second pulse controller, constructs a unified space-time reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite cooperative mission dynamics model, and uses the unified space-time reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance model; the simulation industrial control computer generates multi-satellite cooperative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite cooperative motion simulation data;

[0177] Step S102: The simulation industrial control computer collects the telemetry data of each satellite system to be tested through a bus interface, and the telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters; time-aligns the telemetry data according to the unified space-time reference to generate data to be evaluated; the simulation industrial control computer writes the simulation data and the data to be evaluated into an evaluation database;

[0178] Step S103: The simulation industrial control computer reads the simulation data and the data to be evaluated from the evaluation database, establishes a multi-satellite cooperative mission evaluation index system, and the evaluation index system includes system-level indicators, subsystem-level indicators, and component-level indicators; the simulation industrial control computer calculates the weight coefficients of each level of indicators based on the execution status of the cooperative mission; performs data analysis on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients to generate a three-dimensional error curve graph and a key event time sequence graph; the simulation industrial control computer extracts the inflection point information in the three-dimensional error curve graph, matches the inflection point information with the key event time sequence graph, and generates an evaluation result report.

[0179] As can be seen from the above description, the electronic device provided by the embodiments of the present application constructs a unified space-time reference based on standard time signals, and establishes a complete environmental perturbation model including solar radiation pressure, atmospheric drag, and geomagnetic field interference. An innovative fault injection mechanism for payload, attitude, and orbit control is designed to achieve a realistic simulation of multi-satellite cooperative motion. The system establishes a multi-level evaluation index system at the system level, subsystem level, and component level, adopts a dynamic weight calculation method based on the task execution status, and combines the matching analysis of the three-dimensional error curve and the key event time sequence to achieve an accurate evaluation of the performance of multi-satellite cooperative tasks, providing a reliable decision-making basis for space mission planning and system optimization.

[0180] In another embodiment, the simulation evaluation device for multi-satellite cooperative tasks can be separately configured from the central processing unit 9100. For example, the simulation evaluation device for multi-satellite cooperative tasks can be configured as a chip connected to the central processing unit 9100, and the functions of the simulation evaluation method for multi-satellite cooperative tasks are realized through the control of the central processing unit.

[0181] As Figure 9 shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily have to include Figure 9 all the components shown in Figure 9 ; in addition, the electronic device 9600 may further include

[0182] components not shown in Figure 9 ; reference may be made to the prior art.

[0182] As Figure 9 shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processing unit 9100 receives inputs and controls the operations of the various components of the electronic device 9600.

[0183] Among them, the memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. The above information related to failures can be stored, and in addition, programs for executing relevant information can also be stored. And the central processing unit 9100 can execute the programs stored in the memory 9140 to achieve information storage or processing, etc.

[0184] The input unit 9120 provides inputs to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0185] The memory 9140 can be a solid-state memory, for example, a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that stores information even when power is off, can be selectively erased and has more data. Examples of such a memory are sometimes referred to as EPROMs, etc. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage unit 9142 that is used to store application programs and function programs or the processes for operating the electronic device 9600 by the central processor 9100.

[0186] The memory 9140 can also include a data storage unit 9143 that is used to store data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 can include various drivers of the electronic device for communication functions and / or for performing other functions of the electronic device (such as a messaging application, an address book application, etc.).

[0187] The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0188] Based on different communication technologies, multiple communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module 9110 (transmitter / receiver) is also coupled to the speaker 9131 and the microphone 9132 via the audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby implementing normal telecommunication functions. The audio processor 9130 can include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine via the microphone 9132, and the sound stored on the local machine can be played via the speaker 9131.

[0189] An embodiment of the present application also provides a computer-readable storage medium that can implement all steps of the simulation evaluation method for multi-satellite collaborative tasks where the execution entity in the above embodiments is a server or a client. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it implements all steps of the simulation evaluation method for multi-satellite collaborative tasks where the execution entity in the above embodiments is a server or a client. For example, when the processor executes the computer program, the following steps are implemented:

[0190] Step S101: The simulation industrial control computer receives a standard time signal sent by a second pulse controller, constructs a unified space-time reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite collaborative task dynamics model, and uses the unified space-time reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance model; the simulation industrial control computer generates multi-satellite collaborative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite collaborative motion simulation data;

[0191] Step S102: The simulation industrial control computer collects telemetry data of each satellite system to be tested through a bus interface. The telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters; the telemetry data is time-aligned according to the unified space-time reference to generate data to be evaluated; the simulation industrial control computer writes the simulation data and the data to be evaluated into an evaluation database;

[0192] Step S103: The simulation industrial control computer reads the simulation data and the data to be evaluated from the evaluation database, establishes a multi-satellite collaborative task evaluation index system. The evaluation index system includes system-level indicators, subsystem-level indicators, and component-level indicators; the simulation industrial control computer calculates the weight coefficients of each level of indicators based on the execution status of the collaborative task; data analysis is performed on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients to generate a three-dimensional error curve graph and a key event time sequence graph; the simulation industrial control computer extracts the inflection point information in the three-dimensional error curve graph, matches the inflection point information with the key event time sequence graph, and generates an evaluation result report.

[0193] As can be seen from the above description, the computer-readable storage medium provided by the embodiments of the present application constructs a unified space-time reference based on the standard time signal, and establishes a complete environmental disturbance model including solar radiation pressure, atmospheric drag, and geomagnetic field interference. An innovative fault injection mechanism for payload, attitude, and orbit control is designed to achieve a real simulation of multi-satellite cooperative motion. The system establishes a multi-level evaluation index system at the system level, subsystem level, and component level, adopts a dynamic weight calculation method based on the task execution status, and combines the matching analysis of the three-dimensional error curve and the key event time sequence to achieve an accurate evaluation of the performance of multi-satellite cooperative tasks, providing a reliable decision-making basis for space mission planning and system optimization.

[0194] An embodiment of the present application also provides a computer program product that can implement all the steps in the simulation evaluation method of the multi-satellite cooperative task with the execution subject being a server or a client in the above embodiments. When the computer program / instructions are executed by a processor, the steps of the simulation evaluation method of the multi-satellite cooperative task are implemented. For example, the computer program / instructions implement the following steps:

[0195] Step S101: The simulation industrial control computer receives the standard time signal sent by the second pulse controller, constructs a unified space-time reference based on the standard time signal; the simulation industrial control computer establishes a multi-satellite cooperative task dynamics model, and uses the unified space-time reference as the time parameter of the dynamics model; the simulation industrial control computer inputs solar radiation pressure, atmospheric drag, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance model; the simulation industrial control computer generates multi-satellite cooperative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial control computer constructs a payload fault model, an attitude control fault model, and an orbit control fault model, and injects the fault models into the multi-satellite cooperative motion simulation data;

[0196] Step S102: The simulation industrial control computer collects the telemetry data of each satellite system to be tested through the bus interface. The telemetry data includes orbit parameters, attitude parameters, control parameters, and payload parameters; the telemetry data is time-aligned according to the unified space-time reference to generate data to be evaluated; the simulation industrial control computer writes the simulation data and the data to be evaluated into the evaluation database;

[0197] Step S103: The simulation industrial control computer reads the simulation data and the data to be evaluated from the evaluation database, establishes a multi-satellite collaborative mission evaluation index system, where the evaluation index system includes system-level indexes, subsystem-level indexes, and component-level indexes; the simulation industrial control computer calculates the weight coefficients of each level of indexes based on the collaborative mission execution status; performs data analysis on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients to generate a three-dimensional error curve graph and a key event time sequence graph; the simulation industrial control computer extracts the inflection point information from the three-dimensional error curve graph, matches the inflection point information with the key event time sequence graph, and generates an evaluation result report.

[0198] As can be seen from the above description, the computer program product provided by the embodiments of the present application constructs a unified spatio-temporal reference based on the standard time signal, and establishes a complete environmental disturbance model including solar radiation pressure, atmospheric drag, and geomagnetic field interference. Innovatively designs a fault injection mechanism for payload, attitude, and orbit control to achieve a realistic simulation of multi-satellite collaborative motion. The system establishes a multi-level evaluation index system at the system level, subsystem level, and component level, adopts a dynamic weight calculation method based on the mission execution status, and combines the matching analysis of the three-dimensional error curve and the key event time sequence to achieve an accurate evaluation of the performance of multi-satellite collaborative missions, providing a reliable decision-making basis for space mission planning and system optimization.

[0199] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, apparatus, or computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0201] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0202] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0203] In the present invention, specific embodiments are used to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only for helping to understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A simulation evaluation method for a multi-satellite collaborative mission, characterized in that: The method comprises: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, reads the internal timing signal of the timer, performs a phase comparison between the standard time signal and the internal timing signal, calculates the time synchronization error, calibrates the frequency and phase of the internal timing signal based on the time synchronization error, generates a reference clock signal, maps the reference clock signal to the coordinated universal time scale, and constructs a unified space-time reference; the simulation industrial computer establishes a multi-satellite collaborative task dynamics model according to Newton's mechanics equations, uses the unified space-time reference as the time parameter of the dynamics model, calculates the six orbital elements and Euler angle parameters of each satellite, inputs the solar light pressure value, the atmospheric drag coefficient, and the geomagnetic field intensity value into the dynamics model, constructs an environmental disturbance term matrix, and superimposes the environmental disturbance term matrix onto the motion equation of the dynamics model; the simulation industrial computer generates multi-satellite collaborative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial computer constructs a load fault model, an attitude control fault model, and an orbit control fault model, and injects the fault model into the multi-satellite collaborative motion simulation data; The simulation industrial computer collects telemetry data of each satellite system to be tested through a bus interface, and the telemetry data includes orbit parameters, attitude parameters, control parameters and payload parameters; the telemetry data is time-aligned according to the unified time-space reference to generate data to be evaluated, specifically including: firstly establishing a relative time series based on a reference satellite, calculating the time deviation of other satellite data relative to the reference series, using a linear fitting method to compensate for the time deviation, and calculating the fitting coefficient by the least square method. The compensated data is cross-correlation analyzed to verify the synchronization effect, and the correlation coefficient is used as a synchronization quality indicator; the simulation industrial computer writes the simulation data and the data to be evaluated into an evaluation database; The simulation industrial computer reads the simulation data and the data to be evaluated from the evaluation database, and establishes a multi-satellite collaborative task evaluation index system, wherein the evaluation index system includes system-level indicators, subsystem-level indicators and component-level indicators; the simulation industrial computer calculates weight coefficients of indicators at each level based on the collaborative task execution status; performs data analysis on the simulation data and the data to be evaluated according to the evaluation index system and the weight coefficients, and generates a three-dimensional error curve graph and a key event timing diagram; the simulation industrial computer extracts inflection point information in the three-dimensional error curve graph, matches the inflection point information with the key event timing diagram, and generates an evaluation result report.

2. The simulation evaluation method for multi-satellite collaborative tasks according to claim 1, characterized in that: The simulation industrial control computer generates multi-satellite coordinated motion simulation data based on the dynamic model and the environmental disturbance model; the simulation industrial control computer builds a load fault model, an attitude control fault model, and an orbit control fault model, and injects the fault model into the multi-satellite coordinated motion simulation data, including: The simulation industrial computer inputs the dynamic model and the environmental disturbance model into the Runge-Kutta numerical integrator, sets the integration step size based on the unified space-time reference, calculates the orbital element number change and attitude angle change of each satellite, substitutes the change into the orbit prediction model, calculates the position vector, velocity vector and attitude vector of each satellite in the coordinated motion process, and generates multi-satellite coordinated motion simulation data; The simulation industrial computer uses Markov chain to establish a load switch fault model, an attitude sensor drift fault model, and an orbit control engine start-stop fault model, calculates the state transition probability matrix of each fault model, generates a fault state sequence based on the state transition probability matrix, and writes the fault parameters in the fault state sequence into the corresponding time points of the multi-satellite collaborative motion simulation data.

3. The simulation evaluation method for multi-satellite collaborative mission according to claim 1, characterized in that: The simulation industrial computer collects telemetry data of each satellite system to be tested through a bus interface, and the telemetry data includes orbit parameters, attitude parameters, control parameters and payload parameters, including: The simulation industrial computer reads the 1553B bus communication protocol stack, configures the message block descriptor of the bus controller, sets the data length, sampling period and cache address of the message block, sends data acquisition instructions to each satellite system to be tested, reads the data frame of each sub-address on the bus, and parses the six orbital numbers, Euler angles, angular velocity, engine switch value, sensor output value and payload working status in the data frame; The simulation industrial computer detects data integrity according to the synchronization word and check code in the data frame, extracts the time tag information in the data frame, maps the time tag information with the unified space-time reference, generates a time series of telemetry data, and reconstructs the six orbital numbers, Euler angles, angular velocity, engine switching quantity, sensor output value and load working status into a telemetry data stream according to the time series.

4. The simulation evaluation method for multi-satellite collaborative mission according to claim 3, characterized in that: said performing time alignment on the telemetry data according to the unified spatiotemporal reference to generate data to be evaluated; The simulation industrial computer writes the simulation data and the data to be evaluated into an evaluation database, including: The simulation industrial computer reads the time tag information in the telemetry data stream, calculates the time intervals of adjacent data points, performs cubic spline interpolation on the unequally spaced telemetry data, resamples the interpolated data into an equally spaced sequence, performs linear fitting on the resampled data based on the unified time-space reference, calculates the time deviation compensation amount, and superimposes the compensation amount on the time tag of the resampled data to generate time-aligned data to be evaluated; The simulation industrial control computer constructs a relational database table structure, indexes the simulation data and the data to be evaluated according to the satellite number, data type and time tag, calculates the statistical characteristic value of the data, writes the statistical characteristic value into the database table as a data quality mark, establishes a database transaction, and writes the simulation data and the data to be evaluated into the evaluation database in batch mode.

5. The simulation evaluation method for multi-satellite collaborative mission according to claim 1, characterized in that: The simulation industrial computer reads the simulation data and the data to be evaluated from the evaluation database, and establishes a multi-satellite collaborative task evaluation index system, wherein the evaluation index system includes system-level indicators, subsystem-level indicators, and component-level indicators, including: The simulation industrial computer executes a database query instruction, constructs a time window, reads the simulation data and the data to be evaluated from the evaluation database based on the time window, groups the simulation data and the data to be evaluated according to satellite numbers and data types, calculates the mean and standard deviation of each group of data, and establishes a data preprocessing matrix; The simulation industrial computer constructs a hierarchical evaluation index system based on the data preprocessing matrix, sets formation keeping accuracy, task completion rate, and resource utilization rate indicators at the system level, sets orbit control accuracy, attitude stability, and payload work efficiency indicators at the subsystem level, sets engine working status, sensor measurement error, and data transmission quality indicators at the component level, and writes the calculation formulas and threshold ranges of indicators at each level into the index library.

6. The simulation evaluation method for multi-satellite collaborative mission according to claim 1, characterized in that: The simulation industrial computer calculates the weight coefficients of indicators at all levels based on the collaborative task execution status; performs data analysis on the simulation data and the data to be evaluated according to the evaluation indicator system and the weight coefficients, and generates a three-dimensional error curve graph and a key event timing diagram; The simulation industrial computer extracts the inflection point information in the three-dimensional error curve diagram, matches the inflection point information with the key event timing diagram, and generates an evaluation result report, including: The simulation industrial computer reads the collaborative task execution status data, establishes a hierarchical analysis model for the system-level indicators, subsystem-level indicators and component-level indicators, calculates the eigenvalues ​​and eigenvectors of the judgment matrix, performs consistency check, calculates the weight coefficients of indicators at all levels based on the eigenvector corresponding to the maximum eigenvalue, and combines the weight coefficients with the calculation formula in the indicator library to form an evaluation model; The simulation industrial computer applies the evaluation model to the simulation data and the data to be evaluated, calculates the three-dimensional position error and posture error at each moment, draws an error curve, extracts the inflection point coordinates in the curve, reads the key event information corresponding to the inflection point moment, establishes a mapping relationship between errors and events, and generates an evaluation result report including indicator scores, error analysis and key events.

7. A simulation evaluation device for multi-satellite collaborative tasks, characterized in that: The device comprises: A simulation model construction module is used to simulate an industrial computer receiving a standard time signal sent by a pulse per second controller, reading an internal timing signal of a timer, performing a phase comparison between the standard time signal and the internal timing signal, calculating a time synchronization error, calibrating the frequency and phase of the internal timing signal based on the time synchronization error, generating a reference clock signal, mapping the reference clock signal to the coordinated universal time scale, and constructing a unified space-time reference; the simulation industrial computer establishes a multi-satellite collaborative task dynamics model according to Newton's mechanics equations, uses the unified space-time reference as the time parameter of the dynamics model, calculates the six orbital elements and Euler angle parameters of each satellite, inputs the solar light pressure value, the atmospheric drag coefficient, and the geomagnetic field intensity value into the dynamics model, constructs an environmental disturbance term matrix, and superimposes the environmental disturbance term matrix onto the motion equation of the dynamics model; the simulation industrial computer generates multi-satellite collaborative motion simulation data based on the dynamics model and the environmental disturbance model; the simulation industrial computer constructs a load fault model, an attitude control fault model, and an orbit control fault model, and injects the fault model into the multi-satellite collaborative motion simulation data; The data processing module is used for the simulation industrial computer to collect telemetry data of each satellite system to be tested through a bus interface, wherein the telemetry data includes orbit parameters, attitude parameters, control parameters and payload parameters; the telemetry data is time-aligned according to the unified time-space reference to generate data to be evaluated, specifically including: firstly establishing a relative time series based on a reference satellite, calculating the time deviation of other satellite data relative to the reference series, using a linear fitting method to compensate for the time deviation, calculating the fitting coefficient by the least squares method, verifying the synchronization effect of the compensated data by cross-correlation analysis, and using the correlation coefficient as a synchronization quality indicator; the simulation industrial computer writes the simulation data and the data to be evaluated into an evaluation database; A task indicator evaluation module is used for the simulation industrial computer to read the simulation data and the data to be evaluated from the evaluation database, and establish a multi-satellite collaborative task evaluation indicator system, wherein the evaluation indicator system includes system-level indicators, subsystem-level indicators and component-level indicators; the simulation industrial computer calculates the weight coefficients of indicators at each level based on the collaborative task execution status; performs data analysis on the simulation data and the data to be evaluated according to the evaluation indicator system and the weight coefficients, and generates a three-dimensional error curve diagram and a key event timing diagram; the simulation industrial computer extracts inflection point information in the three-dimensional error curve diagram, matches the inflection point information with the key event timing diagram, and generates an evaluation result report.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the simulation evaluation method for multi-satellite collaborative tasks described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the simulation evaluation method for multi-satellite collaborative tasks described in any one of claims 1 to 6 are implemented.

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