Simulation evaluation method and device for multi-satellite collaborative task

Abstract

The embodiment of the invention provides a simulation evaluation method and device for a multi-satellite cooperative task, and the method comprises the steps: building a unified time-space reference based on a standard time signal, and building a complete environment disturbance model comprising sunlight pressure, atmospheric resistance and geomagnetic field interference. A fault injection mechanism of load, attitude and orbit control is innovatively designed, and real simulation of multi-satellite cooperative motion is realized. According to the system, a system-level, subsystem-level and component-level multi-level evaluation index system is established, a dynamic weight calculation method based on a task execution state is adopted, and matching analysis of a three-dimensional error curve and a key event time sequence is combined, so that accurate evaluation of multi-satellite cooperative task performance is realized; and a reliable decision basis is provided for space task planning and system optimization.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to a simulation evaluation method and device for multi-satellite collaborative tasks. Background Art

[0002] The existing multi-satellite collaborative mission simulation and evaluation technology has obvious deficiencies. Traditional systems usually use simplified dynamic models, which fail to fully consider the combined effects of environmental disturbance factors such as solar pressure, atmospheric drag, and geomagnetic field, resulting in a large deviation between the simulation results and the actual operating status. At the same time, the existing system lacks a complete fault simulation mechanism, making it difficult to effectively verify the collaborative performance of the multi-satellite system under various fault conditions.

[0003] In addition, existing technologies also have limitations in the construction of evaluation index systems and data analysis. Most systems use a single level of evaluation indicators and fail to establish a multi-level evaluation system at the system level, subsystem level, and component level, making it difficult to fully reflect the execution effect of multi-satellite collaborative tasks. 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 system has technical bottlenecks in telemetry data processing and time alignment. The lack of a unified spatiotemporal reference framework makes it difficult to achieve accurate synchronization and comparative analysis of multi-source telemetry data. Solving these problems is of great significance to improving the accuracy and practicality of multi-satellite collaborative mission simulation evaluation. Summary of the invention

[0005] In response to the problems in the prior art, the present application provides a simulation evaluation method and device for multi-satellite collaborative missions, which can achieve accurate evaluation of the performance of multi-satellite collaborative missions and provide a reliable decision-making basis for space mission planning and system optimization.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions: In a first aspect, the present application provides a simulation evaluation method for a multi-satellite collaborative task, comprising: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, and builds a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to build an environmental disturbance 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 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 collaborative motion simulation data; The simulation industrial computer collects 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 and space 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; 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.

[0007] Furthermore, the simulation industrial computer receives a standard time signal sent by a second pulse controller, and builds a unified space-time reference based on the standard time signal; the simulation industrial computer establishes a multi-satellite collaborative task dynamics model, and uses the unified space-time reference as a time parameter of the dynamics model; the simulation industrial computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to build an environmental disturbance model, including: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, reads the internal timing signal of the timer, compares the phases 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; The simulation industrial computer establishes a multi-satellite collaborative mission dynamics model based on Newton's mechanics equations, takes 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 on the motion equation of the dynamics model.

[0008] Further, the simulation industrial control computer generates multi-satellite collaborative motion simulation data based on the dynamic model and the environmental disturbance model; the simulation industrial control 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, 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.

[0009] Furthermore, 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.

[0010] Further, the telemetry data is time-aligned according to the unified time-space 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.

[0011] Furthermore, the simulation industrial computer reads the simulation data and the data to be evaluated from the evaluation database, and establishes a multi-satellite collaborative mission 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.

[0012] Further, 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 to generate 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 graph, 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.

[0013] In a second aspect, the present application provides a simulation evaluation device for a multi-satellite collaborative task, comprising: A simulation model construction module is used to simulate an industrial computer receiving a standard time signal sent by a second pulse controller, and to construct a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance 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 and space 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; 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.

[0014] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the simulation evaluation method for multi-satellite collaborative tasks when executing the program.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the simulation evaluation method for multi-satellite collaborative tasks.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the simulation evaluation method for multi-satellite collaborative tasks.

[0017] It can be seen from the above technical solutions that the present application provides a simulation evaluation method and device for multi-satellite collaborative missions. By constructing a unified space-time reference based on standard time signals, a complete environmental disturbance model including solar pressure, atmospheric drag and geomagnetic field interference is established. The fault injection mechanism for payload, attitude and orbit control is innovatively designed to achieve a true 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 sequences to achieve accurate evaluation of the performance of multi-satellite collaborative missions, providing a reliable decision-making basis for space mission planning and system optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is one of the flow charts of the simulation evaluation method of the multi-satellite collaborative task in the embodiment of the present application; Figure 2 The second flowchart of the simulation evaluation method for multi-satellite collaborative tasks in the embodiment of the present application; Figure 3 The third flowchart of the simulation evaluation method for multi-satellite collaborative tasks in the embodiment of the present application; Figure 4 This is a fourth flow chart of the simulation evaluation method for multi-satellite collaborative tasks in an embodiment of the present application; Figure 5 5 is a flowchart of a simulation evaluation method for a multi-satellite collaborative task in an embodiment of the present application; Figure 6 This is a sixth flow chart of the simulation evaluation method for multi-satellite collaborative tasks in an embodiment of the present application; Figure 7 FIG7 is a flowchart of a simulation evaluation method for a multi-satellite collaborative task in an embodiment of the present application; Figure 8 A structural diagram of a simulation evaluation device for a multi-satellite collaborative task in an embodiment of the present application; Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0020] Reference numerals: 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 unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0023] Taking into account the problems existing in the prior art, the present application provides a simulation evaluation method and device for multi-satellite collaborative missions. By constructing a unified space-time reference based on standard time signals, a complete environmental disturbance model including solar pressure, atmospheric drag and geomagnetic field interference is established. The fault injection mechanism for payload, attitude and orbit control is innovatively designed to achieve a true 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 sequences to achieve accurate evaluation of the performance of multi-satellite collaborative missions, providing a reliable decision-making basis for space mission planning and system optimization.

[0024] In order to accurately evaluate the performance of multi-satellite collaborative missions 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 collaborative missions, see Figure 1 The simulation evaluation method of the multi-satellite collaborative mission specifically includes the following contents: Step S101: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, and builds a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to build an environmental disturbance 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 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 collaborative motion simulation data; Optionally, this embodiment adopts a dual-path signal isolation and conditioning architecture when receiving the second pulse standard time signal. The main signal is electrically isolated by the high-speed optocoupler MOC8102, and the secondary signal is backed up by the digital isolator ADuM1401. The signal conditioning circuit uses the operational amplifier AD8065 to build a bandpass filter, and the cut-off frequency is designed to be 10Hz and 100kHz to achieve noise suppression of the second pulse signal. The signal sampling uses the high-speed comparator LT1719, and the trigger level is dynamically adjusted by the precision DAC to ensure stable trigger characteristics when the signal amplitude fluctuates. In order to improve the sampling accuracy, FPGA is used to implement a 100MHz oversampling clock, and digital noise reduction is performed through a moving average algorithm.

[0025] This embodiment implements a highly stable space-time reference construction mechanism. The reference clock uses a constant temperature crystal oscillator OCXO, and its temperature stability is better than 0.1PPB / ℃. The phase-locked loop circuit uses ADF4355 as the phase-locked loop main chip, and cooperates with the fractional divider to achieve precise adjustment of the local clock. The phase detector uses a phase-frequency detector, and achieves fast locking and low jitter through an adaptive loop bandwidth algorithm. Temperature compensation uses third-order polynomial fitting, and the compensation coefficient is dynamically updated through real-time temperature monitoring. The time reference mapping adopts 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.

[0026] This embodiment designs a complete dynamic modeling framework. The satellite motion equation adopts the Newton-Euler equations to couple the translational and rotational motions. The state vector contains the position vector r, velocity vector v, attitude quaternion q and angular velocity vector ω in the inertial coordinate system. The gravitational term in the motion equation adopts the EGM2008 gravity field model and is 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 Reynold number; the calculation of sunlight pressure considers the material properties of each facet on the satellite surface, and the Monte Carlo method is used to calculate the photon reflection and re-radiation effects; the geomagnetic moment is calculated by the dipole model, taking into account the influence of the residual magnetic moment. The model supports multi-satellite collaborative simulation and realizes inter-satellite information interaction through the communication delay model.

[0027] This embodiment realizes high-precision environmental disturbance modeling. When calculating the solar pressure, the satellite surface is divided into multiple facets, and the optical characteristics of each facet are described by a 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 resistance model adopts the NRLMSISE-00 standard, and the input parameters include the solar-terrestrial space weather index F10.7, the geomagnetic index Ap and the geographical location parameters. The model takes into account the daily variation, seasonal variation and solar activity cycle variation of atmospheric density, and realizes density field reconstruction through three-dimensional interpolation. The geomagnetic field model adopts the IGRF13 coefficient set to realize the spherical harmonic function expansion, and the calculation process uses a recursive algorithm to improve efficiency.

[0028] This embodiment adopts a multi-step adaptive numerical integration strategy. The 8th-order Runge-Kutta method is used to solve the equation of motion, and the local truncation error is calculated by an embedded 5th-order solution. The step size control adopts a PI controller to dynamically adjust the step size according to the error estimate. The integrator supports parallel computing, divides the state vector into blocks, and realizes multi-threaded acceleration through OpenMP. For strong nonlinear regions, it automatically switches to the symplectic integrator GGL4 to maintain the energy and angular momentum conservation characteristics of the system.

[0029] This embodiment constructs a comprehensive fault model injection mechanism. The load fault model includes three categories: switch fault, performance degradation and data anomaly. Each type of fault describes the state transition process through a Markov chain. The transition probability matrix is ​​obtained through historical data training, taking into account the temporal correlation of the fault. Attitude control faults include typical fault modes such as gyro drift, star sensor failure, and torque wheel jam. Gyro drift adopts a random walk model, and the drift rate is related to temperature changes. Star sensor faults include three modes: random noise increase, bias mutation, and output truncation. Torque wheel faults are described by friction torque changes and output torque saturation characteristics. The orbit control fault model includes characteristics such as thruster start-up delay, shutdown overshoot, and thrust attenuation, and the fault parameters are generated by 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.

[0030] This embodiment establishes a high-fidelity multi-satellite collaborative simulation system through precise modeling and reliable fault injection. This solution improves the simulation realism through multi-level optimization while ensuring the accuracy of time synchronization. The overall design fully considers the complexity of the space environment and achieves an accurate description of the actual system characteristics. Through a complete dynamic model and precise fault injection, a reliable simulation platform is provided for the verification of multi-satellite collaborative control algorithms, supporting the development and testing of key technologies such as fault diagnosis and fault-tolerant control.

[0031] Step S102: the simulation industrial computer collects telemetry data of each satellite system to be tested through a bus interface, the telemetry data including orbit parameters, attitude parameters, control parameters and payload parameters; the telemetry data is time-aligned according to the unified time and space 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; Optionally, this embodiment adopts a dual-card redundant structure when collecting data on the bus interface. The main acquisition card adopts the AceXtreme series PCIe interface 1553B card, and the backup acquisition card adopts the Mini-PCIe interface card. The two acquisition cards realize data interaction through the optical fiber backplane and support hot backup switching. The data collection process adopts a polling method to request data from each remote terminal on the 1553B bus according to a preset timing. The acquisition card is configured with a double buffer structure to achieve seamless data collection. When the terminal response times out, the data integrity is ensured by a retransmission mechanism.

[0032] This embodiment designs an accurate data analysis framework. The telemetry data frame adopts a hierarchical analysis structure. First, the frame is defined by the frame synchronization word, and then the time code, sequence number and data length field in the frame header are analyzed. The data area analysis adopts a table lookup method to extract the six orbital numbers, attitude quaternion, angular velocity vector, engine working status, torque wheel speed, sun sensor output, star sensor quaternion, GPS position speed, load working mode and other parameters according to the data identification code. The parameter analysis process includes range conversion and engineering value conversion to ensure that the physical meaning of the data is correct.

[0033] This embodiment implements a reliable data integrity verification mechanism. The verification process adopts multiple verification strategies, including frame synchronization check, CRC check, parameter validity check and timing continuity check. Parameter validity check is implemented through threshold judgment, and the threshold range is dynamically adjusted according to the satellite working mode. Timing continuity check is implemented through frame counting and time code comparison, and data repair is performed through interpolation method when frame loss is found.

[0034] This embodiment constructs an innovative time alignment algorithm. First, the time tags in the telemetry data are extracted and converted into timestamps under a unified space-time reference. The time conversion takes into account the leap second relationship between GPS time and UTC time, and accurate conversion is achieved through a table lookup method. The data resampling adopts the cubic spline interpolation method, and the interpolation nodes are adaptively selected through curvature calculation. The resampled data is smoothed by a sliding window, and the window length is dynamically adjusted according to the data change rate.

[0035] This embodiment designs an efficient data synchronization mechanism. The synchronization process first establishes a relative time series based on the reference satellite and calculates the time deviation of other satellite data relative to the reference series. The time deviation compensation adopts a linear fitting method, and the fitting coefficient is calculated by the least squares method. The compensated data is verified by cross-correlation analysis to verify the synchronization effect, and the correlation coefficient is used as a synchronization quality indicator.

[0036] This embodiment implements a reliable database writing strategy. The database uses the PostgreSQL time series database, and the table structure design includes fields such as timestamp index, satellite identifier, data type, data value, and quality mark. Data writing adopts batch processing mode, and efficient writing is achieved through the data buffer. Transaction processing adopts a two-phase commit protocol to ensure data consistency. Index optimization uses a B-tree structure to improve query efficiency.

[0037] This embodiment builds a complete data backup mechanism. Real-time data is recorded in log form, and incremental backup is performed regularly. The backup strategy is graded according to the importance of the data, and key data is stored in multiple copies. Data recovery uses a rollback mechanism to support data recovery at a specified time point. Storage space management adopts a circular overwrite strategy to automatically clean up expired data.

[0038] This embodiment establishes an efficient telemetry data processing system through precise data collection 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 collection, analysis, 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.

[0039] Step S103: 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 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.

[0040] Optionally, this embodiment uses a distributed query engine when reading data. The query process first builds a time window index, and decomposes the query task into multiple sub-queries through a parallel query optimizer. The sub-queries are executed in a vectorized manner, and data processing is accelerated through the SIMD instruction set. The data cache adopts a multi-level structure, with the L1 cache storing hot data and the L2 cache storing pre-fetched data to reduce database access latency.

[0041] This embodiment designs a hierarchical evaluation index system. System-level indicators include the accuracy of maintaining the geometric configuration of the formation, the timeliness of task completion, the efficiency of system resource utilization, etc., where the formation configuration is described by relative position and relative attitude, the task completion is verified by the key event sequence, and resource utilization is comprehensively evaluated through indicators such as energy, storage, and bandwidth. Subsystem-level indicators are subdivided into orbit control accuracy, attitude stability, payload work efficiency, etc. The orbit control accuracy is characterized by the position error ellipsoid, the attitude stability is measured by Euler angle deviation statistics, and the payload efficiency is evaluated by working timing and data quality. Component-level indicators include specific parameters such as propulsion system working characteristics, sensor measurement accuracy, and data transmission quality.

[0042] This embodiment implements an adaptive weight calculation mechanism. The weight calculation adopts the improved hierarchical analysis method. First, a judgment matrix is ​​established, and the matrix elements are dynamically updated through the task status. Expert experience knowledge is introduced through the fuzzy rule base to achieve the fusion 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.

[0043] This embodiment constructs an innovative data analysis framework. The analysis process first performs data preprocessing, including outlier detection, trend decomposition, and feature extraction. Outlier detection uses a local anomaly factor algorithm that considers the temporal and spatial correlation of the data. Trend decomposition uses an empirical mode decomposition method to extract multi-scale features of the data. Feature extraction is achieved through wavelet transform, selecting the most representative wavelet coefficients.

[0044] This embodiment designs an accurate error analysis method. The three-dimensional error curve is represented by parameters and smoothed by B-spline curve fitting. The inflection point extraction adopts the curvature extreme value method, and the robustness of inflection point detection is improved by multi-scale analysis. The curve matching adopts the dynamic time warping algorithm to handle the nonlinear scaling of time series data.

[0045] This embodiment implements intelligent event correlation analysis. The key event extraction adopts the sequence pattern mining method, and the frequent patterns are discovered through sliding window scanning. The event correlation analysis adopts the causal reasoning network, and the network structure is obtained through historical data learning. The time series matching adopts the graph matching algorithm, and the similarity of the event sequence is judged by subgraph isomorphism.

[0046] This embodiment builds a comprehensive evaluation report generation mechanism. Report generation adopts template design, dynamically adjusting the content structure according to the evaluation objectives. Data visualization adopts adaptive layout to support interactive display of multi-dimensional data. Text description adopts natural language generation technology to generate easy-to-read evaluation conclusions through template filling and grammatical rules.

[0047] This embodiment establishes an intelligent multi-satellite collaborative mission evaluation system through in-depth data analysis and a systematic evaluation system. While ensuring the comprehensiveness of the evaluation, the 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 conclusions. Through precise error analysis and intelligent event association, it provides a reliable performance evaluation and optimization basis for multi-satellite collaborative missions, supporting mission planning and decision optimization.

[0048] The evaluation system of this embodiment has good scalability and can dynamically adjust the indicator structure and weight distribution according to mission requirements. Through intelligent data analysis methods, an in-depth understanding of complex system behavior 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 the evaluation, and provides an important reference for performance improvement and fault prevention of space missions.

[0049] From the above description, it can be seen that the simulation evaluation method for multi-satellite collaborative tasks provided in the embodiment of the present application can establish a complete environmental disturbance model including solar pressure, atmospheric drag and geomagnetic field interference by constructing a unified space-time reference based on standard time signals. The fault injection mechanism for payload, attitude and orbit control is innovatively designed to achieve a true 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 sequences 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.

[0050] In one embodiment of the simulation evaluation method of the multi-satellite collaborative task of the present application, see Figure 2 , and can also include the following: Step S201: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, reads the internal timing signal of the timer, compares the phases of 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; Step S202: The simulation industrial computer establishes a multi-satellite collaborative mission 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 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 on the motion equation of the dynamics model.

[0051] Optionally, this embodiment adopts a high-precision clock synchronization architecture when receiving second pulses. The signal receiving circuit adopts a dual-channel design, the main channel is electrically isolated by a high-speed optocoupler, and the backup channel uses a digital isolator to provide redundant protection. The signal conditioning uses a high-speed operational amplifier to construct a bandpass filter, and the cut-off frequency is designed in the range of 0.5Hz to 1kHz to achieve signal shaping and noise suppression. The sampling circuit uses a high-precision comparator to accurately capture signal jumps through programmable thresholds.

[0052] This embodiment implements a precise internal timing mechanism. The timer uses a temperature compensated crystal oscillator as a reference source and generates a system clock through a multi-stage frequency division circuit. The frequency stability control uses a digital phase-locked loop, and fast locking is achieved through adaptive loop bandwidth. The phase comparison adopts a bilateral sampling method, and the measurement accuracy is improved through oversampling and digital filtering. The time synchronization error calculation adopts a sliding window average to eliminate the influence of random disturbances.

[0053] This embodiment designs an innovative clock calibration algorithm. The frequency calibration adopts a proportional integral controller, and the control parameters are optimized by the least square method. The phase calibration adopts the prediction-correction method, and the clock drift is predicted by the Kalman filter. The calibration process takes into account the temperature effect, and a temperature compensation model is established by polynomial fitting. The reference clock signal is generated by digital direct frequency synthesis technology to ensure the phase continuity of the output signal.

[0054] This embodiment builds a reliable space-time benchmark mapping mechanism. UTC time mapping adopts a two-way time transmission method, and eliminates the influence of transmission delay through multiple measurements. Leap second processing adopts a smooth transition strategy to avoid system disturbances caused by time jumps. Time scale conversion takes into account the relativistic effect, and high-precision synchronization is achieved through gravitational potential difference correction.

[0055] This embodiment realizes complete multi-satellite dynamics modeling. The motion equations are described by the Newton-Euler equations, and the state quantities include position vector, velocity vector, attitude quaternion and angular velocity vector. The orbital dynamics calculation is derived by variational method, considering the perturbations from J2 to J4. The attitude dynamics adopts the rigid body motion equations, and the singularity is avoided by quaternion representation.

[0056] This embodiment designs an accurate orbital element calculation method. The six orbital elements are obtained by position and velocity vector conversion, and the conversion process uses an iterative algorithm to improve accuracy. The Euler angle calculation uses quaternion conversion, and the calculation stability is ensured by singular value decomposition. The parameter update adopts a prediction-correction strategy, and the calculation efficiency is improved through multi-step numerical integration.

[0057] This embodiment constructs a comprehensive environmental disturbance model. The solar light pressure model considers the optical properties of each facet on the satellite surface and calculates the photon reflection effect through the Monte Carlo method. The atmospheric drag calculation adopts the NRLMSISE-00 model, considering the influence of solar activity and geomagnetic disturbance. The geomagnetic field model adopts the IGRF series expansion and improves the calculation efficiency through the recursive algorithm.

[0058] This embodiment realizes efficient construction of disturbance matrix. Parallel processing architecture is used for matrix element calculation, and multi-threaded acceleration is achieved through OpenMP. Variable step size algorithm is used for numerical integration, and accuracy is controlled by local truncation error. Disturbance superposition adopts sub-item calculation strategy, and various influences are balanced by weight coefficients.

[0059] This embodiment realizes a high-fidelity multi-satellite simulation system through accurate space-time benchmark construction and reliable dynamic modeling. This solution improves the model realism through multi-level optimization on the basis of ensuring time synchronization accuracy. The overall design fully considers the complexity of the space environment and realizes an accurate description of the actual system characteristics.

[0060] The time and space reference of this embodiment has high stability and traceability, providing a unified time reference for multi-satellite coordinated control. The dynamic model achieves an accurate description of the satellite motion law through complete disturbance modeling, providing reliable data support for subsequent simulation evaluation. The implementation of this solution significantly improves the authenticity and reliability of the simulation system, and provides an important basis for space mission planning and control strategy optimization.

[0061] In one embodiment of the simulation evaluation method of the multi-satellite collaborative task of the present application, see Figure 3 , and can also include the following: Step S301: 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; Step S302: The simulation industrial 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 a state transition probability matrix for 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.

[0062] Optionally, this embodiment uses a high-precision numerical integration framework. The Runge-Kutta integrator uses an 8th-order formula and calculates the truncation error through an embedded 5th-order solution. The integral step size control uses a PI controller and is dynamically adjusted based on the local error estimate. To improve computational efficiency, the integrator supports parallel computing and processes the state vector in blocks, each of which contains position, velocity, and attitude parameters. The calculation process uses double-precision floating-point operations to ensure numerical stability.

[0063] This embodiment implements an adaptive step size control mechanism. The step size selection is based on a unified spatiotemporal reference and is automatically adjusted through an error tolerance. The initial step size is estimated through the system characteristic time to avoid numerical oscillation. The step size is automatically subdivided at key event points to ensure accurate capture of important state transitions. The step size adjustment adopts a smooth transition strategy to avoid numerical instability caused by sudden changes.

[0064] This embodiment designs an accurate orbital element calculation method. The calculation of the variation takes into account the comprehensive influence of the perturbation force, including the non-spherical gravity of the earth, atmospheric drag, and solar light pressure. The calculation process is derived using the variational method, and the evolution of the elements is described by the Gaussian variational equation. The root number update adopts a prediction-correction strategy, and the calculation efficiency is improved through multi-step extrapolation.

[0065] This embodiment constructs an innovative orbit prediction model. The prediction process adopts an analytical-numerical hybrid method, with analytical solutions for the main terms and numerical integration for the perturbation terms. The position vector is calculated by solving the Kepler equation using Newton-Raphson iteration. The velocity vector is obtained by orbital root inversion, taking velocity distortion correction into account. The attitude vector is predicted using quaternion integration, and constraints are maintained using the Lie group method.

[0066] This embodiment realizes reliable fault model construction. The load switch fault model considers three modes: switch stuck, poor contact and delayed response. The state transition probability is obtained by maximum likelihood estimation based on historical data statistics. The model considers the influence of temperature and aging effects on the fault probability and adjusts the transfer matrix through correction factors.

[0067] This embodiment designs an accurate sensor drift model. Drift faults are described by a random walk process, and the drift rate is related to temperature changes. The model includes two parts: deterministic drift and random drift, and the state transition is described by a Markov jump process. Parameter estimation uses an expectation maximization algorithm and is obtained through historical data training.

[0068] This embodiment constructs a comprehensive engine fault model. Start-stop faults include three types of characteristics: start delay, shutdown overshoot, and thrust attenuation. State transitions are described using a multi-layer Markov chain, taking into account the cumulative effect and recovery characteristics of faults. Fault parameter generation is achieved through the Monte Carlo method, maintaining physical constraints.

[0069] This embodiment implements 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. Fault parameter writing adopts an event-driven approach, and the time point is located by binary search. Data updates use atomic operations to ensure concurrency safety.

[0070] This embodiment designs a complete data management architecture. The simulation data adopts a hierarchical storage structure to support fast retrieval by time and satellite number. Data compression adopts a lossless algorithm and reduces storage space through differential coding. Data consistency is guaranteed by a checksum mechanism, which supports detection and recovery of damaged data.

[0071] This embodiment establishes a high-fidelity multi-satellite collaborative simulation system through precise numerical calculation and reliable fault injection. This solution improves simulation efficiency through multi-level optimization on the basis of ensuring calculation accuracy. The overall design fully considers the complexity of the space system and realizes an accurate description of the actual working characteristics.

[0072] 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 achieves an accurate description of various faults through Markov chains, providing a test platform for fault diagnosis and fault-tolerant control. The implementation of this solution significantly improves the authenticity and availability of the simulation system, providing important support for space mission planning and control strategy optimization.

[0073] In one embodiment of the simulation evaluation method of the multi-satellite collaborative task of the present application, see Figure 4 , and can also include the following: Step S401: 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; Step S402: 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 switch quantity, sensor output value and load working status into a telemetry data stream according to the time series.

[0074] Optionally, this embodiment adopts a double buffer architecture during bus configuration. The bus controller adopts the AceXtreme series PCIe interface card to support high-speed data acquisition and real-time processing. The message block descriptor is organized in a linked list manner, and each descriptor contains a message type, data length, sub-address and time tag. The cache management adopts a circular queue structure and realizes seamless data switching through a dual pointer mechanism. The configuration process is directly programmed through hardware registers to reduce software overhead.

[0075] 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 timing. 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 the DMA channel. The timeout processing adopts a watchdog mechanism to automatically resend the failed data request.

[0076] This embodiment designs an accurate data analysis framework. Frame analysis first identifies the frame boundary through the synchronization word, and then extracts the time code, sequence number and data length in the frame header. Parameter analysis uses a table lookup method to extract specific parameters based on the subaddress and data identifier. Track root number analysis considers range conversion and restores physical quantities through calibration coefficients. Attitude parameters are represented by quaternions and visualized through Euler angle conversion.

[0077] 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 uses hardware acceleration and supports polynomial parallel calculation. Parameter verification is determined by threshold value, and the threshold range is dynamically adjusted according to the working mode.

[0078] This embodiment implements an accurate time mapping algorithm. The time label is first converted into a timestamp under a unified time and space reference. The conversion process takes into account the leap second relationship between GPS time and UTC time. Time synchronization uses the relative time scale method and eliminates system delays through linear fitting. Time series reconstruction uses cubic spline interpolation to maintain data continuity.

[0079] 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. Parameter resampling uses linear interpolation, and interpolation nodes are adaptively selected through curvature calculation. Data smoothing uses sliding window processing, and the window length is dynamically adjusted according to the data change rate.

[0080] This embodiment builds a complete data flow management architecture. The data flow is organized in pipeline mode, supporting multi-level cache and parallel processing. Data compression uses differential coding, and lossless compression is achieved through entropy coding. Flow control uses a token bucket algorithm to prevent data bursts from causing buffer overflows.

[0081] This embodiment implements a reliable exception handling mechanism. Anomaly detection includes three categories: data loss, parameter limit violation, and timing anomaly. Lost data is repaired by interpolation, and the interpolation method is selected according to the data characteristics. The limit violation parameters are processed by marking to retain the original information. The timing anomaly is resolved by reordering to ensure the causality of the data.

[0082] This embodiment designs a comprehensive data quality assessment system. Quality assessment includes three dimensions: completeness, accuracy, and timeliness. Completeness is evaluated through data frame statistics, accuracy is determined through noise analysis, and timeliness is calculated through delay statistics. The assessment results serve as data credibility indicators to guide subsequent processing.

[0083] 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 full process management from acquisition to reconstruction.

[0084] The bus communication architecture of this embodiment has high reliability and high real-time performance, providing reliable guarantee for multi-satellite telemetry data collection. 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 collection, providing high-quality data support for subsequent performance evaluation.

[0085] In one embodiment of the simulation evaluation method of the multi-satellite collaborative task of the present application, see Figure 5 , and can also include the following: Step S501: 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 telemetry data with unequal intervals, resamples the interpolated data into an equal interval 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; Step S502: The simulation industrial 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.

[0086] Optionally, this embodiment adopts an adaptive interpolation architecture when processing data. First, the distribution characteristics of the sampling interval are calculated by time difference to establish an irregular sampling point sequence. The cubic spline interpolation adopts a segmented construction method to ensure the continuity of the curve through boundary condition constraints. The node selection adopts local curvature calculation, and the sampling points are encrypted where the data changes drastically. The interpolation coefficients are solved by the catch-up method to solve the tridiagonal equations to improve the calculation efficiency.

[0087] This embodiment implements an accurate resampling mechanism. The resampling period is determined by Shannon's sampling theorem, taking into account the highest frequency component of the data. The sampling point generation uses a linear phase filter to avoid phase distortion. The data reconstruction uses sinc function interpolation and the ringing effect is suppressed by the windowing function. In order to improve the computational efficiency, the fast Fourier transform is used to implement frequency domain resampling.

[0088] This embodiment designs an innovative time deviation compensation algorithm. The compensation process first establishes a reference time series and fits the time deviation trend by the least squares method. The fitting model takes into account linear drift and periodic changes, and the model parameters are obtained by iterative optimization. The compensation amount is calculated using piecewise linear interpolation to ensure the continuity of time correction.

[0089] This embodiment builds a reliable data quality assessment 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 adaptive thresholds. Data integrity assessment uses missing rate statistics to consider data distribution within the time window.

[0090] This embodiment realizes efficient database table structure design. The table structure adopts star mode, the main table stores time series data, and the dimension table contains satellite information and parameter types. The index design adopts composite index to optimize query performance. The partition strategy is based on time range and supports historical data archiving.

[0091] This embodiment designs an accurate data quality marking mechanism. The quality marking 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 delay statistics. The marking results are bitmap encoded to support fast retrieval.

[0092] This embodiment builds a reliable database writing strategy. Batch writing uses precompiled statements and parameterized queries to improve execution efficiency. Transaction management uses ACID features and ensures data consistency through two-phase commit. Concurrency control uses a multi-version concurrency control mechanism to improve concurrency performance.

[0093] This embodiment implements a complete data backup mechanism. The backup strategy uses incremental backup and records changes through binary logs. Data recovery supports point-in-time recovery and transaction rollback through rollback logs. Storage optimization uses compressed storage and reduces storage space through dictionary encoding.

[0094] This embodiment designs an intelligent index optimization mechanism. Index selection is based on query pattern analysis, and the index effect is evaluated through the execution plan. Index maintenance uses online reconstruction to avoid service interruptions caused by table locks. Statistics updates use adaptive sampling to ensure that the optimizer selects the appropriate execution plan.

[0095] This embodiment establishes an efficient evaluation data processing system through precise data processing and reliable storage management. While ensuring data quality, this solution improves processing efficiency through multi-level optimization. The overall design fully considers the characteristics of spatial data and realizes full process optimization from data alignment to storage management.

[0096] 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 data consistency and availability 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 accurate management and efficient use of space mission data.

[0097] In one embodiment of the simulation evaluation method of the multi-satellite collaborative task of the present application, see Figure 6 , and can also include the following: Step S601: 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; Step S602: The simulation industrial computer constructs a hierarchical evaluation index system based on the data preprocessing matrix, sets formation maintenance accuracy, task completion rate, and resource utilization 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.

[0098] Optionally, this embodiment adopts a distributed processing architecture when executing queries. The time window is constructed using 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 a consistent hashing algorithm. Distributed queries use the MapReduce mode to reduce network transmission overhead.

[0099] This embodiment implements an efficient data preprocessing mechanism. Data grouping first establishes a primary index according to the satellite number, and then sub-groups according to the data type. Statistics are calculated using an online algorithm, and the mean and standard deviation are calculated through a single scan. Outlier processing uses 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 usage is reduced through a compressed format.

[0100] This embodiment designs an innovative indicator system construction method. Among the system-level indicators, the formation keeping accuracy is evaluated by relative position error, taking into account geometric configuration and dynamic characteristics. The task completion rate is analyzed by key event sequence, combined with time constraints and functional requirements. Resource utilization is evaluated through multiple dimensions, including energy efficiency, storage utilization, and bandwidth occupancy.

[0101] This embodiment builds an accurate subsystem-level evaluation framework. The orbit control accuracy is characterized by the position error ellipsoid, and the control characteristics are determined by the main axis analysis. The attitude stability is calculated by the Euler angle deviation statistics, considering the influence of angular velocity and angular acceleration. The payload work efficiency is evaluated by the task execution characteristics, including response time and data quality.

[0102] This embodiment implements a reliable component evaluation mechanism. The engine operating status evaluation includes startup characteristics, thrust stability, and shutdown performance. Sensor error analysis uses the Allan variance method to identify the contribution of various error sources. Data transmission quality is evaluated by bit error rate and delay statistics, taking into account the influence of channel characteristics.

[0103] This embodiment designs a complete indicator calculation system. The calculation formula adopts modular design, supports parameterized configuration and dynamic adjustment. Numerical calculation adopts adaptive algorithm, and selects appropriate calculation method according to data characteristics. Threshold setting adopts multi-level judgment mechanism, and flexible evaluation is achieved through fuzzy rules.

[0104] This embodiment builds an intelligent indicator library management system. The indicator library adopts a hierarchical structure and describes the dependencies between indicators through a relational model. The indicator definition supports version control and tracks the change history through difference comparison. The indicator reuse adopts a template mechanism to simplify the definition process of new indicators.

[0105] This embodiment implements an efficient data access mechanism. Multi-level cache is used for index query, and access delay is reduced through pre-fetch mechanism. Data update is incremental, and only the changed part is processed. Concurrency control adopts read-write lock mechanism to improve concurrent access efficiency.

[0106] This embodiment designs a reliable indicator verification mechanism. The verification process includes value range checking, physical constraint verification, and logical relationship judgment. Exception handling adopts a multi-level fault tolerance strategy and ensures system availability through a backup mechanism. Result feedback adopts real-time monitoring and supports dynamic adjustment of evaluation strategies.

[0107] This embodiment establishes a comprehensive multi-satellite evaluation framework through precise data processing and a systematic indicator system. While ensuring the comprehensiveness of the evaluation, the solution improves processing efficiency through multi-level optimization. The overall design fully considers the complexity of the space system and realizes a complete link from data preprocessing to indicator evaluation.

[0108] The evaluation architecture of this embodiment is highly scalable and adaptable, providing a reliable guarantee for multi-satellite system evaluation. The indicator system is implemented 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 accurate evaluation and effective management of complex aerospace systems.

[0109] In one embodiment of the simulation evaluation method of the multi-satellite collaborative task of the present application, see Figure 7 , and can also include the following: Step S701: 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; Step S702: 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.

[0110] Optionally, this embodiment adopts an adaptive weight calculation framework in hierarchical analysis. The task execution status 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 of 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 check is evaluated by random consistency indicators to ensure the rationality of the judgment.

[0111] This embodiment implements an innovative weight optimization mechanism. The system-level indicator weights are determined by task criticality analysis, taking into account task objectives and constraints. The subsystem-level indicator weights are calculated based on functional dependencies, and the degree of impact is determined through sensitivity analysis. The component-level indicator weights are dynamically adjusted based on the data volatility characteristics using the entropy weight method. The weight combination uses a linear weighting method, and the weight sum is ensured to be 1 through normalization.

[0112] This embodiment designs an accurate evaluation model construction method. Model construction first establishes the mathematical expression of indicator calculation and ensures physical consistency through dimensional analysis. Parameter setting adopts an adaptive mechanism and is dynamically adjusted according to task characteristics. Model verification evaluates the prediction ability and stability of the model through historical data backtesting.

[0113] This embodiment builds a reliable error analysis framework. The three-dimensional position error adopts ellipsoid error analysis, and the error distribution characteristics are determined by the principal axis transformation. The attitude error is calculated by quaternion difference, and the Euler angle singular point processing is considered. The error statistics adopts non-parametric methods and describes the error distribution through kernel density estimation.

[0114] This embodiment implements an intelligent curve analysis mechanism. Adaptive sampling is used for curve drawing, and sampling points are encrypted in key areas. Wavelet transform method is used for inflection point detection, and feature points are extracted through multi-scale analysis. Interpolation algorithm is used for coordinate extraction to improve the accuracy of inflection point positioning. Local regression method is used for curve smoothing to eliminate the influence of random fluctuations.

[0115] This embodiment designs a complete event correlation analysis system. The key event extraction adopts time series pattern mining, and the typical pattern is identified through sequence alignment. The event classification adopts hierarchical clustering, and the event family is established through similarity measurement. The mapping relationship is established by association rule mining, and the causal relationship between errors and events is identified.

[0116] This embodiment builds a comprehensive evaluation report generation mechanism. The indicator score calculation adopts the fuzzy evaluation method, and the qualitative and quantitative conversion is realized through the membership function. The error analysis adopts multi-dimensional representation, including statistical characteristics and dynamic characteristics. The event description is generated in natural language, and the readability report is generated by template filling.

[0117] This embodiment implements an efficient visualization display framework. Error curve drawing adopts responsive design and supports multi-resolution display. Graphic interaction adopts event-driven mechanism to support data drilling and detail zooming. The annotation system adopts intelligent layout to avoid label overlap.

[0118] This embodiment designs a reliable quality control mechanism. Data quality control is achieved through anomaly detection, identifying and marking suspicious data points. The calculation process uses numerical stability checks and condition number analysis to evaluate the calculation accuracy. The result verification uses a cross-validation method to evaluate the reliability of the evaluation results.

[0119] This embodiment establishes a comprehensive performance evaluation system through precise hierarchical analysis and systematic error evaluation. While ensuring the accuracy of the evaluation, the solution improves the analysis efficiency through multi-level optimization. The overall design fully considers the complexity of the space mission and realizes the complete process from data analysis to result display.

[0120] The evaluation system of this embodiment has high accuracy and high interpretability, providing a reliable basis for multi-satellite system evaluation. 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 performance optimization and decision support of space missions, and realizes accurate evaluation and in-depth understanding of complex aerospace systems.

[0121] In order to accurately evaluate the performance of multi-satellite collaborative missions and provide a reliable decision-making basis for space mission planning and system optimization, the present application provides an embodiment of a simulation evaluation device for multi-satellite collaborative missions for implementing all or part of the contents of the simulation evaluation method for multi-satellite collaborative missions, see Figure 8 The simulation evaluation device for the multi-satellite collaborative task specifically includes the following contents: The simulation model construction module 10 is used to simulate the industrial computer receiving the standard time signal sent by the pulse per second controller, and construct a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar light pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance 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 20 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 and space 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; The task indicator evaluation module 30 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 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.

[0122] From the above description, it can be seen that the simulation evaluation device for multi-satellite collaborative tasks provided in the embodiment of the present application can establish a complete environmental disturbance model including solar pressure, atmospheric drag and geomagnetic field interference by constructing a unified space-time reference based on standard time signals. The fault injection mechanism for payload, attitude and orbit control is innovatively designed to achieve a true 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 sequences 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.

[0123] From the hardware level, in order to achieve accurate evaluation of the performance of multi-satellite collaborative missions and provide a reliable decision-making basis for space mission planning and system optimization, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the simulation evaluation method of the multi-satellite collaborative mission, and the electronic device specifically includes the following contents: Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the simulation evaluation device of multi-satellite collaborative tasks and related devices such as core business systems, user terminals and related databases; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., and the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the simulation evaluation method of multi-satellite collaborative tasks in the embodiment, and the embodiment of the simulation evaluation device of multi-satellite collaborative tasks, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0124] It is understandable 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.

[0125] In practical applications, part of the simulation evaluation method of the multi-satellite collaborative task can be executed on the electronic device side as described above, or all operations can be completed in the client device. The selection can be made based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0126] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0127] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0128] In one embodiment, the simulation evaluation method function of the multi-satellite collaborative task can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: Step S101: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, and builds a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to build an environmental disturbance 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 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 collaborative motion simulation data; Step S102: the simulation industrial computer collects telemetry data of each satellite system to be tested through a bus interface, the telemetry data including orbit parameters, attitude parameters, control parameters and payload parameters; the telemetry data is time-aligned according to the unified time and space 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; Step S103: 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 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.

[0129] From the above description, it can be seen that the electronic device provided in the embodiment of the present application establishes a complete environmental disturbance model including solar pressure, atmospheric drag and geomagnetic field interference by constructing a unified space-time reference based on standard time signals. The fault injection mechanism of payload, attitude and orbit control is innovatively designed to achieve a true 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 sequences 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.

[0130] In another embodiment, the simulation and evaluation device for multi-satellite collaborative tasks can be configured separately from the central processing unit 9100. For example, the simulation and evaluation device for multi-satellite collaborative tasks can be configured as a chip connected to the central processing unit 9100, and the simulation and evaluation method functions of multi-satellite collaborative tasks can be implemented through the control of the central processing unit.

[0131] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0132] like Fig. 9 As 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 device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0133] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0134] The input unit 9120 provides input 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.

[0135] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0136] The memory 9140 may also include a data storage unit 9143 for storing 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 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0137] The communication module 9110 is a transmitter / receiver that sends 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 may be the same as the case of a conventional mobile communication terminal.

[0138] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may 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 through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0139] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the simulation evaluation method for a multi-satellite collaborative task whose execution subject is a server or a client in the above embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all steps of the simulation evaluation method for a multi-satellite collaborative task whose execution subject is a server or a client in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented: Step S101: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, and builds a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to build an environmental disturbance 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 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 collaborative motion simulation data; Step S102: the simulation industrial computer collects telemetry data of each satellite system to be tested through a bus interface, the telemetry data including orbit parameters, attitude parameters, control parameters and payload parameters; the telemetry data is time-aligned according to the unified time and space 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; Step S103: 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 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.

[0140] As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application establishes a complete environmental disturbance model including solar pressure, atmospheric drag and geomagnetic field interference by constructing a unified space-time reference based on a standard time signal. The fault injection mechanism for payload, attitude and orbit control is innovatively designed to achieve a true 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 the three-dimensional error curve and the key event sequence to achieve an accurate evaluation of the performance of multi-satellite collaborative tasks, providing a reliable decision-making basis for space mission planning and system optimization.

[0141] The embodiments of the present application also provide a computer program product capable of implementing all steps of the simulation evaluation method for multi-satellite collaborative tasks in the above embodiments, wherein the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the simulation evaluation method for multi-satellite collaborative tasks are implemented. For example, the computer program / instruction implements the following steps: Step S101: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, and builds a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to build an environmental disturbance 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 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 collaborative motion simulation data; Step S102: the simulation industrial computer collects telemetry data of each satellite system to be tested through a bus interface, the telemetry data including orbit parameters, attitude parameters, control parameters and payload parameters; the telemetry data is time-aligned according to the unified time and space 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; Step S103: 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 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.

[0142] From the above description, it can be seen that the computer program product provided in the embodiment of the present application establishes a complete environmental disturbance model including solar pressure, atmospheric drag and geomagnetic field interference by constructing a unified space-time reference based on standard time signals. The fault injection mechanism of payload, attitude and orbit control is innovatively designed to achieve a true 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 sequences 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.

[0143] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take 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.

[0144] The present invention is described with reference to 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 process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0147] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on 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, and builds a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to build an environmental disturbance 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 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 collaborative motion simulation data; The simulation industrial computer collects 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 and space 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; 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 computer receives the standard time signal sent by the pulse per second controller, and builds a unified space-time reference based on the standard time signal; the simulation industrial computer establishes a multi-satellite collaborative 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 light pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamic model to construct an environmental disturbance model, including: The simulation industrial computer receives the standard time signal sent by the pulse per second controller, reads the internal timing signal of the timer, compares the phases 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; The simulation industrial computer establishes a multi-satellite collaborative mission dynamics model based on Newton's mechanics equations, takes 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 on the motion equation of the dynamics model.

3. The simulation evaluation method for multi-satellite collaborative mission 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.

4. 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.

5. The simulation evaluation method for multi-satellite collaborative mission according to claim 1, 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.

6. 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.

7. 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.

8. 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 second pulse controller, and to construct a unified space-time reference based on the standard time signal; the simulation industrial 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 computer inputs solar pressure, atmospheric resistance, and geomagnetic field interference parameters into the dynamics model to construct an environmental disturbance 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 and space 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; 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.

9. 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 7 are implemented.

10. 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 7 are implemented.

Images