Star-ground collaborative task planning-oriented remote sensing system digital model integration modeling method

Abstract

The application discloses a remote sensing system digital model integration modeling method for a star-ground cooperative task planning, abstracts the remote sensing system into a digital system composed of a running management model, an orbit model, a task planning model, a posture control model, a target environment model, an atmospheric correction model, a load model, a data transmission model, a power supply and distribution model, an on-board processing model and a task efficiency model, and reflects a process of solving key constraints faced by the remote sensing system through dynamic adaptive iteration optimization, is close to a star-ground application whole process, realizes fine calculation and estimation on process running conditions, and meets the needs of star-ground key technical index verification.

Description

Technical Field

[0001] This invention relates to the field of digital modeling, and in particular to an integrated modeling method for digital models of remote sensing systems for satellite-ground collaborative mission planning. Background Technology

[0002] High-resolution optical remote sensing systems are characterized by rich image texture information, simple image interpretation and application, large detection range, and fast information acquisition, playing an important role in high-resolution Earth observation applications.

[0003] Currently, the number of targets imaged by high-resolution remote sensing satellites is rapidly increasing daily, and the volume of operational data is growing exponentially to tens of gigabits per second. Multiple mission requirements and imaging modes necessitate comprehensive optimization of satellite mission planning, orchestration, and scheduling, as well as dynamic management of data transmission, agile attitude control, energy management, and onboard storage. Furthermore, remote sensing satellites typically plan missions on a time-segment basis, and these plans must simultaneously meet the requirements of multiple missions, high dynamism, and high precision. This necessitates highly coordinated management of various satellite subsystems, such as attitude maneuvering, imaging control, and data transmission, placing higher demands on satellite-ground collaborative management. Because the ground cannot predict the satellite's status in advance, onboard resource utilization is low, dynamic management and optimization are impossible, and the satellite's capabilities cannot be fully utilized. This results in insufficient satellite-ground coordination during satellite use, leading to wasted onboard resources.

[0004] As satellite imaging capabilities improve, usage and maneuvering modes become increasingly complex, leading to a sharp increase in imaging data volume and energy consumption. The execution of imaging missions determines data transmission planning, energy status, and storage status. Therefore, satellites in orbit face a complex interplay of constraints related to imaging quality, energy, and data transmission. From the perspective of space-ground collaborative mission planning, efficient and precise verification methods are needed. On the one hand, this aims to maximize satellite capabilities, pursuing multiple missions with high-quality imaging. On the other hand, prior to mission deployment, it is necessary to conduct operational verification on the ground through parallel simulations to ensure mission success rates and guarantee the satellite's usability and ease of use.

[0005] Based on digital means, abstracting the capabilities of each subsystem of space and ground into digital models and simulating the state and operating environment of space and ground in digital space to plan space-ground collaborative missions is an important development trend. However, the granularity and realism of digital models are limited. Therefore, the functional characteristics of each digital model, as well as the logical interaction and dynamic adaptation between models, are the focus of modeling during the digital model integration modeling process.

[0006] Therefore, in order to resolve the contradiction between the on-orbit mission plan of high-resolution remote sensing satellite systems and the on-board operating status and constraints, and to meet the need for refined calculation and prediction of satellite operating status and processes, it is urgent to propose corresponding solutions. Summary of the Invention

[0007] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide an integrated modeling method for digital models of remote sensing systems for satellite-ground collaborative mission planning. Through dynamic adaptation and iterative optimization, it truly reflects the process of solving key constraints faced by remote sensing systems, closely follows the entire process of satellite-ground applications, and enables refined calculation and prediction of process operation, thus meeting the needs of verifying key technical indicators of satellite-ground systems.

[0008] The technical solution of this invention is: a method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning, comprising the following steps:

[0009] (1) The remote sensing system is abstracted into an operation management model, an orbit model, a mission planning model, an attitude control model, a target environment model, an atmospheric correction model, a payload model, a data transmission model, a power supply and distribution model, an on-board processing model, and a mission performance model;

[0010] (2) Design the collaborative mode and interaction logic of each digital model, simulate the satellite-to-ground process of the remote sensing system, and perform detailed calculations and predictions on the process operation, as follows:

[0011] (2.1) The operation management model receives the initial configuration information, configures the task scenario and each digital model, determines all imaging tasks within the simulation period, and forms a task pool;

[0012] (2.2) Based on the task pool, the track model calculates the track information and arc information during the simulation period;

[0013] (2.3) The task planning model performs autonomous task planning based on arc information to generate a task sequence; the attitude control model performs attitude calculations for each imaging task in the task sequence to obtain an attitude sequence.

[0014] (2.4) The target environment model calculates the target radiation characteristics, the atmospheric correction model calculates the atmospheric correction coefficient, the target radiation characteristics and atmospheric correction coefficient are input into the payload model to estimate the imaging quality, and the imaging quality pre-evaluation index of each imaging task in the task sequence is obtained; it is determined whether the imaging quality pre-evaluation index of each imaging task meets the preset imaging quality expectation; if it does not meet the expectation, the imaging task that does not meet the expectation is deleted, the task pool is updated, and the process returns to step (2.2); if all the expectations are met, the process continues to step (2.5).

[0015] (2.5) The data transmission model calculates the total imaging data volume of each imaging task in the task sequence based on the orbit information and attitude sequence, and plans and arranges the data transmission tasks; it determines whether the on-board data dynamic balance is satisfied during the execution of each imaging task and data transmission task; if not, it deletes low-priority imaging tasks according to task priority, updates the task pool, and returns to step (2.2); if satisfied, it continues to step (2.6).

[0016] (2.6) The power supply and distribution model calculates the power generation and consumption at each moment during the simulation period based on the orbit information and attitude sequence; it determines whether the dynamic balance of on-board energy is satisfied during the entire simulation period; if not, it deletes low-priority imaging tasks according to task priority, updates the task pool, and returns to step (2.2); if satisfied, it continues to step (2.7).

[0017] (2.7) Based on the feasible task sequence formed by iteration and the calculation results of the above digital models, the on-board processing model predicts the imaging performance, and the task performance model evaluates the task performance throughout the entire simulation period.

[0018] Furthermore, in step (2.1):

[0019] The initial configuration information includes two categories: task scenario information and model initialization information. The task scenario information includes target, task, and environment information, while the model initialization information includes preset parameters and simulation conditions required for each digital model simulation process.

[0020] Furthermore, the target information includes, but is not limited to, target location information and target type information; the task information includes, but is not limited to, the priority of each task and the imaging mode of each task; the environmental information includes, but is not limited to, surface information and atmospheric information; and the simulation conditions include, but are not limited to, simulation start and end times and simulation step size.

[0021] Furthermore, in step (2.2):

[0022] The orbit model extrapolates the orbit information within the simulation period set in step (2.1) based on the satellite's initial orbit epoch and initial orbit six-element number. Based on the satellite's transit conditions and its pitch and yaw maneuvers, the target's visible arc segment, the ground data transmission station's visible arc segment, and the relay satellite's visible arc segment are calculated.

[0023] Furthermore, in step (2.3):

[0024] The task planning model autonomously plans tasks based on the target visible arc segments in the arc segment information of step (2.2) to generate a task sequence, as follows:

[0025] The mission planning model decomposes the complex imaging mode into meta-tasks based on the imaging working mode set in step (2.1). Under the constraints of the satellite's maximum maneuver angle and maneuver speed, and in combination with the imaging attitude requirements, the meta-tasks are planned as specific observation strips within the target's visible arc, and the start and end times of each imaging mission and the latitude and longitude of the camera pointing to the ground at the start and end times are obtained.

[0026] Furthermore, in step (2.3):

[0027] The attitude control model calculates the attitude sequence based on the visible arc segments of the ground data transmission station and the visible arc segments of the relay satellite in the arc segment information of step (2.2), as well as the mission sequence. The specific method is as follows:

[0028] The attitude control model plans specific data transmission arcs based on the visible arcs of ground data transmission stations and relay satellites. Then, the specific information of the mission sequence is input into the attitude control model, which calculates the complete attitude of the imaging mission segment, the attitude of the data transmission mission segment, and the attitude of the remaining time periods according to the preset attitude maneuver strategy.

[0029] Furthermore, in step (2.4):

[0030] Based on the target location information, target type information, surface information and atmospheric information preset for different targets in step (2.1), the target environment model calculates the target radiation characteristics, which are characterized by the radiance or radiance of the stationary target on the ground at the time of the imaging mission; the atmospheric correction model calculates the atmospheric correction coefficient, which is characterized by the correction accuracy of the reflectivity of the target area.

[0031] By inputting the target radiation characteristics and atmospheric correction coefficients into the payload model, the imaging quality pre-evaluation indexes for each imaging task in the mission sequence are obtained, characterized by modulation transfer function, positioning accuracy, spatial resolution, spectral resolution, and signal-to-noise ratio.

[0032] The imaging quality pre-evaluation index of each imaging task is compared with the preset imaging quality expectation threshold in step (2.1). If it is lower than the threshold, it is considered that the preset imaging quality expectation is not met, the corresponding imaging task is deleted from the task pool, and the task pool is updated.

[0033] Furthermore, the target environment model, based on target type information including target material reflectivity, transmittance, emissivity, and temperature, as well as surface information including the environmental background reflectivity, transmittance, emissivity, temperature, and surface elevation of the target location, uses the MODTRAN atmospheric radiative transfer model to calculate the surface radiance or radiance of the stationary target at the mission time.

[0034] Furthermore, the atmospheric correction model calculates the correction accuracy of the reflectivity of the target area based on atmospheric information, including the aerosol pattern, optical thickness, and atmospheric profile at the target's location, combined with the imaging angle and imaging time.

[0035] Furthermore, in step (2.5):

[0036] The data transmission model calculates the velocity-to-height ratio of imaging based on the trajectory information of each imaging task in the task sequence and the attitude of the imaging task segment in the attitude sequence. Then, it calculates the imaging integration time of each spectral segment of each imaging task. Furthermore, it combines the preset parameters of the data transmission model in step (2.1) to calculate the total data volume of each spectral segment of a single imaging task. The total data volume of each spectral segment of each imaging task is accumulated to obtain the final total imaging data volume.

[0037] Then, based on the start and end times of the data transmission arc segment planned in step (2.3) and the total amount of imaging data calculated, the data transmission task is planned and arranged to determine the data to be transmitted for each data transmission task.

[0038] Then, according to the simulation step size preset in step (2.1), the on-board real-time data storage volume is calculated step by step; it is determined whether the on-board real-time data storage volume exceeds the total on-board solid storage capacity. If it does, it means that the on-board data dynamic balance is not satisfied. One low-priority task is deleted from the task pool and the task pool is updated.

[0039] The calculation method for the real-time data storage volume on the satellite is as follows: the sum of the data volume generated by the imaging task up to the current simulation time, the data occupied at the initial moment on the satellite, and the data occupied by other satellite engineering parameters, minus the data volume already transmitted in the data transmission task up to the current simulation time.

[0040] Furthermore, in step (2.6):

[0041] The power supply and distribution model calculates the charging power at the current simulation moment, taking into account the influence of the shadow area and the sunlight area, based on the track information and attitude sequence.

[0042] Then, according to the simulation step size preset in step (2.1), the integral of charging power with respect to time is calculated step by step to obtain the total power generation, and then the total power consumption is obtained to calculate the real-time battery capacity percentage on the satellite.

[0043] Determine if the real-time battery capacity percentage on the satellite is greater than the maximum allowable depth of discharge. If it is, it means that the dynamic balance of onboard energy is not met. Delete one low-priority task from the task pool and update the task pool.

[0044] The onboard real-time battery capacity percentage is defined as the ratio of the total power consumption up to the current simulation time to the sum of the battery capacity and total power generation at the initial moment on the satellite.

[0045] Furthermore, the power supply and distribution model, based on track information and attitude sequence, calculates the charging power at the current simulation moment, taking into account the influence of the shadow area and the sunlit area. The specific method is as follows:

[0046] The attitude angles of the satellite at each moment in the attitude sequence are obtained. Based on the relative position of the satellite in orbit and the sun, the projection direction of the sunlight is calculated, and the shading of the solar array by the satellite body is calculated to obtain the unshaded solar array panels. The solar incident vector on the unshaded solar array panels at each moment is calculated. Under the full adjustment mode, the relationship between the illumination angle of the unshaded solar array panels and the short-circuit current is calculated to obtain the charging power provided by the solar array at the current simulation moment.

[0047] Furthermore, in step (2.7):

[0048] The on-board processing model estimates imaging performance indicators including target detection probability, target recognition probability, and false alarm rate; the mission performance model evaluates mission performance based on both the amount and accuracy of target acquisition throughout the simulation period.

[0049] The advantages of this invention compared to the prior art are:

[0050] (1) This invention comprehensively considers the integrated operation of the interrelationships between models such as space-ground integrated mission planning, imaging, data transmission, and energy, ensuring that the constraints of remote sensing satellite missions are truly reflected and the space-ground integrated usage process is truly reflected.

[0051] (2) The present invention uses the task sequence output by the task planning model as the simulation benchmark. Each complete simulation of each constraint model is a cycle. If any constraint condition is not met in each cycle, the initial task pool is slightly updated until the task sequence that satisfies all constraints is obtained through iteration, so as to ensure that the requirements for satellite use in orbit are met while obtaining the maximum task efficiency.

[0052] (3) This invention defines the update criteria of the initial task pool and reflects the characteristics of the remote sensing system by adjusting the initialization parameter configuration of each model, so as to ensure that the digital model integration modeling method is applicable to different users and satellite conditions and has universality. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the method of the present invention. Detailed Implementation

[0054] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] To address the challenge of constructing a realistic and accurate remote sensing system model architecture under the comprehensive constraints of complex mission target characteristics, environmental conditions, operating modes, and resource constraints, this invention proposes an integrated modeling method for remote sensing systems oriented towards space-ground collaborative mission planning. This method abstracts the remote sensing system into a digital system comprised of an operation management model, orbit model, mission planning model, attitude control model, target environment model, atmospheric correction model, payload model, data transmission model, power supply and distribution model, on-board processing model, and mission performance model. Furthermore, it designs a collaborative mode and interactive logic for each digital model to simulate the space-ground process of the remote sensing system, performing refined calculations and predictions of the process operation. (Refer to...) Figure 1 .

[0056] (1) The operation management model receives the initial configuration information, configures the task scenarios and digital models, determines all imaging tasks within the simulation period, and forms a task pool.

[0057] This step is the initialization step in the model running process, mainly used to set the initial configuration parameters.

[0058] Specifically, the initial configuration information includes two categories: task scenario information and model initialization information. The task scenario information includes target, task, and environment information, while the model initialization information includes preset parameters and simulation conditions required for each digital model simulation process.

[0059] Target information includes, but is not limited to, target location information (such as target latitude and longitude or target number in a pre-stored target library) and target type information (such as target material reflectivity, transmittance, emissivity, temperature, etc.).

[0060] Task information, including but not limited to the priority of each task and the imaging mode of each task.

[0061] Environmental information includes, but is not limited to, surface information (ambient background reflectance, transmittance, emissivity, temperature, surface elevation, etc.) and atmospheric information (aerosol patterns, optical thickness, atmospheric profiles, etc.).

[0062] Preset parameters are provided for each digital model, such as the initial orbit time of the orbit model, the optical thickness of the atmospheric correction model, the ground data transmission station of the data transmission model, the relay satellite orbit position, the satellite-to-ground data transmission rate, the compression ratio and the proportion of auxiliary data, the number of pixels in each spectral band, the number of quantization bits, the satellite power consumption in each mode of the power supply and distribution model, the maximum battery discharge ratio, and other parameters. By defining the digital model parameters that represent the functions of each subsystem of the remote sensing satellite, the capabilities of the remote sensing satellite system can be constructed, and the key features of the satellite-to-ground system can be constructed.

[0063] Simulation conditions, mainly including simulation start and end times, simulation step size, speedup ratio, loop termination conditions, and displayed data, serve as operating parameters for the digital satellite system.

[0064] Once the initial configuration of the operation management model is completed, all imaging tasks within the simulation period are obtained, thus forming the initial task pool.

[0065] (2) Based on the task pool, the track model calculates the track information and arc information during the simulation period.

[0066] In one possible implementation, the orbit model extrapolates the orbit information within the simulation time period set in step (1) based on the satellite's initial orbit epoch and the initial orbital root numbers (including semi-major axis a, eccentricity e, orbital inclination i, ascending node longitude Ω, perihelion argument ω, and true anomaly angle φ). Based on the satellite's transit conditions and its pitch and yaw maneuvers, the arc segment information is calculated, namely the target visible arc segment, the ground data transmission station visible arc segment, and the relay satellite visible arc segment.

[0067] (3) The task planning model performs autonomous task planning based on arc information and generates a task sequence. The attitude control model performs attitude calculations for each imaging task in the task sequence to obtain the attitude sequence.

[0068] In one possible implementation, the task planning model autonomously plans tasks based on the target visible arc segment from step (2) to generate a task sequence. The preferred approach is as follows:

[0069] (3.1) The task planning model decomposes complex imaging modes (such as multi-strip stitching and stereo imaging) into meta-tasks based on the imaging working modes (such as passive push-broom and multi-strip stitching) set in step (1).

[0070] (3.2) Under the constraints of the satellite's maximum maneuver angle and speed, and in combination with the imaging attitude requirements, the meta-task is planned as a specific observation strip within the target's visible arc, and the start and end times of each imaging task and the latitude and longitude of the camera pointing to the ground at the start and end times are obtained, i.e., the task sequence.

[0071] Among them, the planning principle is to prioritize the imaging task when there is a conflict in the visible arc segment of the target.

[0072] In one possible implementation, the attitude control model performs attitude calculations based on the visible arc segments of the ground data transmission station and the visible arc segments of the relay satellite, as well as the mission sequence, from the arc segment information in step (2), to obtain the attitude sequence. The preferred method is as follows:

[0073] (3.3) The attitude control model plans specific data transmission arcs based on the visible arcs of the ground data transmission station and the visible arcs of the relay satellite. Then, the specific information of the mission sequence (imaging time, imaging duration, latitude and longitude of the imaging mission start and end, etc.) is input into the attitude control model. The attitude control model calculates the complete attitude of the imaging mission segment, the attitude of the data transmission mission segment and the attitude of the non-imaging segment (that is, the other time periods except for the imaging mission segment and the data transmission mission segment) according to the preset attitude maneuver strategy (point-to-point maneuver, imaging maneuver, etc.), which is the attitude sequence.

[0074] (4) The target environment model calculates the target radiation characteristics, the atmospheric correction model calculates the atmospheric correction coefficient, the load model predicts the imaging quality, and the imaging quality pre-evaluation index is obtained. Based on the judgment result of whether the preset imaging quality expectation is met, the task pool is updated.

[0075] Based on the target location information, target type information, surface information, and atmospheric information preset for different targets in step (1), the target environment model and atmospheric correction model are called to perform corresponding calculations, as follows:

[0076] (4.1) The target environment model calculates the radiance or radiance of a stationary target at the time of the mission using the MODTRAN atmospheric radiative transfer model, based on the target material's reflectance, transmittance, emissivity, temperature, and the environmental background reflectance, transmittance, emissivity, temperature, and surface elevation of the target's location. The atmospheric correction model calculates the correction accuracy for the reflectance of the target area based on the aerosol pattern, optical thickness, atmospheric profile, etc., at the target's location, combined with the imaging angle and imaging time.

[0077] By inputting the aforementioned target radiation characteristics and atmospheric correction coefficients into the payload model, the imaging quality pre-evaluation indicators for each imaging mission in the mission sequence can be obtained, specifically including modulation transfer function, positioning accuracy, spatial resolution, spectral resolution, signal-to-noise ratio, and NETD characterization.

[0078] (4.2) Determine whether the imaging quality pre-evaluation index of each imaging task meets the preset imaging quality expectation.

[0079] The imaging quality pre-evaluation index of each imaging task is compared with the preset imaging quality expectation threshold in step (1). Imaging tasks that are lower than the threshold are considered unsatisfactory. The unsatisfactory imaging tasks are deleted, the task pool is updated, and the process returns to step (2) to recalculate the orbit and arc segment and plan subsequent tasks. If no deletion occurs in this step, it means that the imaging quality of each imaging task in the simulation period meets the requirements, and step (5) continues.

[0080] (5) The data transmission model calculates the total imaging data volume of each imaging task in the mission sequence based on the orbit information and attitude sequence, plans and arranges the data transmission tasks, and updates the task pool based on the judgment result of whether the on-board data dynamic balance is met.

[0081] (5.1) First, the data transmission model calculates the velocity-to-height ratio of imaging based on the trajectory information of each imaging task in the task sequence and the attitude of the imaging task segment in the attitude sequence, and then calculates the imaging integration time of each spectral segment of each imaging task; further, combined with the preset parameters of the data transmission model in step (1), the total data volume of each spectral segment of a single imaging task is calculated, and the total data volume of each spectral segment of each imaging task is accumulated to obtain the final total imaging data volume.

[0082] This embodiment presents a specific calculation method:

[0083] Based on the imaging attitude and orbital position of each imaging mission, the velocity-to-height ratio of the imaging is calculated. According to the formula Calculate the imaging integration time t for the j-th spectral band in the i-th imaging task. ij .

[0084] Where d is the pixel size of the camera's TDICCD device, f is the camera's focal length, h is the slant distance of the shooting point, and v cx It is the x-direction component of the vector projection of the ground velocity in the camera's body coordinate system.

[0085] Furthermore, combining the compression ratio, auxiliary data ratio, number of pixels in each spectral band, and quantization bit depth set for the data transmission model in (1), the total data volume D for each spectral band in a single imaging task is calculated. i

[0086]

[0087] Where, p j T is the number of pixels in the j-th spectral segment. ij t is the total imaging duration of the j-th spectral band in the i-th imaging task. ij It is the imaging integration time of the j-th spectral band in the i-th imaging task, n j c is the quantization bit depth of the j-th spectral band. ij It is the compression ratio of the j-th spectral band in the i-th imaging task, a i It is the ratio of auxiliary data to imaging data for the i-th imaging task. All of the above parameters are preset in step (1).

[0088] The total imaging data volume D is obtained by summing the total data volume of each spectral band from all imaging missions. total

[0089]

[0090] (5.2) Then, based on the start and end times of the data transmission arc segment planned in step (3) and the calculated total imaging data volume D, total This involves planning and arranging data transmission tasks to determine the data to be transmitted for each task.

[0091] Specifically, data transmission tasks can be planned and arranged according to a certain logic, taking into account their sequence. For example, based on the priority of imaging tasks, higher priority tasks can be played back first; then, based on the time sequence of imaging tasks, the first to be imaged can be played back first; and multiple imaging task data can be downloaded within a single data transmission segment as much as possible.

[0092] (5.3) Finally, determine whether the dynamic balance of on-board data is satisfied during the execution of each imaging and data transmission task.

[0093] This embodiment presents a specific calculation method:

[0094] It is assumed that each imaging task occupies onboard storage at the start of execution, and after playback via the specific data transmission task planned in (5.2), it no longer occupies onboard storage. The total capacity of onboard storage is D. max On-board real-time data storage volume D sat The data volume D generated by the imaging mission up to the current simulation time, the data usage D0 at the initial time on the satellite, and the usage d of other satellite engineering parameters. sat The sum of these three, minus the amount of data D that has been transmitted up to the current simulation time during the data transmission task. dl ,Right now:

[0095] D sat =D0+D+d sat -D dl <D max

[0096] Based on the above rules, the on-board real-time data storage capacity D is calculated step by step according to the simulation step size preset in step (1). sat And compare D sat Is it greater than D? max If it is greater than D max If the dynamic balance of data on the satellite is not met, one low-priority task is deleted from the task pool. If any task is deleted in the task pool during this step, the process returns to step (2) to perform orbit and arc calculations and subsequent task planning. If no deletion occurs during this step, it means that the dynamic balance of data on the satellite during the simulation period meets the requirements, and the process proceeds to the next step.

[0097] (6) The power supply and distribution model calculates the power generation and consumption at each moment during the simulation period based on the orbit information and attitude sequence, and updates the task pool based on the judgment result of whether the on-board energy dynamic balance is met.

[0098] (6.1) First, based on the track information and attitude sequence, and considering the influence of the shadow area and the sunlit area, the power supply and distribution model calculates the charging power at the current simulation moment. The specific method is as follows:

[0099] The attitude angles of the satellite at each moment of the attitude sequence are obtained. Based on the relative position of the satellite in orbit and the sun, the direction of the sun's projection is calculated, and the shading of the solar array by the satellite body is calculated to obtain the proportion of the unshaded solar array sheet.

[0100] Based on the preset coordinates of the solar wing, calculate the solar incident vector on the unobstructed solar wing at each moment;

[0101] Under the fully regulated system, the solar array voltage remains constant. The relationship between the illumination angle of the unshaded solar array and the short-circuit current is calculated to obtain the charging power P provided by the solar array at the current simulation moment. BC This method automatically takes into account the effects of the shadow area and the sunny area.

[0102] Then, according to the simulation step size preset in step (1), the integral of charging power with respect to time is calculated step by step to obtain the total power generation, and then the total power consumption is obtained to calculate the real-time battery capacity percentage on the satellite.

[0103] This embodiment provides a specific calculation method:

[0104] Based on the satellite power consumption in each mode preset in (1), and the simulation step size preset in (1), the total power generation up to the current moment is obtained by integrating over time. And the total power consumption of the satellite in each time period from the start to the current time within the simulation start and end time period, corresponding to the working mode. Where m represents each mode, such as recording mode 1, recording mode 2, playback, etc.

[0105] Total power consumption Q F With total power generation Q C The real-time battery capacity percentage on the satellite is obtained by comparing it with the initial battery capacity E0.

[0106] (6.2) Determine whether the on-board energy dynamic balance is satisfied. That is, throughout the entire simulation start and end time period, determine whether the simulation step size is satisfied by gradually increasing the step size according to the preset simulation step size in (1).

[0107]

[0108] The maximum allowable depth of discharge (DOD) of the battery is typically 60-70%. If it exceeds this limit, it indicates that the dynamic energy balance on the satellite is not met, and one low-priority task is deleted from the task pool. If any task in the task pool is deleted during this step, the process returns to step (2) to perform orbit and arc segment calculations and subsequent task planning. If no deletion occurs during this step, it indicates that the dynamic energy balance on the satellite meets the requirements during the simulation start and end time period, and the process proceeds to the next step.

[0109] (7) Finally, based on the feasible task sequence formed by iteration and the calculation results of the above digital models, the on-board processing model predicts the imaging performance, and the task performance model evaluates the task performance throughout the entire simulation period.

[0110] Specifically, the target type and imaging scene type, imaging quality pre-assessment indicators, and atmospheric correction coefficients within the simulation start and end time period are input into the on-board processing model to predict imaging performance, obtaining the target detection and recognition probability and false alarm rate for the mission scenario. Subsequently, a comprehensive mission performance evaluation based on the number and accuracy of target acquisition is conducted within the simulation period, such as average imaging quality and the degree of fulfillment of specific tasks.

[0111] It is understood that this invention has been described through embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of this invention. Furthermore, under the teachings of this invention, these features and embodiments can be modified to adapt to specific circumstances without departing from the spirit and scope of this invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are protected by this invention.

[0112] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning, characterized in that, include: (1) The remote sensing system is abstracted into an operation management model, an orbit model, a mission planning model, an attitude control model, a target environment model, an atmospheric correction model, a payload model, a data transmission model, a power supply and distribution model, an on-board processing model, and a mission performance model; (2) The collaborative mode and interaction logic of each digital model are designed, the satellite-to-ground process of the remote sensing system is simulated, and the process operation is calculated and predicted in detail, as follows: (2.1) The operation management model receives the initial configuration information, configures the task scenario and each digital model, determines all imaging tasks within the simulation period, and forms a task pool; (2.2) Based on the task pool, the track model calculates the track information and arc information during the simulation period; (2.3) The task planning model performs autonomous task planning based on arc segment information to generate a task sequence; The attitude control model performs attitude calculations for each imaging task in the task sequence to obtain the attitude sequence. (2.4) The target environment model calculates the target radiation characteristics, the atmospheric correction model calculates the atmospheric correction coefficient, the target radiation characteristics and atmospheric correction coefficient are input into the payload model to estimate the imaging quality, and the imaging quality pre-evaluation index of each imaging task in the task sequence is obtained; it is determined whether the imaging quality pre-evaluation index of each imaging task meets the preset imaging quality expectation; if it does not meet the expectation, the imaging task that does not meet the expectation is deleted, the task pool is updated, and the process returns to step (2.2); if all the expectations are met, the process continues to step (2.5). (2.5) The data transmission model calculates the total amount of imaging data for each imaging task in the mission sequence based on the orbit information and attitude sequence, and plans and arranges the data transmission tasks; it also determines whether the dynamic balance of on-board data is satisfied during the execution of each imaging task and data transmission task. If the conditions are not met, delete low-priority imaging tasks according to task priority, update the task pool, and return to step (2.2); if the conditions are met, continue to step (2.6). (2.6) The power supply and distribution model calculates the power generation and consumption at each moment during the simulation period based on the orbit information and attitude sequence; and determines whether the dynamic balance of onboard energy is satisfied throughout the entire simulation period. If the conditions are not met, delete low-priority imaging tasks according to task priority, update the task pool, and return to step (2.2); if the conditions are met, continue to step (2.7). (2.7) Based on the feasible task sequence formed by iteration and the calculation results of the above digital models, the on-board processing model predicts the imaging performance, and the task performance model evaluates the task performance throughout the entire simulation period. In step (2.4): Based on the target location information, target type information, surface information and atmospheric information preset for different targets in step (2.1), the target environment model calculates the target radiation characteristics, which are characterized by the radiance or radiance of the stationary target on the ground at the time of the imaging mission; the atmospheric correction model calculates the atmospheric correction coefficient, which is characterized by the correction accuracy of the reflectivity of the target area. By inputting the target radiation characteristics and atmospheric correction coefficients into the payload model, the imaging quality pre-evaluation indexes for each imaging task in the mission sequence are obtained, characterized by modulation transfer function, positioning accuracy, spatial resolution, spectral resolution, and signal-to-noise ratio. The imaging quality pre-evaluation index of each imaging task is compared with the preset imaging quality expectation threshold in step (2.1). If it is lower than the threshold, it is considered that the preset imaging quality expectation is not met, the corresponding imaging task is deleted from the task pool, and the task pool is updated. In step (2.5): The data transmission model calculates the velocity-to-height ratio of imaging based on the trajectory information of each imaging task in the task sequence and the attitude of the imaging task segment in the attitude sequence. Then, it calculates the imaging integration time of each spectral segment of each imaging task. Furthermore, it combines the preset parameters of the data transmission model in step (2.1) to calculate the total data volume of each spectral segment of a single imaging task. The total data volume of each spectral segment of each imaging task is accumulated to obtain the final total imaging data volume. Then, based on the start and end times of the data transmission arc segment planned in step (2.3) and the total amount of imaging data calculated, the data transmission task is planned and arranged to determine the data to be transmitted for each data transmission task. Then, according to the simulation step size preset in step (2.1), the amount of real-time data stored on the satellite is calculated step by step. Determine whether the real-time data storage volume on the satellite exceeds the total on-board storage capacity. If it does, it means that the dynamic balance of on-board data is not met. Delete one low-priority task from the task pool and update the task pool. The calculation method for the real-time data storage volume on the satellite is as follows: the sum of the data volume generated by the imaging task up to the current simulation time, the data volume occupied at the initial moment on the satellite, and the data volume occupied by other satellite engineering parameters, minus the data volume already transmitted in the data transmission task up to the current simulation time. In step (2.6): The power supply and distribution model calculates the charging power at the current simulation moment, taking into account the influence of the shadow area and the sunlight area, based on the track information and attitude sequence. Then, according to the simulation step size preset in step (2.1), the integral of charging power with respect to time is calculated step by step to obtain the total power generation, and then the total power consumption is obtained to calculate the real-time battery capacity percentage on the satellite. Determine if the real-time battery capacity percentage on the satellite is greater than the maximum allowable depth of discharge. If it is, it means that the dynamic balance of onboard energy is not met. Delete one low-priority task from the task pool and update the task pool. The onboard real-time battery capacity percentage is defined as the ratio of the total power consumption up to the current simulation time to the sum of the battery capacity and total power generation at the initial moment on the satellite.

2. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning as described in claim 1, characterized in that: In step (2.1): The initial configuration information includes two categories: task scenario information and model initialization information. The task scenario information includes target, task, and environment information, while the model initialization information includes preset parameters and simulation conditions required for each digital model simulation process.

3. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning as described in claim 2, characterized in that: The target information includes, but is not limited to, target location information and target type information; the task information includes, but is not limited to, the priority of each task and the imaging mode of each task; the environmental information includes, but is not limited to, surface information and atmospheric information; and the simulation conditions include, but are not limited to, simulation start and end times and simulation step size.

4. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning as described in claim 1, characterized in that: In step (2.2): The orbit model extrapolates the orbit information within the simulation period set in step (2.1) based on the satellite's initial orbit epoch and initial orbit six-element number. Based on the satellite's transit conditions and its pitch and yaw maneuvers, the target's visible arc segment, the ground data transmission station's visible arc segment, and the relay satellite's visible arc segment are calculated.

5. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning as described in claim 1, characterized in that: In step (2.3): The task planning model autonomously plans tasks based on the target visible arc segments in the arc segment information of step (2.2) to generate a task sequence, as follows: The mission planning model decomposes the complex imaging mode into meta-tasks based on the imaging working mode set in step (2.1). Under the constraints of the satellite's maximum maneuver angle and maneuver speed, and in combination with the imaging attitude requirements, the meta-tasks are planned as specific observation strips within the target's visible arc, and the start and end times of each imaging mission and the latitude and longitude of the camera pointing to the ground at the start and end times are obtained.

6. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning according to claim 1, characterized in that: In step (2.3): The attitude control model calculates the attitude sequence based on the visible arc segments of the ground data transmission station and the visible arc segments of the relay satellite in the arc segment information of step (2.2), as well as the mission sequence. The specific method is as follows: The attitude control model plans specific data transmission arcs based on the visible arcs of ground data transmission stations and relay satellites. Then, the specific information of the mission sequence is input into the attitude control model, which calculates the complete attitude of the imaging mission segment, the attitude of the data transmission mission segment, and the attitude of the remaining time periods according to the preset attitude maneuver strategy.

7. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning as described in claim 1, characterized in that: The target environment model, based on target type information including target material reflectivity, transmittance, emissivity, and temperature, and surface information including environmental background reflectivity, transmittance, emissivity, temperature, and surface elevation at the target location, uses the MODTRAN atmospheric radiative transfer model to calculate the radiance or radiance of the stationary target on the surface at the mission time.

8. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning according to claim 1, characterized in that: The atmospheric correction model calculates the correction accuracy of the reflectivity of the target area based on atmospheric information, including the aerosol pattern, optical thickness, and atmospheric profile at the target's location, combined with the imaging angle and imaging time.

9. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning according to claim 1, characterized in that: The power supply and distribution model calculates the charging power at the current simulation moment based on track information and attitude sequence, taking into account the influence of the shadow area and the sunlit area. The specific method is as follows: Obtain the satellite's attitude angles at each moment in the attitude sequence. Calculate the direction of sunlight projection based on the satellite's relative position to the sun in its orbit. Also calculate the shading of the solar array by the satellite body to obtain the unshaded solar array panels. Calculate the solar incident vector on the unshaded solar array panels at each moment. Under the fully regulated system, the relationship between the illumination angle of the unshaded solar fins and the short-circuit current is calculated to obtain the charging power provided by the solar array at the current simulation moment.

10. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning according to claim 1, characterized in that: In step (2.7): The on-board processing model estimates imaging performance indicators including target detection probability, target recognition probability, and false alarm rate; the mission performance model evaluates mission performance based on both the amount and accuracy of target acquisition throughout the simulation period.

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