Low-orbit satellite data volume prediction and evaluation planning method based on imaging model

Abstract

The application discloses a low-orbit satellite data volume prediction and evaluation planning method based on an imaging model, and relates to the field of space remote sensing satellite task planning. The application receives a task request and acquires a real-time on-board resource snapshot, maps task parameters to specific load working sequences, calculates data volume based on the physical imaging principles of optics and SAR, performs fine estimation, and further estimates the storage space and download time required for imaging data based on the satellite fixed storage mechanism and data download encoding mechanism. Based on the estimation results and multi-dimensional resource states such as storage and data transmission, the system autonomously outputs a decision result of approved execution, parameter adjustment suggestion or suggestion rejection. The application realizes the leap from experience estimation to model driving in data volume estimation, continuously optimizes the model precision through on-orbit self-learning mechanism, and significantly improves the reliability, safety and intelligent level of ground task planning, thereby providing key technical support for the intelligentization of future remote sensing satellites and the autonomous cooperation of constellations.

Description

Technical Field

[0001] This invention relates to the field of aerospace remote sensing satellite mission planning, and in particular to a low-Earth orbit satellite data volume prediction and assessment planning method based on imaging models. It is a systematic method that addresses on-board resource constraints, is based on the physical principles of optical and synthetic aperture radar (SAR) imaging, and integrates real-time resource status and on-orbit learning capabilities to achieve accurate prediction of imaging mission data volume and real-time feasibility assessment. Background Technology

[0002] Low-Earth orbit (LEO) remote sensing satellites, especially those with high-resolution optical and SAR imaging capabilities, are the core infrastructure of modern Earth observation systems. As remote sensing satellites develop towards higher resolution, faster response times, and greater intelligence, the amount of satellite imaging data is exploding, while resources such as onboard storage capacity, data transmission bandwidth, and energy supply are severely limited. This contradiction becomes particularly prominent when addressing advanced application modes such as emergency observation, multi-satellite collaboration, and on-orbit autonomous mission planning.

[0003] Currently, in satellite mission planning and data management, the main technical bottlenecks in estimating the amount of data and assessing the feasibility of imaging missions are as follows:

[0004] (1) The data volume prediction model is too simplistic and lacks theoretical basis.

[0005] Most existing technologies employ linear estimation methods based on fixed data rates. For example, a fixed empirical value of "megabytes per second" or "gigabytes per revolution" is set for each imaging mode. This method completely deviates from the physical nature of imaging, ignoring the fundamental impact of dynamic changes in imaging parameters (such as lateral angle, resolution, and spectral / polarization mode) on data volume. Its theoretical flaws are: for optical imaging, it does not consider the changes in pixel count, quantization bits, and swath width and integration time caused by variations in lateral angle; for SAR imaging, it does not consider the order-of-magnitude differences in data rates caused by different imaging modes (strip, scan, spotlight), polarization methods, and parameters such as PRF and frame length under the corresponding modes. This simplified model, lacking physical theoretical support, inevitably leads to a certain amount of prediction bias, making subsequent mission planning and resource allocation based on an unreliable data model.

[0006] (2) Disconnect between resource assessment and task planning

[0007] Traditional task planning and resource management systems are often two relatively independent modules. The planning system is mainly responsible for calculating the visible time window, while resource management simply monitors the current remaining capacity. There is a lack of deep, forward-looking coupling between the two. When scheduling tasks, the planning system cannot accurately know how much storage space a task will consume, whether it will affect the download of subsequent planned tasks, or whether the energy is sufficient to support the execution of the entire task. This disconnect easily leads to two consequences: either being too conservative and wasting valuable observation opportunities, or being too aggressive and causing memory overflow, data loss, or insufficient energy to interrupt the task.

[0008] (3) Lack of intelligent conflict resolution and decision support capabilities

[0009] When new imaging mission requests conflict with existing plans or resource status, existing systems typically only provide a binary "yes" or "no" answer, lacking the ability to offer constructive adjustment solutions. Ground operators need to manually try adjusting various parameters based on experience, which may involve multiple iterations. This process is cumbersome and inefficient, failing to meet the real-time requirements of emergency response or autonomous decision-making.

[0010] (4) The model does not have adaptive and evolutionary capabilities.

[0011] The performance of satellite payloads and the efficiency of compression algorithms may change after the satellite is in orbit, and the fixed models preset on the ground cannot reflect these changes. As the time in orbit increases, the estimation error of the amount of data may gradually accumulate and amplify, and the availability of the system will become increasingly worse.

[0012] Therefore, there is an urgent need in this field for an innovative method that can fundamentally solve the above problems and achieve a leap from "fixed calculation formulas based on human experience" to "computational models driven by imaging principles" and from "one-way binary judgment" to "two-way recommendation decision-making". Summary of the Invention

[0013] In view of this, this invention proposes a method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model. The main objective is:

[0014] (1) Establish a high-fidelity data volume refinement prediction model based on the physical principles of optical and SAR satellite payload imaging, fundamentally improve the prediction accuracy, and provide a complete and systematic theoretical formula.

[0015] (2) To achieve deep and real-time coupling between imaging mission planning and multi-dimensional resources such as on-board storage, data transmission, and energy, and to ensure the feasibility and security of mission planning, the core is to use accurate data volume estimation as the direct input for planning decisions.

[0016] (3) Design a decision support mechanism with intelligent conflict detection and multi-scheme adjustment suggestions to improve the flexibility and automation of task planning.

[0017] (4) An on-orbit self-learning adaptive calibration mechanism is introduced to enable the prediction model to be continuously optimized as the satellite operates in orbit, thus maintaining long-term accuracy.

[0018] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0019] A method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model includes the following steps:

[0020] Step 1, Task Request Reception and Resource Snapshot Acquisition: Receive imaging task requests from ground control; simultaneously, acquire real-time resource status snapshots of the satellite platform, including the current used capacity, total capacity, and safety threshold coefficient of the onboard solid-state storage; as well as the number of data transmission windows planned within future fixed orbit cycles, the time, duration, and equivalent data transmission bandwidth of each data transmission window;

[0021] Step 2, Imaging task parameterization and satellite imaging window parameter calculation;

[0022] Step 3: Calculate the compressed optical imaging data volume and SAR imaging data volume based on physical principles, and obtain the total low-orbit satellite data volume after removing invalid data.

[0023] Step 4: Calculate the required onboard solid-state storage capacity based on the total amount of low-Earth orbit satellite data, and then conduct a comprehensive assessment of mission feasibility from multiple dimensions; based on the assessment results, perform decision analysis and mission planning output.

[0024] Step 5: After the mission is actually executed and the data is downloaded, record the actual amount of data generated by the mission; compare the actual amount of data with the required physical storage capacity of the on-board solid-state memory calculated in Step 4, and adjust and update the compression ratio based on the comparison error, and carry out the next round of low-orbit satellite data volume prediction and evaluation planning based on the imaging model.

[0025] Furthermore, the specific method for step 2 is as follows:

[0026] Step 201, Imaging task parameterization:

[0027] The imaging task request is analyzed, the latitude and longitude range of the target area is extracted, and the cloud cover level of the target area is obtained based on the meteorological forecast data provided by the meteorological department. The interference level of the target area is obtained based on historical radio frequency interference databases or real-time spectrum monitoring data. ; ; ;

[0028] Acquire and standardize satellite platform parameters, payload physical parameters, and mission requirement parameters to form a complete mission parameter set, including satellite orbital altitude. The unit is meters, and the focal length of the optical satellite payload. The unit is meters, the length of an optical satellite detector. The unit is meters, and the ground sampling distance along the track direction. Ground sampling distance in the vertical direction The unit is meters, and the number of spectral channels of the optical payload. Channel quantization bits The unit is bits;

[0029] Step 202, Calculation of satellite imaging window parameters:

[0030] Based on precise orbital ephemeris and payload constraints, the visible time window of the satellite over the target region is calculated using orbital mechanics and geometric models. Within each visible time window, the specific operating parameters required for the payload to perform imaging are solved.

[0031] For optical imaging satellites, determine the imaging mode: including single-strip / multi-strip stitching / multi-angle imaging, and calculate the side-swing angle. The unit is radians; satellite imaging start time Imaging end time Then calculate the imaging duration. The unit is seconds;

[0032] For SAR imaging satellites, determine the imaging mode (including strip / scan / spotlight) and calculate the satellite imaging start time. Imaging end time Then calculate the synthesis aperture time. The unit is seconds, distance window reception time. The unit is seconds; and the pulse repetition frequency is calculated. The unit is Hz.

[0033] Furthermore, the specific method for calculating the compressed optical imaging data volume in step 3 is as follows:

[0034] Step 301a, calculate the instantaneous imaging swath width and the number of pixels in a single-row detector:

[0035] ;

[0036] ;

[0037] in, The number of pixels in a single row detector is dimensionless. The instantaneous imaging swath width is expressed in meters.

[0038] Step 302a: Calculate the satellite's along-orbit imaging length and the total number of imaging rows:

[0039] ;

[0040] in, The satellite's ground velocity is expressed in meters per second. The Earth's gravitational constant has a value of 3.986 × 10⁻⁶. 14 m 3 / s 2 , The average radius of the Earth is taken as 6371 km;

[0041] ;

[0042] ;

[0043] in, The total number of imaging rows is dimensionless. The image length along the satellite's orbit is expressed in meters.

[0044] Step 303a, calculate the amount of raw data for a single row of pixels:

[0045] Based on the detector's physical structure and imaging principle, the amount of raw data generated per row is:

[0046] ;

[0047] in, This represents the amount of raw data per line, expressed in bits.

[0048] Step 304a, calculate the total amount of original data:

[0049] ;

[0050] in, Total raw data volume, in bits;

[0051] Step 305a, calculate the amount of data after compression:

[0052] Calculate the compressed data size by applying the selected lossless or lossy compression algorithm and its nominal compression ratio:

[0053]

[0054] in, The compressed optical imaging data size, in bits. The optical data compression ratio is dimensionless.

[0055] Furthermore, the specific method for calculating the compressed SAR imaging data volume in step 3 is as follows:

[0056] Step 301b, calculate the number of sampling points in the azimuth and range directions:

[0057] ;

[0058] in, This represents the number of sampling points in the azimuth direction. The distance is the number of sampling points, dimensionless. The sampling frequency of the satellite receiver's ADC for the echo signal is greater than or equal to twice the bandwidth of the echo signal, and the unit is Hz;

[0059] Step 302b: Determine the number of bytes occupied by each sampling point:

[0060] ;

[0061] in, The number of bytes per sampling point, in bytes. The sampling accuracy of the satellite receiver's ADC is set to 8 / 16 / 32 bits.

[0062] Step 303b, calculate the data volume of a single polarization channel:

[0063] ;

[0064] in, This represents the raw data volume of a single polarization channel of the satellite, in bytes.

[0065] Step 304b, calculate the total amount of multipolar data:

[0066] ;

[0067] in, This represents the total raw data volume across multiple polarization channels, in bytes. The polarization channel number refers to the number of combinations of electromagnetic wave polarization that the radar can simultaneously transmit and receive. The value is 1 for single polarization, 2 for dual polarization, and 4 for full polarization.

[0068] Step 305b, calculate the compressed data volume:

[0069] Applying a compression algorithm for raw SAR data, calculate the amount of compressed data:

[0070] ;

[0071] in, This refers to the compressed SAR imaging data volume, in bits. This represents the SAR data compression ratio, which is dimensionless.

[0072] Furthermore, the specific method for obtaining the total amount of low-Earth orbit satellite data after removing invalid data in step 3 is as follows:

[0073] ;

[0074] ;

[0075] ;

[0076] in, and These are the invalid data scaling factors for optical imaging and SAR imaging, respectively. This represents the total amount of low-orbit satellite data.

[0077] Furthermore, step 4 is specifically implemented as follows:

[0078] Step 401: Calculate the onboard solid-state memory logical storage space required for the total amount of low-Earth orbit satellite data. :

[0079] ;

[0080] in, This is the data packaging cost factor, with a value between 2% and 8%. For error correction coding efficiency, the value should be between 80% and 95%. Fixed metadata overhead includes file control blocks, directory entry structures, bad block mapping tables, wear leveling tables, and system reserved areas.

[0081] Step 402, from the on-board solid-state memory logical storage space Calculate the required physical storage capacity of on-board solid-state memory. :

[0082] ;

[0083] in, The wear leveling retention factor has a value range of [value range missing]. ; This represents the engineering design margin, with a range of values. ;

[0084] Step 403, determine the feasibility of storage capacity:

[0085] ;

[0086] in, This represents the current used capacity of the onboard solid-state storage. This represents the total capacity of the onboard solid-state storage. This represents the safety threshold coefficient for on-board solid-state storage. ;

[0087] Step 404, determine the feasibility of the data transmission window:

[0088]

[0089] in, This represents the number of files to be downloaded from the onboard solid-state storage. The first in on-board solid-state memory The amount of data to be transferred for the file to be downloaded. The equivalent data transmission bandwidth of the data transmission window. For the number of data transmission windows, For the first The number of windows is transmitted for the duration of the window;

[0090] ;

[0091] The first in on-board solid-state memory The required on-board solid-state storage capacity for each file to be downloaded This is the protocol overhead coefficient. This is the coding efficiency coefficient;

[0092] ;

[0093] in, This is the frame header overhead, including frame count, virtual channel ID, and data length, used for data frame identification and management, with a value ranging from 3% to 5%. Synchronization overhead, including synchronization words used for data alignment, ranges from 1% to 2%; The overhead for verification includes cyclic redundancy check (CRC) to detect data errors during transmission, with a value ranging from 2% to 4%. This is the encryption overhead, including the encryption algorithm header information, to ensure the security of data transmission; its value ranges from 0.5% to 1%.

[0094] Coding efficiency coefficient Values , , , ;

[0095] Step 405: If both storage capacity feasibility and data transmission window feasibility are met, the mission is approved and corresponding storage space is reserved for the mission in the on-board solid-state storage.

[0096] Otherwise, adjust the task parameters:

[0097] For optical imaging: adjustments can be made by reducing the number of spectral channels of the optical payload, increasing the ground sampling distance along the track, increasing the ground sampling distance in the vertical track direction, and increasing the optical data compression ratio;

[0098] For SAR imaging: Adjustments can be made by adopting one or more of the following methods: simplifying the polarization mode and increasing the SAR data compression ratio;

[0099] If, after adjusting the task parameters once, both storage capacity feasibility and data transmission window feasibility cannot be simultaneously satisfied, then the task will be rejected.

[0100] Furthermore, step 5 is specifically implemented as follows:

[0101] Step 501: After the satellite imaging mission is executed and the data is downloaded, the system records the actual amount of data. By calculating the required physical storage capacity of the on-board solid-state memory. relative error :

[0102] ;

[0103] Step 502, calculate the model correction factor :

[0104] ;

[0105] in, The initial value is 1. This represents historical relative error. This is the proportionality coefficient, and its value range is... ; The integral coefficient has a range of values ​​of 1. And satisfy ;

[0106] Step 503, Correction or :

[0107] ;

[0108] .

[0109] Due to the adoption of the above technical solution, the beneficial effects of this invention compared with the prior art are as follows:

[0110] 1. A qualitative leap in prediction accuracy and theoretical completeness: By constructing differentiated models based on imaging physics and introducing invalid data filtering, the data volume prediction has been elevated from rough empirical estimation to a precise and formulaic physical model-driven stage. A complete and traceable theoretical formula system has been provided, enhancing the scientificity and credibility of the scheme.

[0111] 2. Resource-aware intelligent planning is achieved, making decision-making more reliable: By deeply coupling task planning with real-time resource status based on physical prediction, a "resource-driven" task planning mode is realized. The task planning decision-making mode is transformed from the traditional "guess-based" to "accurate calculation-based," and accurate data volume prediction fundamentally avoids task failure or data loss due to resource misjudgment.

[0112] 3. Significantly improved automation and intelligence: Based on the rapid recalculation capability of the physical model in steps 3 and 4, the system can automatically perform conflict detection and generate quantitative and executable adjustment plans, freeing ground operators from tedious manual trial and error, greatly improving the efficiency of mission planning and the satellite's autonomous operation capability, especially suitable for emergency response and on-board autonomous mission planning scenarios.

[0113] 4. Possesses self-evolution and long-term reliability: The introduced on-orbit self-learning mechanism enables the system to adapt to the slow changes in payload performance and the changes in the on-orbit environment, ensuring that the prediction model can maintain high accuracy throughout the entire satellite's lifespan.

[0114] 5. Maximizing resource utilization: Accurate forecasting and intelligent decision-making avoid over-reservation or shortage of satellite storage resources, enabling satellites to perform more effective imaging tasks under the same resource conditions, thereby indirectly improving the economic benefits and application value of satellites. Attached Figure Description

[0115] Figure 1 This is an overall flowchart of a low-orbit satellite data volume prediction and evaluation planning method based on an imaging model, as described in an embodiment of the present invention.

[0116] Figure 2 This is a flowchart illustrating the data volume estimation process in steps 3 and 4 of this invention. Detailed Implementation

[0117] The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0118] A method for predicting and evaluating low-Earth orbit (LEO) satellite data volume based on an imaging model is proposed. This embodiment uses a virtual LEO remote sensing satellite with both high-resolution optical and C-band SAR capabilities as an example. Figure 1This paper describes the complete application of the method of the present invention in an emergency disaster reduction mission, and provides detailed formulaic calculations and comparisons.

[0119] Step 1: Receiving Task Requests and Obtaining Resource Snapshots

[0120] Based on historical mission planning schemes and real-time satellite telemetry data, the ground control system obtains the following real-time snapshot of satellite resources:

[0121] Storage status: Total capacity is 1024GB, used capacity The total capacity is 500GB, including a 10GB file to be distributed (located within the used capacity), with a security threshold of 0.9.

[0122] Data transmission status: The next data transmission window will be in 1 hour, with a duration of 10 minutes, a downlink bandwidth of 450Mbps, and an estimated data transmission capacity of approximately 33.75GB.

[0123] Step 2: Imaging mission parameterization and satellite imaging window parameter calculation

[0124] Based on mission simulation, the ground control system derived the following core parameters for the imaging mission: The target area is a flood-stricken area (rectangle, upper left 100°E / 31°N, upper right 103°E / 31°N, lower right 103°E / 31°N, lower left 100°E / 33°N), and the resolution requirement is 0.5 meters, which is the ground sampling distance along the rail direction and the ground sampling distance perpendicular to the rail direction.

[0125] Step 201, Imaging task parameterization:

[0126] The ground control system determines the orbit of a specific satellite by calculating the orbits of multiple satellites. There was an imaging window for the target area, and the satellite's resolution was 0.5 meters, which met the mission requirements. The mission was then assigned to the satellite.

[0127] According to the mission requirements, the target area is a rectangle (top left 100°E / 31°N, top right 103°E / 31°N, bottom right 103°E / 31°N, bottom left 100°E / 33°N), with a required resolution of 0.5 meters. Calculate the cloud cover level for the target area based on meteorological forecast data provided by the meteorological department. (Weiyun) assesses the interference level of the target area based on historical radio frequency interference databases or real-time spectrum monitoring data. (No interference).

[0128] Simultaneously based on satellite The satellite's orbital altitude can be determined from its technical specifications. , focal length of optical satellite payload Length of optical satellite detector Target area location, spatial resolution requirement = 0.5 meters, number of spectral channels for optical payload Quantization bit depth wait.

[0129] Step 202, Satellite imaging window calculation:

[0130] Based on satellite Based on the precise orbital ephemeris and payload constraints, the visible time window of the satellite over the target area was calculated using orbital mechanics and geometric models. It was found that the satellite has a chance to pass over the target area once in 60 minutes (2025-12-01 12:08:00~2025-12-01 12:08:10), which can form an optical imaging window and a SAR imaging window lasting 10 seconds. At the same time, the side swing angle of the optical payload was calculated to be 15°.

[0131] Therefore, the specific operating parameters of the imaging window were determined:

[0132] For optical imaging satellites, the imaging mode is single-strip, and the satellite imaging start time is... =2025-12-01 12:08:00, Imaging End Time =2025-12-01 12:08:10, Side swing angle =15°, imaging time =10 seconds, with both vertical and along-rail spatial resolutions of 0.5 meters.

[0133] For SAR imaging satellites, the imaging mode is stripe, and the satellite imaging start time is... =2025-12-01 12:08:00, Imaging End Time =2025-12-01 12:08:10, and then calculate the synthesis aperture time. =10 seconds =500 And solve for the pulse repetition frequency. .

[0134] Step 3: Refined data volume prediction based on physical principles (combined with...) Figure 2 )

[0135] (1) Optical calculation process (This step is the core and theoretical foundation of the method, applying the formula system of this invention. It starts the dedicated data generation model of optical or SAR for calculation. The essential difference from the traditional fixed data rate method is that each sub-process of this step is based on clear physical principles and mathematical formulas, forming a complete formula system):

[0136] Step S301a Calculation of instantaneous imaging swath width and number of pixels per row:

[0137] enter Calculate instantaneous width .

[0138] This formula describes the plane dimensions ( The process of magnifying the image to the ground plane through projection relationships, lateral sway angle The width of the image is directly affected by the cosine function. Traditional methods completely ignore this crucial geometric relationship.

[0139] According to the satellite instantaneous swath width Ground sampling distance in the vertical direction Derive the number of pixels in a single-row detector:

[0140]

[0141] Step 302a, Calculation of satellite along-orbit imaging length and total number of imaging rows:

[0142] satellite ground speed Satellite imaging length along orbit Calculate the total number of rows .

[0143] Step 303a, Calculation of raw data volume for a single row of pixels:

[0144] enter Calculate the amount of data in a single row. This formula is derived directly from the physical characteristics of the detector's pixel count, spectral dimension, and quantization depth.

[0145] Step 304a, Calculation of total raw data volume: Total amount of raw data .

[0146] Step 305a: Input compressed data volume .

[0147] (2) SAR calculation process (using the formula system of this invention):

[0148] Step 301b: Identified as stripe pattern, input Calculate the number of sampling points in the azimuth and range directions:

[0149]

[0150] The number of azimuth sampling points (the total number of pulses collected by the satellite during the entire synthetic aperture time, dimensionless). The pulse repetition frequency (determines the azimuth sampling rate, in Hz). Synthetic aperture time (the duration of effective imaging of the same ground target by a satellite, in seconds). The number of range sampling points (the number of sampling points for each pulse echo signal by the satellite, dimensionless); This is the sampling frequency of the receiver's ADC for the echo signal (according to the Nyquist sampling theorem, it must be greater than or equal to twice the signal bandwidth, in Hz). Range window reception time (the time it takes for the satellite to turn on the receiver to receive the entire strip echo, in seconds).

[0151] Step 302b: Determine the number of bytes occupied by each sampling point.

[0152] Step 303b: Calculate the data volume of a single polarization channel

[0153] Step 304b: Total amount of raw data in the bipolar mode ;

[0154] in, This represents the total amount of raw data (in bytes) across multiple polarization channels. Each polarization channel is essentially an independent data acquisition stream, with the satellite independently storing its complete raw data matrix (including range and azimuth sampling) for each channel. The number of polarization channels refers to the number of combinations of electromagnetic wave polarization that the radar can simultaneously transmit and receive (e.g., single polarization = 1, dual polarization = 2, full polarization = 4). Traditional fixed data rate methods completely mask the decisive influence of different imaging modes (with huge bandwidth differences) and polarization methods on the data rate.

[0155] Step 305b: Compressed data volume

[0156] (3) Correction of effective data volume

[0157] Step 306, Invalid data prediction and deduction:

[0158] For optical imaging satellites, cloud cover levels (Target area micro-clouds), calculation For SAR imaging satellites, due to interference levels (Target area is free of interference), calculate .

[0159] Estimated final effective data from the optical imaging satellite: .

[0160] Estimated final effective data value from SAR imaging satellites: .

[0161] Total data volume estimate (in this invention): .

[0162] (4) Calculation of actual space occupied by satellite solid-state storage: Based on the design mechanism of satellite on-board solid-state storage, a calculation model for solid-state storage space occupancy is proposed through systematic analysis of various factors affecting storage capacity. A precise data calculation formula is established, the technical basis and value range of each parameter are clarified, and the solid-state storage space is decomposed into two orthogonal processes: logical data formation and physical space mapping. This forms a quantitative model of the entire link overhead from data generation to physical storage, providing precise support for engineering design and effectively avoiding the problems of insufficient storage capacity estimation or excessive redundancy. The specific calculation steps are as follows:

[0163] Step 307, Logical storage space calculation:

[0164] enter Then the logical storage space is:

[0165] .

[0166] in, The final amount of raw data for the imaging payload generated after the above detailed calculations reflects the information acquisition capability of the mission; The data packaging overhead coefficient is determined by the data frame structure. It is generally the ratio of the sum of the source packet header, the transmission frame header and the frame tail checksum to the length of the data field. It reflects the efficiency characteristics of the communication protocol and is generally between 2% and 8%. To ensure error correction coding efficiency, the error correction coding scheme is selected by the satellite developer based on requirements such as single-event upset capability, on-board processing capacity limitations, and system power consumption constraints. This represents the cost of the satellite's radiation resistance, such as... or Encoding method The value is generally between 80% and 95%. The fixed metadata overhead is determined by the specific satellite storage file system architecture design and generally includes file control blocks, directory entry structures, bad block mapping tables, wear leveling tables, and a small amount of system reserved areas. The precise value is obtained through file system formatting tests and prototype system verification, representing the basic cost of storage management.

[0167] Step 308, Physical storage capacity calculation:

[0168] Let wear leveling retention coefficient be set Engineering design margin Then the physical storage capacity is:

[0169] .

[0170] in, The wear leveling retention factor is determined by the durability characteristics of the flash memory medium in the satellite's storage and the mission lifespan requirements, and typically ranges from [value range missing]. ; This represents a design margin, determined by the task risk level, technology maturity, and historical engineering experience. It reflects the stability requirements of engineering practice and typically ranges from [value range missing]. These two parameters are key parameters for ensuring the long-term reliability and engineering robustness of satellite storage systems.

[0171] Step 4, Multi-dimensional Task Feasibility Comprehensive Assessment: This step is the core application of data volume estimation in task planning. It utilizes the accurate, physically driven data obtained in Step 3. The estimated value is deeply integrated with the resource snapshot from step 1 to generate a quantitative feasibility report.

[0172] Step 401, Feasibility assessment of storage capacity: Based on the scenario set in Step 1, the total satellite storage capacity is 1024GB, and the used capacity... 500GB, security threshold If the value is 0.9, then the total used capacity of the solid storage after this storage is... Less than the solid storage safe capacity The feasibility assessment was passed.

[0173] Step 402, Feasibility assessment of data transmission window:

[0174] Based on the scenario setting in step 1 and the results above, there are two files waiting to be downloaded in the fixed storage (10GB and 9.37GB respectively). The next download window will be 1 hour later, with a duration of 10 minutes and a downlink bandwidth of 450Mbps.

[0175] Before data transfer occurs from a stored file, various protocol and encoding overheads are introduced, significantly increasing the actual amount of data transmitted and thus affecting data transfer time. Therefore, it is necessary to construct an accurate and complete formula for calculating the amount of data to be transferred. First, the amount of data to be transferred is calculated based on the original stored file's data volume. and The original file data size in the fixed storage is given by the protocol overhead coefficient. 5 (of which, The value is 0.04. The value is 0.01. The value is 0.03. (Value 0.005) Coding efficiency coefficient If the encoding method is 7 / 8, then:

[0176] ,

[0177] .

[0178] Secondly, determine whether all stored files can be downloaded in subsequent data transfer windows by inputting... Satellite effective data transmission rate Number of currently available data transmission windows ,but: Less than 10 minutes are available.

[0179] Step 5: Decision Analysis and Results Output

[0180] System output instruction A: Execution approved. Immediately reserve 9.37GB precisely from the 921.6GB available capacity and inject this task into the mission schedule, awaiting execution.

[0181] Step 6, adaptive calibration of the computational model

[0182] After the mission was completed, the data processing system reported that the actual amount of valid data received was: 5.2GB from optical satellites and 4.2GB from SAR satellites, for a total of 9.4GB.

[0183] Calculation error:

[0184] The error is fed into the error controller, according to the formula. Output correction values, adjust model parameters, and achieve self-optimization.

[0185] The previous value, This is the result of this calculation; The initial value is strictly set to 1.0, indicating that during the initial on-orbit phase of the satellite, the system fully trusts and adopts the preset model parameters (such as compression ratio) obtained from precise ground calibration and testing. and Therefore, the correction factor should start learning from the "invalid positive" state (with a value of 1.0);

[0186] and The controller coefficients form a proportional-integral controller used to smoothly adjust the number of model adoptions. This is the proportionality coefficient, used to provide an immediate response to the current prediction error. A larger value results in a greater adjustment to a single error and a faster response; however, an excessively large value may lead to overshoot or oscillation. The typical value range is [range missing]. ; This is the integral coefficient, used to accumulate historical errors, eliminate steady-state deviations, and help compensate for long-term slow drift of the system. Its typical value range is [value range missing]. And usually satisfy In the absence of prior knowledge, we can assume... and This is a relatively conservative and stable starting point.

[0187] Quantitative comparative analysis with traditional methods:

[0188] Traditional method (fixed data rate): Assuming an empirical data rate of 5Gbps for optical imaging mode and 4Gbps for SAR imaging mode.

[0189] Estimated amount of optical imaging data: .

[0190] Estimated SAR imaging data volume: .

[0191] Total data volume estimate (empirical value): .

[0192] Comparative conclusion: The traditional method's estimated value (11.25 GB) is 1.2 times that of the method in this invention (9.37 GB), indicating a significant overestimation. If data volume calculations, mission planning, and data transmission window feasibility assessments are based on this erroneous estimate, the system is likely to incorrectly reject the urgent mission due to "insufficient satellite storage space," leading to decision-making errors and missed critical observation opportunities. This fully demonstrates the decisive role of the refined physical formula in this invention in improving the accuracy of mission planning.

[0193] The core innovation of this invention lies in the construction of a new satellite mission and storage management model that is state-aware, formula-driven, and closed-loop optimized. Its most prominent technological advancement is reflected in the establishment and implementation of a complete and rigorous physical formula system for estimating on-board data volume.

[0194] This formulaic system fundamentally overturns the traditional empirical models that rely on fixed data rates. Through rigorous theoretical derivation and parameterized design, it precisely correlates the specific parameters of the imaging task (side angle, resolution, mode, etc.) with the final data volume, giving the prediction results a solid physical foundation and high interpretability. This fundamental improvement provides reliable input for subsequent resource assessment and intelligent decision-making, thereby ensuring the scientific validity and correctness of the entire closed-loop system's decisions. It will accelerate the evolution of low-Earth orbit remote sensing satellites towards "autonomy and intelligence," and has milestone significance for promoting the intelligence of future satellites and the autonomous collaboration of constellations.

[0195] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model, characterized in that, Includes the following steps: Step 1, Task Request Reception and Resource Snapshot Acquisition: Receive imaging task requests from ground control; simultaneously, acquire real-time resource status snapshots of the satellite platform, including the current used capacity, total capacity, and safety threshold coefficient of the onboard solid-state storage; as well as the number of data transmission windows planned within future fixed orbit cycles, the time, duration, and equivalent data transmission bandwidth of each data transmission window; Step 2, Imaging task parameterization and satellite imaging window parameter calculation; Step 3: Calculate the compressed optical imaging data volume and SAR imaging data volume based on physical principles, and obtain the total low-orbit satellite data volume after removing invalid data. Step 4: Calculate the required onboard solid-state storage capacity based on the total amount of low-Earth orbit satellite data, and then conduct a comprehensive assessment of mission feasibility from multiple dimensions; based on the assessment results, perform decision analysis and mission planning output. Step 5: After the mission is actually executed and the data is downloaded, record the actual amount of data generated by the mission; compare the actual amount of data with the required physical storage capacity of the on-board solid-state memory calculated in Step 4, and adjust and update the compression ratio based on the comparison error, and carry out the next round of low-orbit satellite data volume prediction and evaluation planning based on the imaging model.

2. The method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model according to claim 1, characterized in that, The specific method for step 2 is as follows: Step 201, Imaging task parameterization: The imaging task request is analyzed, the latitude and longitude range of the target area is extracted, and the cloud cover level of the target area is obtained based on the meteorological forecast data provided by the meteorological department. The interference level of the target area is obtained based on historical radio frequency interference databases or real-time spectrum monitoring data. ; ; ; Acquire and standardize satellite platform parameters, payload physical parameters, and mission requirement parameters to form a complete mission parameter set, including satellite orbital altitude. The unit is meters, and the focal length of the optical satellite payload. The unit is meters, the length of an optical satellite detector. The unit is meters, and the ground sampling distance along the track direction. Ground sampling distance in the vertical direction The unit is meters, and the number of spectral channels of the optical payload is... Channel quantization bits The unit is bits; Step 202, Calculation of satellite imaging window parameters: Based on precise orbital ephemeris and payload constraints, the visible time window of the satellite over the target area is calculated using orbital mechanics and geometric models. Within each visible time window, the specific operating parameters required for the payload to perform imaging are solved. For optical imaging satellites, determine the imaging mode: including single-strip / multi-strip stitching / multi-angle imaging, and calculate the side-swing angle. The unit is radians; satellite imaging start time Imaging end time Then calculate the imaging duration. The unit is seconds; For SAR imaging satellites, determine the imaging mode (including strip / scan / spotlight) and calculate the satellite imaging start time. Imaging end time Then calculate the synthesis aperture time. The unit is seconds, distance window reception time. The unit is seconds; And solve for the pulse repetition frequency. The unit is Hz.

3. The method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model according to claim 2, characterized in that, The specific method for calculating the compressed optical imaging data volume in step 3 is as follows: Step 301a, calculate the instantaneous imaging swath width and the number of pixels in a single-row detector: ； ； in, The number of pixels in a single row detector is dimensionless. The instantaneous imaging swath width is expressed in meters. Step 302a: Calculate the satellite's along-orbit imaging length and the total number of imaging rows: ； in, The satellite's ground velocity is expressed in meters per second. The Earth's gravitational constant has a value of 3.986 × 10⁻⁶. 14 m 3 / s 2 , The average radius of the Earth is taken as 6371 km; ； ； in, The total number of imaging rows is dimensionless. The image length along the satellite's orbit is expressed in meters. Step 303a, calculate the amount of raw data for a single row of pixels: Based on the detector's physical structure and imaging principle, the amount of raw data generated per row is: ； in, This represents the amount of raw data per row, expressed in bits. Step 304a, calculate the total amount of original data: ； in, Total raw data volume, in bits; Step 305a, calculate the amount of data after compression: Calculate the compressed data size by applying the selected lossless or lossy compression algorithm and its nominal compression ratio: ； in, The compressed optical imaging data size, in bits. The optical data compression ratio is dimensionless.

4. The method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model according to claim 2, characterized in that, The specific method for calculating the compressed SAR imaging data volume in step 3 is as follows: Step 301b, calculate the number of sampling points in the azimuth and range directions: ； in, This represents the number of sampling points in the azimuth direction. The distance is the number of sampling points, dimensionless. The sampling frequency of the satellite receiver's ADC for the echo signal is greater than or equal to twice the bandwidth of the echo signal, and the unit is Hz; Step 302b: Determine the number of bytes occupied by each sampling point: ； in, The number of bytes per sampling point, in bytes. The sampling accuracy of the satellite receiver's ADC is set to 8 / 16 / 32 bits. Step 303b, calculate the data volume of a single polarization channel: ； in, This represents the raw data volume of a single polarization channel of the satellite, in bytes. Step 304b, calculate the total amount of multipolar data: ； in, This represents the total raw data volume across multiple polarization channels, in bytes. The polarization channel number refers to the number of combinations of electromagnetic wave polarization that the radar can simultaneously transmit and receive. The value is 1 for single polarization, 2 for dual polarization, and 4 for full polarization. Step 305b, calculate the compressed data volume: Applying a compression algorithm for raw SAR data, calculate the amount of compressed data: ； in, This refers to the compressed SAR imaging data volume, in bits. This represents the SAR data compression ratio, which is dimensionless.

5. A method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model, as described in claim 3 or 4, characterized in that... The specific method for obtaining the total amount of low-Earth orbit satellite data after removing invalid data in step 3 is as follows: ； ； ； in, and These are the invalid data scaling factors for optical imaging and SAR imaging, respectively. This represents the total amount of low-orbit satellite data.

6. The method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model according to claim 5, characterized in that, The specific method for step 4 is as follows: Step 401: Calculate the onboard solid-state memory logical storage space required for the total amount of low-Earth orbit satellite data. : ； in, This is the data packaging cost factor, with a value between 2% and 8%. For error correction coding efficiency, the value should be between 80% and 95%. Fixed metadata overhead includes file control blocks, directory entry structures, bad block mapping tables, wear leveling tables, and system reserved areas. Step 402, from the on-board solid-state memory logical storage space Calculate the required physical storage capacity of on-board solid-state memory. : ； in, The wear equalization retention factor has a value range of [value range missing]. ; This represents the engineering design margin, with a range of values. ; Step 403, determine the feasibility of storage capacity: ； in, This represents the current used capacity of the onboard solid-state storage. This represents the total capacity of the onboard solid-state storage. This represents the safety threshold coefficient for on-board solid-state storage. ; Step 404, determine the feasibility of the data transmission window: ； in, This represents the number of files to be downloaded from the onboard solid-state storage. The first in on-board solid-state memory The amount of data to be transferred for the file to be downloaded. The equivalent data transmission bandwidth of the data transmission window. For the number of data transmission windows, For the first The number of windows is transmitted for the duration of the window; ； The first in on-board solid-state memory The required on-board solid-state storage capacity for each file to be downloaded This is the protocol overhead coefficient. This is the coding efficiency coefficient; ； in, This is the frame header overhead, including frame count, virtual channel ID, and data length, used for data frame identification and management, with a value ranging from 3% to 5%. Synchronization overhead, including synchronization words used for data alignment, ranges from 1% to 2%; The overhead for verification includes cyclic redundancy check (CRC) to detect data errors during transmission, with a value ranging from 2% to 4%. This is the encryption overhead, including the encryption algorithm header information, to ensure the security of data transmission; its value ranges from 0.5% to 1%. Coding efficiency coefficient Values , , , ; Step 405: If both storage capacity feasibility and data transmission window feasibility are met, the mission is approved and corresponding storage space is reserved for the mission in the on-board solid-state storage. Otherwise, adjust the task parameters: For optical imaging: adjustments can be made by reducing the number of spectral channels of the optical payload, increasing the ground sampling distance along the track, increasing the ground sampling distance in the vertical track direction, and increasing the optical data compression ratio; For SAR imaging: Adjustments can be made by adopting one or more of the following methods: simplifying the polarization mode and increasing the SAR data compression ratio; If, after adjusting the task parameters once, both storage capacity feasibility and data transmission window feasibility cannot be simultaneously satisfied, then the task will be rejected.

7. The method for predicting and evaluating low-Earth orbit satellite data volume based on an imaging model according to claim 6, characterized in that, The specific method for step 5 is as follows: Step 501: After the satellite imaging mission is executed and the data is downloaded, the system records the actual amount of data. By calculating the required physical storage capacity of the on-board solid-state memory. relative error : ； Step 502, calculate the model correction factor : ； in, The initial value is 1. This represents historical relative error. This is the proportionality coefficient, and its value range is... ; The integral coefficient has a range of values ​​of 1. And satisfy ; Step 503, Correction or : ； 。

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