Remote sensing system digital model integrated modeling method oriented to satellite-ground cooperative task planning

Abstract

The invention discloses a remote sensing system digital model integrated modeling method for satellite-ground cooperative task planning. The method comprises the following steps: abstracting a remote sensing system into a digital system consisting of an operation management model, an orbit model, a task planning model, an attitude 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-satellite processing model and a task efficiency model; the method truly reflects the solving process of a remote sensing system facing key constraints, is close to the whole satellite-ground application process, realizes refined calculation and estimation of the process operation condition, and meets the requirements of satellite-ground key technical index verification.

Description

Technical Field

[0001] The invention relates to the field of digital modeling, and in particular to a remote sensing system digital model integrated modeling method for satellite-ground collaborative mission planning. Background Art

[0002] High-resolution optical remote sensing systems have the characteristics of rich image texture information, simple image interpretation and application, large detection range, and fast information acquisition. They play an important role in high-resolution earth observation applications.

[0003] At present, the number of targets imaged by high-resolution remote sensing satellites is growing rapidly every day, and the amount of business data has grown to tens of gigabits per second. Multiple mission requirements and multiple imaging modes require satellites to fully optimize the planning, scheduling, and scheduling of missions, and to dynamically manage data transmission, agile attitude control, energy management, and on-board storage. In addition, remote sensing satellites usually plan their missions by time period, and mission planning must simultaneously meet the requirements of multi-task, high dynamics, and high precision. It requires highly coordinated management of the satellite's subsystems, such as attitude maneuvers, imaging control, and data transmission, and puts forward higher requirements for satellite-ground coordinated management and control. Since the ground cannot predict the status of the satellite in advance, the utilization rate of on-board resources is low, and dynamic management and optimization cannot be carried out. The satellite's capabilities cannot be fully utilized, resulting in insufficient coordination between the satellite and the ground during the use of the satellite, and a waste of on-board resources.

[0004] As satellite imaging capabilities improve, usage and maneuvering modes become increasingly complex, and the amount of imaging data and energy consumption also increase dramatically. The execution of imaging tasks determines the data transmission planning and energy status and storage status. Therefore, satellites in orbit face a situation where comprehensive constraints such as imaging quality, energy, and data transmission constrain each other. From the perspective of satellite-ground collaborative mission planning, efficient and accurate verification methods are needed. On the one hand, the capabilities of satellites should be maximized to pursue more tasks and better imaging effects; on the other hand, it is necessary to conduct operational verification on the ground through parallel simulation before the mission is launched to ensure the success rate of mission execution and the ease of use of the satellite.

[0005] An important development trend is to abstract the capabilities of satellite and ground subsystems into digital models based on digital means, simulate the status and operating environment of satellite and ground in digital space, and conduct satellite-ground collaborative mission planning. The granularity and authenticity of digital models are limited. Therefore, the functional characteristics of each digital model in the digital model integration modeling process, as well as the logical interaction and dynamic adaptation between models are the focus of modeling.

[0006] Therefore, in order to resolve the contradiction between the tight coupling of the on-orbit mission plan of the high-resolution remote sensing satellite system and the on-board operating status and constraints, and to meet the need for refined calculation and estimation of the satellite's operating status and operation process, it is urgent to propose corresponding solutions. Summary of the invention

[0007] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art and provide an integrated modeling method of digital models of remote sensing systems for satellite-ground collaborative mission planning, which truly reflects the process of solving key constraints faced by the remote sensing system through dynamic adaptive iterative optimization, is close to the entire process of satellite-ground application, realizes refined calculation and prediction of the process operation status, and meets the needs of verification of key technical indicators of satellite and ground.

[0008] The technical solution of the present invention is: a remote sensing system digital model integrated modeling method 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 onboard processing model, and a mission effectiveness model;

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

[0011] (2.1) The operation management model receives the initial configuration information, configures the task scenarios 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 orbit model calculates the orbit information and arc segment information within the simulation period;

[0013] (2.3) The task planning model performs autonomous task planning based on the arc information and generates a task sequence; the attitude control model performs attitude calculation on each imaging task in the task sequence and obtains 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 the atmospheric correction coefficient are input into the load model, the imaging quality is estimated, 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 expectations; if not, the imaging tasks that do not meet the expectations are deleted, the task pool is updated, and the step (2.2) is returned; if all are satisfied, the step (2.5) is continued;

[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; determines whether the dynamic balance of on-board data is met during the execution of each imaging task and data transmission task; if not, deletes the low-priority imaging task according to the task priority, updates the task pool, and returns to step (2.2); if satisfied, continues to step (2.6);

[0016] (2.6) The power supply and distribution model calculates the power generation and consumption at each moment in the simulation period according to the orbit information and attitude sequence; determines whether the on-board energy dynamic balance is met during the entire simulation period; if not, delete the low-priority imaging task according to the task priority, update the task pool, and return to step (2.2); if satisfied, continue to step (2.7);

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

[0018] Further, in the step (2.1):

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

[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 the simulation start and end time and the simulation step size.

[0021] Further, in the step (2.2):

[0022] The orbit model extrapolates the orbit information in the simulation period set in step (2.1) based on the satellite's initial orbit epoch time and the six initial orbit numbers, and calculates the target visible arc, the ground data transmission station visible arc and the relay satellite visible arc based on the satellite's transit conditions and the satellite's pitch and roll maneuverability.

[0023] Further, in the step (2.3):

[0024] The task planning model performs autonomous task planning based on the target visible arc segment in the arc segment information in step (2.2) to generate a task sequence. The specific method is as follows:

[0025] The mission planning model decomposes the complex imaging mode into meta-tasks according to the imaging working mode set in step (2.1); under the constraints of the satellite's maximum maneuvering angle and maneuvering speed, combined with the imaging attitude requirements, the meta-task is planned as a specific observation strip within the target visible arc segment, and the start and end times of each imaging mission and the ground longitude and latitude of the camera at the start and end times are obtained.

[0026] Further, in the step (2.3):

[0027] The attitude control model performs attitude calculation according to the visible arc segment of the ground data transmission station and the visible arc segment of the relay satellite in the arc segment information of step (2.2) and the task sequence to obtain the attitude sequence. The specific method is as follows:

[0028] The attitude control model plans the specific data transmission arc based on the visible arc of the ground data transmission station and the visible arc of the relay satellite; then the specific information of the task sequence is input into the attitude control model, and the attitude control model calculates the complete imaging mission segment attitude, the data transmission mission segment attitude and the attitude of the remaining time periods according to the preset attitude maneuver strategy.

[0029] Further, in the step (2.4):

[0030] Based on the target position 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 surface 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] The target radiation characteristics and atmospheric correction coefficients are input into the payload model to obtain the imaging quality pre-evaluation indicators of each imaging task in the task sequence, which are characterized by modulation transfer function, positioning accuracy, spatial resolution, spectral resolution, and signal-to-noise ratio;

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

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

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

[0035] Further, in the step (2.5):

[0036] The digital transmission model calculates the imaging speed-to-height ratio according to the orbit information of each imaging task in the task sequence and the imaging task segment attitude in the attitude sequence, and then calculates the imaging integration time of each spectral segment of each imaging task. The total data volume of each spectral segment of a single imaging task is further calculated by combining the preset parameters of the digital transmission model in step (2.1). The total data volume of each spectral segment of each imaging task is accumulated to obtain the final total imaging data volume.

[0037] Then, according to the start and end time of the data transmission arc segment planned in step (2.3) and the total imaging data volume calculated, the data transmission task is planned and arranged to determine the downlink data for each data transmission task;

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

[0039] The calculation method of 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 moment, the data occupied at the initial moment on the satellite, and the data occupied by other satellite engineering parameters, minus the data volume that has been downloaded in the data transmission task executed up to the current simulation moment.

[0040] Further, in the step (2.6):

[0041] The power supply and distribution model calculates the charging power at the current simulation time based on the orbit information and attitude sequence, taking into account the influence of the ground shadow area and the sun-illuminated area;

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

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

[0044] The percentage of the real-time battery capacity on the satellite is: the total power consumption up to the current simulation time divided by the sum of the battery capacity and the total power generation at the initial time on the satellite.

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

[0046] The attitude angle of the satellite at each moment in the attitude sequence is obtained. According to the relative position of the satellite in orbit and the sun, the projection direction of the sunlight is calculated, and the shielding of the solar wing by the satellite body is calculated to obtain the unobstructed solar wing patch. The solar incident vector on the unobstructed solar wing patch at each moment is calculated. Under the full regulation system, the relationship between the illumination angle of the unobstructed solar wing patch and the short-circuit current is calculated to obtain the charging power provided by the solar array at the current simulation moment.

[0047] Further, in the step (2.7):

[0048] The on-board processing model estimates the imaging performance indicators, including target detection probability, target recognition probability and false alarm rate. The mission effectiveness model evaluates the mission effectiveness based on the target acquisition quantity and acquisition accuracy during the entire simulation period.

[0049] The advantages of the present invention compared with the prior art are:

[0050] (1) The present invention comprehensively considers the integrated operation of the relationships among the satellite-ground integrated mission planning, imaging, data transmission, energy and other models to ensure that the remote sensing satellite mission constraints can be truly reflected and the satellite-ground integrated use process can be truly reflected.

[0051] (2) The present invention uses the task sequence output by the task planning model as the simulation benchmark. Each constraint model is fully simulated and deduced once as a cycle. When 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 iteratively obtained, ensuring that the satellite meets the requirements for on-orbit use while obtaining the maximum task efficiency.

[0052] (3) The present invention defines the updating criteria of the initial task pool and adjusts the initialization parameter configuration of each model to reflect the characteristics of the remote sensing system, thereby ensuring that the digital model integrated modeling method is applicable to different users and satellite conditions and has universal applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0054] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0055] In order to solve the problem of building a true and accurate remote sensing system model architecture for remote sensing satellite systems under comprehensive constraints such as target characteristics, environmental conditions, working modes, and resource constraints of complex tasks, the present invention proposes an integrated modeling method for digital models of remote sensing systems for satellite-ground collaborative mission planning. The remote sensing system is abstracted into a digital system consisting of 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 effectiveness model. Each digital model is designed to use the following collaborative mode and interactive logic to simulate the satellite-ground process of the remote sensing system, and to perform refined calculations and estimates on the process operation status. For reference, 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, which is mainly used to set the initial configuration parameters.

[0058] Specifically, the initial configuration information includes two types of information: task scenario information and model initialization information; wherein the task scenario information includes target, task and environment information, and the model initialization information includes preset parameters and simulation conditions required in the simulation process of each digital model.

[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, etc.) and target type information (such as target material reflectivity, transmittance, emissivity, temperature, etc.).

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

[0061] Environmental information, including but not limited to surface information (environmental background reflectivity, transmittance, emissivity, temperature, surface elevation, etc.) and atmospheric information (aerosol pattern, optical thickness, atmospheric profile, etc.).

[0062] Preset parameters have default parameter settings for each digital model, such as the initial orbital moment of the orbital model, the optical thickness of the atmospheric correction model, the ground data transmission station of the digital transmission model, the orbit position of the relay satellite, 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 representing the functions of each subsystem of the remote sensing satellite, the remote sensing satellite system capabilities can be constructed, and the key features of the satellite-to-ground system can be constructed.

[0063] The simulation conditions mainly include simulation start and end time, simulation step size, acceleration ratio, loop termination condition, display data, etc., which serve as the operating parameters of the digital satellite system.

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

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

[0066] In a possible implementation, the orbit model extrapolates the orbit information within the simulation time period set in step (1) based on the initial orbit epoch time of the satellite and the six initial orbit numbers (including the semi-major axis a, the eccentricity e, the orbit inclination i, the ascending node ecliptic longitude Ω, the perihelion argument ω, and the true anomaly φ), and calculates the arc information, i.e., the target visible arc, the ground data transmission station visible arc, and the relay satellite visible arc, based on the satellite transit situation and the satellite's pitch and roll maneuverability.

[0067] (3) The task planning model performs autonomous task planning based on the arc information and generates a task sequence. The attitude control model calculates the attitude of each imaging task in the task sequence and obtains the attitude sequence.

[0068] In a possible implementation, the task planning model performs autonomous task planning according to the target visible arc segment in step (2) to generate a task sequence. The preferred implementation 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 according to the imaging working mode (such as passive push-scanning and multi-strip stitching) set in step (1);

[0070] (3.2) Under the constraints of the satellite’s maximum maneuvering angle and maneuvering speed, combined with the imaging attitude requirements, the meta-task is planned as a specific observation strip within the target visible arc segment, and the start and end times of each imaging mission and the ground longitude and latitude of the camera at the start and end times are obtained, that is, the mission sequence.

[0071] Among them, the planning principle is to sort according to the priority of imaging tasks when the target visible arc segments conflict.

[0072] In a possible implementation, the attitude control model performs attitude calculation according to the visible arc segment of the ground data transmission station and the visible arc segment of the relay satellite and the task sequence in the arc segment information in step (2) to obtain an attitude sequence. The preferred method is as follows:

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

[0074] (4) The target environment model calculates the target radiation characteristics, the atmospheric correction model calculates the atmospheric correction coefficient, and the payload model estimates the imaging quality to obtain the imaging quality pre-evaluation index. The task pool is updated based on the judgment result of whether the preset imaging quality expectations are met.

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

[0076] (4.1) The target environment model uses the MODTRAN atmospheric radiation transfer model to calculate the radiance or radiance of the stationary surface target at the mission time based on the reflectivity, transmittance, emissivity, temperature of the target material and the physical properties of the environmental background reflectivity, transmittance, emissivity, temperature and surface elevation at the target location. The atmospheric correction model calculates the correction accuracy of the reflectivity of the target area based on the aerosol pattern, optical thickness, atmospheric profile, etc. at the target location, combined with the imaging angle and imaging time.

[0077] The above-mentioned target radiation characteristics and atmospheric correction coefficients are input into the payload model to obtain the imaging quality pre-evaluation indicators of each imaging task in the task sequence, which can be specifically characterized by modulation transfer function, positioning accuracy, spatial resolution, spectral resolution, signal-to-noise ratio, and NETD.

[0078] (4.2) Determine whether the imaging quality pre-assessment indicators of each imaging task meet the preset imaging quality expectations.

[0079] That is, the imaging quality pre-evaluation index of each of the above imaging tasks is compared with the imaging quality expectation threshold preset in step (1). Imaging tasks below the threshold are considered to be unsatisfactory, and the imaging tasks that do not meet the expectations 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 within the simulation period meets the requirements, and the process continues to step (5).

[0080] (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, plans and arranges the data transmission tasks, and updates the task pool based on the judgment result of whether the dynamic balance of on-board data is met.

[0081] (5.1) First, the digital transmission model calculates the imaging speed-to-height ratio according to the orbit information of each imaging task in the task sequence and the imaging task segment attitude 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 digital 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 provides a specific calculation method:

[0083] Calculate the imaging speed-to-height ratio based on the imaging attitude and orbital position of each imaging mission According to the formula Calculate the imaging integration time t of the jth spectral segment in the i-th imaging task ij .

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

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

[0086]

[0087] Among them, p j is the number of pixels in the jth spectral segment, T ij is the total imaging time of the jth spectral segment in the i-th imaging task, t ij is the imaging integration time of the jth spectral segment in the i-th imaging task, n j is the number of quantization bits of the jth spectral segment, c ij is the compression ratio of the jth spectral segment in the i-th imaging task, a i is the ratio of the auxiliary data to the imaging data of the i-th imaging task. The above parameters are preset in step (1).

[0088] The total imaging data volume D is obtained by summing up the total data volume of each spectrum segment in all previous imaging tasks. total

[0089]

[0090] (5.2) Then, according to the start and end time of the data transmission arc segment planned in step (3) and the total imaging data volume D calculated total , plan and arrange the data transmission tasks, and determine the downlink data for each data transmission task.

[0091] Specifically, the data transmission tasks can be planned and arranged according to a certain logic in combination with the order of data transmission tasks. For example, based on the priority of imaging tasks, the ones with higher priority are played back first; based on the time order of imaging tasks, the ones that are imaged first are played back first; and multiple imaging task data can be downloaded within one data transmission arc segment as much as possible.

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

[0093] This embodiment provides a specific calculation method:

[0094] It is assumed that each imaging task occupies the on-board fixed storage when it starts to execute, and no longer occupies the on-board fixed storage after the specific data transmission task planned by (5.2) is played back. The total on-board fixed storage capacity is D max , the amount of real-time data storage on board D sat is the amount of data D generated by the imaging task up to the current simulation time, the amount of data occupied on the satellite at the initial time D0, and the amount of other satellite engineering parameters occupied d sat The sum of the three minus the amount of data D that has been downloaded during the data transmission task up to the current simulation time dl ,Right now:

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

[0096] According to the above rules, the on-board real-time data storage capacity D is calculated step by step according to the simulation step length preset in step (1). sat , and compare D sat Is it greater than D max , if it is greater than D max , indicating that the dynamic balance of the data on the star is not met, and a low-priority task is deleted from the task pool. If a task in the task pool is deleted in this step, return to step (2) to perform orbit and arc calculation and subsequent task planning, etc. If no deletion occurs in this step, it means that the dynamic balance of the data on the star during the simulation period meets the requirements, and proceed to the next step.

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

[0098] (6.1) First, the power supply and distribution model calculates the charging power at the current simulation time based on the orbit information and attitude sequence, taking into account the influence of the ground shadow area and the sun-illuminated area. The specific method is:

[0099] Obtain the satellite's attitude angle at each moment in the attitude sequence, calculate the direction of sunlight projection based on the relative position of the satellite in orbit and the sun, and calculate the shielding of the solar wing by the satellite body to obtain the proportion of the solar wing surface that is not shielded;

[0100] According to the preset solar wing patch coordinates, the solar incident vector on the solar wing patch that is not blocked at each moment is calculated;

[0101] Under the full regulation system, the voltage of the solar wing remains unchanged, and the relationship between the illumination angle of the unshaded solar wing and the short-circuit current is calculated to obtain the charging power P provided by the solar array at the current simulation time. BC In this way, the effects of the shadow area and the sunlit area are automatically taken into account.

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

[0103] This embodiment provides a specific calculation method:

[0104] According to 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 the time: And the total power consumption of the satellite in the working mode corresponding to each time period from the start to the end of the simulation time period to the current time Where m represents each mode, such as recording mode 1, recording mode 2, playback, etc.;

[0105] The total power consumption Q F and total power generation Q C Compared with the sum of the battery capacities E0 at the initial moment, the real-time battery capacity percentage on board is obtained.

[0106] (6.2) Determine whether the on-board energy dynamic balance is met, that is, during the entire simulation start and end time period, according to the simulation step length preset in (1), gradually increase the value to determine whether

[0107]

[0108] If the battery allows a maximum depth of discharge DOD (generally 60-70%), it means that the on-board energy dynamic balance is not met, and a low-priority task is deleted from the task pool. If a task in the task pool is deleted during this step, return to step (2) to perform orbit and arc calculations and subsequent task planning. If no deletion occurs during this step, it means that the on-board energy dynamic balance during the simulation start and end time period meets the requirements, and proceed to the next step.

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

[0110] Specifically, the target type and imaging scene type, imaging quality pre-assessment index, atmospheric correction coefficient, etc. within the simulation start and end time period are input into the on-board processing model to estimate the imaging performance, and obtain the target discovery, recognition probability and false alarm rate that appear in this mission scene. Then, the mission performance evaluation based on the comprehensive target acquisition quantity and acquisition accuracy within the simulation time period is carried out, such as average imaging quality, specific mission satisfaction, etc.

[0111] It is to be understood that the present invention is described by way of embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and embodiments that can fall within the scope of the claims of this application all fall within the scope protected by the present invention.

[0112] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A remote sensing system digital model integrated modeling method 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 effectiveness model; (2) The coordination mode and interaction logic of each digital model are designed, the satellite-ground process of the remote sensing system is simulated, and the process operation status is refined and estimated, as follows: (2.1) The operation management model receives the initial configuration information, configures the task scenarios 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 orbit model calculates the orbit information and arc segment information within the simulation period; (2.3) The task planning model performs autonomous task planning based on the arc information and generates a task sequence; The attitude control model calculates the attitude of 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 the atmospheric correction coefficient are input into the load model, the imaging quality is estimated, 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 expectations; if not, the imaging tasks that do not meet the expectations are deleted, the task pool is updated, and the step (2.2) is returned; if all are satisfied, the step (2.5) is continued; (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 dynamic balance of on-board data is met during the execution of each imaging task and data transmission task; If not, delete the low-priority imaging task according to the task priority, update the task pool, and return to step (2.2); if satisfied, continue to step (2.6); (2.6) The power supply and distribution model calculates the power generation and consumption at each moment in the simulation period based on the orbit information and attitude sequence; and determines whether the dynamic balance of on-board energy is met during the entire simulation period; If not, delete the low-priority imaging task according to the task priority, update the task pool, and return to step (2.2); if satisfied, continue to step (2.7); (2.7) Based on the iteratively formed feasible task sequence and the calculation results of the above digital models, the on-board processing model estimates the imaging performance, and the task effectiveness model evaluates the task effectiveness during the entire simulation period.

2. The digital model integrated modeling method of remote sensing system for satellite-ground collaborative mission planning according to claim 1 is characterized by: In the step (2.1): The initial configuration information includes two types: task scenario information and model initialization information. The task scenario information includes target, task and environment information, and the model initialization information includes preset parameters and simulation conditions required in the simulation process of each digital model.

3. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning according to claim 2 is 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 each task priority and each task imaging mode, 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 length.

4. The digital model integrated modeling method of remote sensing system for satellite-ground collaborative mission planning according to claim 1 is characterized by: In the step (2.2): The orbit model extrapolates the orbit information in the simulation period set in step (2.1) based on the satellite's initial orbit epoch time and the six initial orbit numbers, and calculates the target visible arc, the ground data transmission station visible arc and the relay satellite visible arc based on the satellite's transit conditions and the satellite's pitch and roll maneuverability.

5. 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 the step (2.3): The task planning model performs autonomous task planning based on the target visible arc segment in the arc segment information in step (2.2) to generate a task sequence. The specific method is as follows: The mission planning model decomposes the complex imaging mode into meta-tasks according to the imaging working mode set in step (2.1); under the constraints of the satellite's maximum maneuvering angle and maneuvering speed, combined with the imaging attitude requirements, the meta-task is planned as a specific observation strip within the target visible arc segment, and the start and end times of each imaging mission and the ground longitude and latitude of the camera 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 the step (2.3): The attitude control model performs attitude calculation according to the visible arc segment of the ground data transmission station and the visible arc segment of the relay satellite in the arc segment information of step (2.2) and the task sequence to obtain the attitude sequence. The specific method is as follows: The attitude control model plans the specific data transmission arc based on the visible arc of the ground data transmission station and the visible arc of the relay satellite; then the specific information of the task sequence is input into the attitude control model, and the attitude control model calculates the complete imaging mission segment attitude, the data transmission mission segment attitude 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 according to claim 1, characterized in that: In the step (2.4): Based on the target position 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 surface 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; The target radiation characteristics and atmospheric correction coefficients are input into the payload model to obtain the imaging quality pre-evaluation indicators of each imaging task in the task sequence, which are characterized by modulation transfer function, positioning accuracy, spatial resolution, spectral resolution, and signal-to-noise ratio; The obtained imaging quality pre-evaluation index of each imaging task is compared with the imaging quality expectation threshold preset in step (2.1). If it is lower than the threshold, it is considered that the preset imaging quality expectation is not met, and the corresponding imaging task is deleted from the task pool and the task pool is updated.

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

9. The method for integrated modeling of digital models of remote sensing systems for satellite-ground collaborative mission planning according to claim 7, 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 of the target location, combined with the imaging angle and imaging time.

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 the step (2.5): The digital transmission model calculates the imaging speed-to-height ratio according to the orbit information of each imaging task in the task sequence and the imaging task segment attitude in the attitude sequence, and then calculates the imaging integration time of each spectral segment of each imaging task. The total data volume of each spectral segment of a single imaging task is further calculated by combining the preset parameters of the digital transmission model in step (2.1). The total data volume of each spectral segment of each imaging task is accumulated to obtain the final total imaging data volume. Then, according to the start and end time of the data transmission arc segment planned in step (2.3) and the total imaging data volume calculated, the data transmission task is planned and arranged to determine the downlink data for each data transmission task; Then, according to the simulation step length preset in step (2.1), the on-board real-time data storage capacity is calculated step by step; it is determined whether the on-board real-time data storage capacity exceeds the on-board fixed storage capacity. If it exceeds, it means that the on-board data dynamic balance is not satisfied, and a low-priority task is deleted from the task pool, and the task pool is updated; The calculation method of 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 moment, the data occupied at the initial moment on the satellite, and the data occupied by other satellite engineering parameters, minus the data volume that has been downloaded in the data transmission task executed up to the current simulation moment.

11. The digital model integrated modeling method of remote sensing system for satellite-ground collaborative mission planning according to claim 1 is characterized by: In the step (2.6): The power supply and distribution model calculates the charging power at the current simulation time based on the orbit information and attitude sequence, taking into account the influence of the ground shadow area and the sun-illuminated area; Then, according to the simulation step size preset in step (2.1), the integral of charging power over time is calculated step by step to obtain the total power generation, and then the total power consumption, and the real-time battery capacity percentage on the satellite is calculated; Determine whether the real-time battery capacity percentage on the satellite is greater than the maximum allowable discharge depth of the battery. If so, it means that the dynamic balance of energy on the satellite is not met. Delete a low-priority task from the task pool and update the task pool. The percentage of the real-time battery capacity on the satellite is: the total power consumption up to the current simulation time divided by the sum of the battery capacity and the total power generation at the initial time on the satellite.

12. The integrated modeling method of digital model of remote sensing system for satellite-ground collaborative mission planning according to claim 11 is characterized in that: The power supply and distribution model calculates the charging power at the current simulation time based on the orbit information and attitude sequence, taking into account the influence of the earth shadow area and the sun-illuminated area. The specific method is as follows: The attitude angle of the satellite at each moment in the attitude sequence is obtained. According to the relative position of the satellite in orbit and the sun, the projection direction of the sunlight is calculated, and the shielding of the solar wing by the satellite body is calculated to obtain the unobstructed solar wing patch. The solar incident vector on the unobstructed solar wing patch at each moment is calculated. Under the full regulation system, the relationship between the illumination angle of the unobstructed solar wing patch and the short-circuit current is calculated to obtain the charging power provided by the solar array at the current simulation moment.

13. The integrated modeling method of digital model of remote sensing system for satellite-ground collaborative mission planning according to claim 1 is characterized by: In the step (2.7): The on-board processing model estimates the imaging performance indicators, including target detection probability, target recognition probability and false alarm rate. The mission effectiveness model evaluates the mission effectiveness based on the target acquisition quantity and acquisition accuracy during the entire simulation period.

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